From 5a7b6789582e9959f1105676f31ab37f877f76d7 Mon Sep 17 00:00:00 2001 From: jreps Date: Wed, 21 Aug 2024 21:49:24 -0400 Subject: [PATCH] Develop (#324) - merged cohortmethod/sccs/evidence synth into estimation module - improved characterization module - bug fixes - updated modules for latest HADES Co-authored-by: nhall6 Co-authored-by: Jamie Gilbert Co-authored-by: Frank DeFalco Co-authored-by: Nathan Hall <106178605+nhall6@users.noreply.github.com> Co-authored-by: Chris Knoll Co-authored-by: Anthony Sena --- DESCRIPTION | 11 +- NAMESPACE | 31 +- NEWS.md | 15 +- R/OhdsiShinyModules.R | 4 +- R/about-main.R | 271 +- R/characterization-aggregateFeatures.R | 870 ------ R/characterization-caseSeries.R | 743 +++++ R/characterization-cohorts.R | 1225 ++++++--- R/characterization-database.R | 320 +++ R/characterization-dechallengeRechallenge.R | 350 ++- R/characterization-incidence.R | 2416 ++++++++--------- R/characterization-main.R | 629 ++++- R/characterization-riskFactors.R | 1004 +++++++ R/characterization-timeToEvent.R | 382 +-- R/cohort-diagnostics-characterization.R | 5 +- R/cohort-diagnostics-cohort-overlap.R | 7 +- ...hort-diagnostics-compareCharacterization.R | 1 + R/cohort-diagnostics-conceptsInDataSource.R | 1 + R/cohort-diagnostics-counts.R | 2 + R/cohort-diagnostics-databaseInformation.R | 1 + R/cohort-diagnostics-definition.R | 4 +- R/cohort-diagnostics-incidenceRates.R | 1 + R/cohort-diagnostics-inclusionRules.R | 1 + R/cohort-diagnostics-indexEventBreakdown.R | 1 + R/cohort-diagnostics-main-ui.R | 2 + R/cohort-diagnostics-main.R | 209 +- R/cohort-diagnostics-orphanConcepts.R | 13 +- R/cohort-diagnostics-timeDistributions.R | 1 + R/cohort-diagnostics-visitContext.R | 1 + R/cohort-generator-main.R | 306 ++- R/cohort-method-diagnosticsSummary.R | 389 --- R/cohort-method-main.R | 293 -- R/components-data-viewer.R | 108 +- R/components-helpInfo.R | 2 +- R/components-largeTableViewer.R | 2 +- R/data-diagnostic-drill.R | 4 +- R/data-diagnostic-main.R | 6 +- R/data-diagnostic-summary.R | 4 +- R/datasources-main.R | 16 +- ...R => estimation-cohort-method-attrition.R} | 4 +- ...timation-cohort-method-covariateBalance.R} | 165 +- R/estimation-cohort-method-diagnostics.R | 264 ++ ...=> estimation-cohort-method-full-result.R} | 14 +- ...> estimation-cohort-method-kaplainMeier.R} | 4 +- R/estimation-cohort-method-plots.R | 156 ++ ...cohort-method-populationCharacteristics.R} | 11 +- ...wer.R => estimation-cohort-method-power.R} | 10 +- ...stimation-cohort-method-propensityModel.R} | 4 +- ...hort-method-propensityScoreDistribution.R} | 4 +- ...y.R => estimation-cohort-method-results.R} | 260 +- ...stimation-cohort-method-systematicError.R} | 4 +- R/estimation-main.R | 414 +++ R/estimation-sccs-diagnostics.R | 261 ++ R/estimation-sccs-plots.R | 163 ++ ...-full.R => estimation-sccs-results-full.R} | 452 ++- ...cs-results.R => estimation-sccs-results.R} | 235 +- R/evidence-synth-cm.R | 549 ---- R/evidence-synth-main.R | 275 -- 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delete mode 100644 man/cohortMethodHelperFile.Rd delete mode 100644 man/cohortMethodResultSummaryServer.Rd delete mode 100644 man/cohortMethodResultSummaryViewer.Rd delete mode 100644 man/cohortMethodServer.Rd delete mode 100644 man/cohortMethodViewer.Rd create mode 100644 man/estimationHelperFile.Rd create mode 100644 man/estimationServer.Rd create mode 100644 man/estimationViewer.Rd delete mode 100644 man/evidenceSynthesisHelperFile.Rd delete mode 100644 man/evidenceSynthesisServer.Rd delete mode 100644 man/evidenceSynthesisViewer.Rd create mode 100644 man/getExampleConnectionDetails.Rd create mode 100644 man/homeHelperFile.Rd rename man/{sccsServer.Rd => homeServer.Rd} (51%) create mode 100644 man/homeViewer.Rd create mode 100644 man/reportHelperFile.Rd create mode 100644 man/reportServer.Rd create mode 100644 man/reportViewer.Rd delete mode 100644 man/sccsHelperFile.Rd delete mode 100644 man/sccsView.Rd create mode 100644 tests/resources/cDatabase/databaseFile.sqlite delete mode 100644 tests/resources/characterizationDatabase/databaseFile.sqlite delete mode 100644 tests/resources/cmDatabase/databaseFile.sqlite delete mode 100644 tests/resources/esDatabase/databaseFile.sqlite create mode 100644 tests/resources/estDatabase/databaseFile.sqlite delete mode 100644 tests/resources/sccsDatabase/databaseFile.sqlite delete mode 100644 tests/testthat/test-characterization-aggregate-features.R create mode 100644 tests/testthat/test-characterization-caseSeries.R create mode 100644 tests/testthat/test-characterization-database.R create mode 100644 tests/testthat/test-characterization-riskFactors.R delete mode 100644 tests/testthat/test-cohort-method-DiagnosticsSummary.R delete mode 100644 tests/testthat/test-cohort-method-main.R rename tests/testthat/{test-cohort-method-attrition.R => test-estimation-cohort-method-attrition.R} (60%) rename tests/testthat/{test-cohort-method-CovariateBalance.R => test-estimation-cohort-method-covariateBalance.R} (89%) create mode 100644 tests/testthat/test-estimation-cohort-method-diagnostics.R rename tests/testthat/{test-cohort-method-full-result.R => test-estimation-cohort-method-full-result.R} (50%) rename tests/testthat/{test-cohort-method-KaplanMeier.R => test-estimation-cohort-method-kaplanMeier.R} (79%) create mode 100644 tests/testthat/test-estimation-cohort-method-plots.R rename tests/testthat/{test-cohort-method-PopulationCharacteristics.R => test-estimation-cohort-method-populationCharacteristics.R} (82%) rename tests/testthat/{test-cohort-method-Power.R => test-estimation-cohort-method-power.R} (80%) rename tests/testthat/{test-cohort-method-propensityModel.R => test-estimation-cohort-method-propensityModel.R} (86%) rename tests/testthat/{test-cohort-method-PropensityScoreDist.R => test-estimation-cohort-method-propensityScoreDist.R} (80%) create mode 100644 tests/testthat/test-estimation-cohort-method-results.R rename tests/testthat/{test-cohort-method-systematicError.R => test-estimation-cohort-method-systematicError.R} (91%) create mode 100644 tests/testthat/test-estimation-main.R create mode 100644 tests/testthat/test-estimation-sccs-diagnostics.R create mode 100644 tests/testthat/test-estimation-sccs-plots.R create mode 100644 tests/testthat/test-estimation-sccs-results-full.R create mode 100644 tests/testthat/test-estimation-sccs-results.R delete mode 100644 tests/testthat/test-evidence-synth-cm.R delete mode 100644 tests/testthat/test-evidence-synth-main.R delete mode 100644 tests/testthat/test-evidence-synth-sccs.R create mode 100644 tests/testthat/test-home.R create mode 100644 tests/testthat/test-report-main.R delete mode 100644 tests/testthat/test-sccs-main.R delete mode 100644 tests/testthat/test-sccs-results-full.R delete mode 100644 tests/testthat/test-sccs-results.R create mode 100644 vignettes/Characterization.Rmd create mode 100644 vignettes/CohortDiagnostics.Rmd create mode 100644 vignettes/CohortMethod.Rmd create mode 100644 vignettes/Cohorts.Rmd create mode 100644 vignettes/DataSources.Rmd create mode 100644 vignettes/EvidenceSynthesis.Rmd create mode 100644 vignettes/Prediction.Rmd create mode 100644 vignettes/SelfControlledCaseSeries.Rmd diff --git a/DESCRIPTION b/DESCRIPTION index 37cb355f..16e43631 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,11 @@ Package: OhdsiShinyModules Type: Package Title: Repository of Shiny Modules for OHDSI Result Viewers -Version: 2.1.5 -Author: Jenna Reps +Version: 3.0.0 +Authors@R: c( + person("Jenna", "Reps", email = "jreps@its.jnj.com", role = c("aut", "cre")), + person("Nathan", "Hall", role = c("aut")), + person("Jamie", "Gibert", role = c("aut"))) Maintainer: Jenna Reps Description: Install this package to access useful shiny modules for building shiny apps to explore results using the OHDSI tools . License: Apache License 2.0 @@ -24,11 +27,13 @@ Imports: htmltools, lubridate, methods, + openxlsx, ParallelLogger, plotly, purrr, reactable, readr, + ReportGenerator, RJSONIO, rlang, rmarkdown, @@ -36,6 +41,7 @@ Imports: shiny, shinycssloaders, shinydashboard, + shinyglide, shinyWidgets, SqlRender, stringi, @@ -54,5 +60,6 @@ Suggests: testthat, withr Remotes: + ohdsi/ReportGenerator, ohdsi/ResultModelManager RoxygenNote: 7.3.1 diff --git a/NAMESPACE b/NAMESPACE index 7702d5cd..97074e8f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -4,18 +4,10 @@ export(LargeDataTable) export(aboutHelperFile) export(aboutServer) export(aboutViewer) -export(characterizationAggregateFeaturesServer) -export(characterizationAggregateFeaturesViewer) -export(characterizationDechallengeRechallengeServer) -export(characterizationDechallengeRechallengeViewer) export(characterizationHelperFile) export(characterizationIncidenceServer) export(characterizationIncidenceViewer) export(characterizationServer) -export(characterizationTableServer) -export(characterizationTableViewer) -export(characterizationTimeToEventServer) -export(characterizationTimeToEventViewer) export(characterizationViewer) export(cohortCountsView) export(cohortDefinitionsView) @@ -30,9 +22,6 @@ export(cohortMethodAttritionServer) export(cohortMethodAttritionViewer) export(cohortMethodCovariateBalanceServer) export(cohortMethodCovariateBalanceViewer) -export(cohortMethodDiagnosticsSummaryServer) -export(cohortMethodDiagnosticsSummaryViewer) -export(cohortMethodHelperFile) export(cohortMethodKaplanMeierServer) export(cohortMethodKaplanMeierViewer) export(cohortMethodPopulationCharacteristicsServer) @@ -43,12 +32,8 @@ export(cohortMethodPropensityModelServer) export(cohortMethodPropensityModelViewer) export(cohortMethodPropensityScoreDistServer) export(cohortMethodPropensityScoreDistViewer) -export(cohortMethodResultSummaryServer) -export(cohortMethodResultSummaryViewer) -export(cohortMethodServer) export(cohortMethodSystematicErrorServer) export(cohortMethodSystematicErrorViewer) -export(cohortMethodViewer) export(cohortOverlapView) export(compareCohortCharacterizationView) export(conceptsInDataSourceView) @@ -65,11 +50,15 @@ export(databaseInformationView) export(datasourcesHelperFile) export(datasourcesServer) export(datasourcesViewer) -export(evidenceSynthesisHelperFile) -export(evidenceSynthesisServer) -export(evidenceSynthesisViewer) +export(estimationHelperFile) +export(estimationServer) +export(estimationViewer) export(getEnabledCdReports) +export(getExampleConnectionDetails) export(getLogoImage) +export(homeHelperFile) +export(homeServer) +export(homeViewer) export(incidenceRatesView) export(inclusionRulesView) export(indexEventBreakdownView) @@ -103,9 +92,9 @@ export(patientLevelPredictionViewer) export(phevaluatorHelperFile) export(phevaluatorServer) export(phevaluatorViewer) -export(sccsHelperFile) -export(sccsServer) -export(sccsView) +export(reportHelperFile) +export(reportServer) +export(reportViewer) export(timeDistributionsView) export(visitContextView) importFrom(dplyr,"%>%") diff --git a/NEWS.md b/NEWS.md index a0b6fc9a..f880d780 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,19 @@ +OhdsiShinyModules v2.2.1 +======================== +- Fixed issue in DatabaseConnector check for pooled connections of sqlite databases on cohort diagnotiscs load (from main hotfix) + +OhdsiShinyModules v2.2.0 +======================== +- Combined cohort method, sccs and evidence synthesis into one estimation module with shared target and outcome ids +- Characterizations now share the target id +- Updated tests to get them all working +- Cleaned R check (but cohort incidence still has many notes) + + OhdsiShinyModules v2.1.5 ======================== -Fixed issue in DatabaseConnector check for pooled connections of sqlite databases on cohort diagnotiscs load +Fixed bug of orphan concepts report not displaying +Fixed bug in orphan concepts where negative database ids were causing dynamic sql queries to crash OhdsiShinyModules v2.1.4 ======================== diff --git a/R/OhdsiShinyModules.R b/R/OhdsiShinyModules.R index 77722f5a..4aaeef44 100644 --- a/R/OhdsiShinyModules.R +++ b/R/OhdsiShinyModules.R @@ -19,7 +19,9 @@ #' OhdsiShinyModules #' #' @description A selection of shiny modules for exploring standardized OHDSI results +#' #' @name OhdsiShinyModules +#' @keywords internal #' @importFrom dplyr %>% #' @importFrom rlang .data -"_PACKAGE" +"_PACKAGE" \ No newline at end of file diff --git a/R/about-main.R b/R/about-main.R index d3019a49..f5225ad7 100644 --- a/R/about-main.R +++ b/R/about-main.R @@ -21,13 +21,14 @@ #' #' @details #' Returns the location of the about helper file -#' +#' @family {About} #' @return #' string location of the about helper file #' #' @export -aboutHelperFile <- function(){ - fileLoc <- system.file('about-www', "about.html", package = "OhdsiShinyModules") +aboutHelperFile <- function() { + fileLoc <- + system.file('about-www', "about.html", package = "OhdsiShinyModules") return(fileLoc) } @@ -37,36 +38,59 @@ aboutHelperFile <- function(){ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {About} #' @return #' The user interface to the home page module #' #' @export -aboutViewer <- function( - id = 'homepage' - ) { +aboutViewer <- function(id = 'homepage') { ns <- shiny::NS(id) - shinydashboard::box( - status = 'info', width = 12, - title = shiny::span( shiny::icon("info"), "About OHDSI Viewer"), - solidHeader = TRUE, - - shiny::fluidPage( - shiny::fluidRow( - shiny::includeMarkdown( - path = system.file( - 'about-document', - "introduction.md", - package = "OhdsiShinyModules" - ) - ) - ) + #shinydashboard::dashboardBody( + shiny::div( + shiny::fluidRow( + shiny::tags$head(shiny::tags$style( + shiny::HTML(".small-box {height: 200px; width: 100%;}") + )), + shinydashboard::box(width = "100%", + shiny::htmlTemplate( + system.file("about-www", "about.html", package = utils::packageName()) + )) + # ) + ), + shiny::fluidRow( + shinydashboard::valueBoxOutput(ns("datasourcesBox"), width = 3), + shinydashboard::valueBoxOutput(ns("cohortsBox"), width = 3), + shinydashboard::valueBoxOutput(ns("characterizationBox"), width = 3), + shinydashboard::valueBoxOutput(ns("cohortDiagnosticsBox"), width = 3) + ), + shiny::fluidRow( + shinydashboard::valueBoxOutput(ns("cohortMethodBox"), width = 3), + shinydashboard::valueBoxOutput(ns("predictionBox"), width = 3), + shinydashboard::valueBoxOutput(ns("sccsBox"), width = 3), + shinydashboard::valueBoxOutput(ns("evidenceSynthesisBox"), width = 3) ) - ) } +targetedValueBox <- function( + value, + subtitle, + icon, + color, + href, + target = "_new" + ) { + valueBox <- shinydashboard::valueBox( + value = value, + subtitle = subtitle, + icon = icon, + color = color, + href = href + ) + shiny::tagAppendAttributes(valueBox,.cssSelector="a", target=target) +} + #' The module server for the shiny app home #' #' @details @@ -75,21 +99,196 @@ aboutViewer <- function( #' @param id the unique reference id for the module #' @param connectionHandler a connection to the database with the results #' @param resultDatabaseSettings a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix -#' +#' @param config the config from the app.R file that contains a list of which modules to include +#' @family {About} #' @return #' The server for the shiny app home #' #' @export -aboutServer <- function( - id = 'homepage', - connectionHandler = NULL, - resultDatabaseSettings = NULL - ) { - shiny::moduleServer( - id, - function(input, output, session) { - - - } - ) +aboutServer <- function(id = 'homepage', + connectionHandler = NULL, + resultDatabaseSettings = NULL, + config) { + shiny::moduleServer(id, + function(input, output, session) { + tab_names <- character() + # Loop through shinyModules and extract tabName values + for (i in seq_along(config[["shinyModules"]])) { + tab_name <- config[["shinyModules"]][[i]][["tabName"]] + tab_names <- c(tab_names, tab_name) + } + # View the extracted tabName values + # print(tab_names) + + output$datasourcesBox <- + shinydashboard::renderValueBox({ + if ("DataSources" %in% tab_names) { + targetedValueBox( + value = "Data Sources", + subtitle = "Data sources used in this analysis", + icon = shiny::icon("database"), + color = "aqua", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/DataSources.html" + ) + } else { + targetedValueBox( + value = "Data Sources", + subtitle = "This module was not included in this analysis", + icon = shiny::icon("database"), + color = "black", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/DataSources.html" + ) + } + }) + + output$cohortsBox <- + shinydashboard::renderValueBox({ + if ("Cohorts" %in% tab_names) { + targetedValueBox( + value = "Cohorts", + subtitle = "Cohorts included in this analysis", + icon = shiny::icon("user-gear"), + color = "purple", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/Cohorts.html" + ) + } else { + targetedValueBox( + value = "Cohorts", + subtitle = "This module was not included in this analysis", + icon = shiny::icon("user-gear"), + color = "black", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/Cohorts.html" + ) + } + }) + + output$characterizationBox <- + shinydashboard::renderValueBox({ + if ("Characterization" %in% tab_names) { + targetedValueBox( + value = "Characterization", + subtitle = "Characterization results for this analysis", + icon = shiny::icon("table"), + color = "teal", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/Characterization.html" + ) + } else { + targetedValueBox( + value = "Characterization", + subtitle = "This module was not included in this analysis", + icon = shiny::icon("table"), + color = "black", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/Characterization.html" + ) + } + }) + + output$cohortDiagnosticsBox <- + shinydashboard::renderValueBox({ + if ("CohortDiagnostics" %in% tab_names) { + targetedValueBox( + value = "Cohort Diagnostics", + subtitle = "Cohort Diagnostics results for the cohorts included in this analysis", + icon = shiny::icon("users"), + color = "yellow", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/CohortDiagnostics.html" + ) + } else { + targetedValueBox( + value = "Cohort Diagnostics", + subtitle = "This module was not included in this analysis", + icon = shiny::icon("users"), + color = "black", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/CohortDiagnostics.html" + ) + } + }) + + + output$cohortMethodBox <- + shinydashboard::renderValueBox({ + if ("CohortMethod" %in% tab_names) { + targetedValueBox( + value = "Cohort Method", + subtitle = "Cohort Method results for this analysis", + icon = shiny::icon("chart-column"), + color = "maroon", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/CohortMethod.html" + ) + } else { + targetedValueBox( + value = "Cohort Method", + subtitle = "This module was not included in this analysis", + icon = shiny::icon("chart-column"), + color = "black", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/CohortMethod.html" + ) + } + }) + + output$predictionBox <- + shinydashboard::renderValueBox({ + if ("Prediction" %in% tab_names) { + targetedValueBox( + value = "Prediction", + subtitle = "Patient-level Prediction results for this analysis", + icon = shiny::icon("chart-line"), + color = "blue", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/Prediction.html" + ) + } else { + targetedValueBox( + value = "Prediction", + subtitle = "This module was not included in this analysis", + icon = shiny::icon("chart-line"), + color = "black", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/Prediction.html" + ) + } + }) + + output$sccsBox <- + shinydashboard::renderValueBox({ + if ("SCCS" %in% tab_names) { + targetedValueBox( + value = "SCCS", + subtitle = "Self-Controlled Case Series results for this analysis", + icon = shiny::icon("people-arrows"), + color = "red", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/SelfControlledCaseSeries.html" + ) + } else { + targetedValueBox( + value = "SCCS", + subtitle = "This module was not included in this analysis", + icon = shiny::icon("people-arrows"), + color = "black", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/SelfControlledCaseSeries.html" + ) + } + }) + + output$evidenceSynthesisBox <- + shinydashboard::renderValueBox({ + if ("Meta" %in% tab_names) { + targetedValueBox( + value = "Meta", + subtitle = "Meta Analysis results for this analysis", + icon = shiny::icon("sliders"), + color = "olive", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/EvidenceSynthesis.html" + ) + } else { + targetedValueBox( + value = "Meta", + subtitle = + "This module was not included in this analysis", + icon = shiny::icon("sliders"), + color = "black", + href = "https://ohdsi.github.io/OhdsiShinyModules/articles/EvidenceSynthesis.html" + ) + } + }) + + }) } diff --git a/R/characterization-aggregateFeatures.R b/R/characterization-aggregateFeatures.R deleted file mode 100644 index 0f64b826..00000000 --- a/R/characterization-aggregateFeatures.R +++ /dev/null @@ -1,870 +0,0 @@ -# @file characterization-aggregateFeatures.R -# -# Copyright 2024 Observational Health Data Sciences and Informatics -# -# This file is part of OhdsiShinyModules -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -#' The module viewer for exploring aggregate feature results -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' -#' @return -#' The user interface to the description aggregate feature module -#' -#' @export -characterizationAggregateFeaturesViewer <- function(id) { - ns <- shiny::NS(id) - - shiny::div( - - infoHelperViewer( - id = "helper", - helpLocation= system.file("characterization-www", "help-OutcomeStratified.html", package = utils::packageName()) - ), - - - # module that does input selection for a single row DF - inputSelectionViewer( - id = ns("input-selection") - ), - - # COV: RUN_ID DATABASE_ID COHORT_DEFINITION_ID COVARIATE_ID SUM_VALUE AVERAGE_VALUE - # COV REF: RUN_ID DATABASE_ID COVARIATE_ID COVARIATE_NAME ANALYSIS_ID CONCEPT_ID - # settings: RUN_ID DATABASE_ID COVARIATE_SETTING_JSON RISK_WINDOW_START START_ANCHOR RISK_WINDOW_END END_ANCHOR - # cohort_details: RUN_ID DATABASE_ID COHORT_DEFINITION_ID TARGET_COHORT_ID OUTCOME_COHORT_ID COHORT_TYPE - # analysis_ref: RUN_ID DATABASE_ID ANALYSIS_ID ANALYSIS_NAME DOMAIN_ID START_DAY END_DAY IS_BINARY MISSING_MEANS_ZERO - # cov cont: RUN_ID DATABASE_ID COHORT_DEFINITION_ID COVARIATE_ID COUNT_VALUE MIN_VALUE MAX_VALUE AVERAGE_VALUE STANDARD_DEVIATION MEDIAN_VALUE P_10_VALUE P_25_VALUE P_75_VALUE P_90_VALUE - # add table with options to select T, O and TAR - - # add UI to pick database/type 1 and database/type 2 - - shiny::conditionalPanel( - condition = 'input.generate != 0', - ns = shiny::NS(ns("input-selection")), - - shinydashboard::tabBox( - width = "100%", - # Title can include an icon - title = shiny::tagList(shiny::icon("gear"), "Table and Plots"), - shiny::tabPanel("Binary Feature Table", - resultTableViewer(ns('binaryTable')) - ), - shiny::tabPanel("Continuous Feature Table", - resultTableViewer(ns('continuousTable')) - ), - shiny::tabPanel("Binary Feature Plot", - shinycssloaders::withSpinner( - plotly::plotlyOutput(ns("binaryPlot")) - ) - ), - shiny::tabPanel("Continuous Feature Plot", - shinycssloaders::withSpinner( - plotly::plotlyOutput(ns("continuousPlot")) - ) - ) - ) - ) - ) -} - - -#' The module server for exploring aggregate features results -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' @param connectionHandler the connection to the prediction result database -#' @param resultDatabaseSettings a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix -#' -#' @return -#' The server to the description aggregate features module -#' -#' @export -characterizationAggregateFeaturesServer <- function( - id, - connectionHandler, - resultDatabaseSettings -) { - shiny::moduleServer( - id, - function(input, output, session) { - - # get the possible options - options <- getAggregateFeatureOptions( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - # get databases - databases <- getAggregateFeatureDatabases( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - # input selection component - inputSelected <- inputSelectionServer( - id = "input-selection", - inputSettingList = list( - createInputSetting( - rowNumber = 1, - columnWidth = 4, - varName = 'targetIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Target: ', - choices = options$targets, - selected = options$targets[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - createInputSetting( - rowNumber = 1, - columnWidth = 4, - varName = 'outcomeIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Outcome: ', - choices = options$outcomes, - selected = options$outcomes[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ) - , - createInputSetting( - rowNumber = 1, - columnWidth = 4, - varName = 'tarIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Time at risk: ', - choices = options$tars, - selected = options$tars[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ) - , - createInputSetting( - rowNumber = 2, - columnWidth = 6, - varName = 'database', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Database: ', - choices = databases, - selected = databases[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ) - , - createInputSetting( - rowNumber = 2, - columnWidth = 3, - varName = 'firstO', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Restrict to first O: ', - choices = c(T,F), - selected = T, - multiple = F - ) - ) - , - createInputSetting( - rowNumber = 2, - columnWidth = 3, - varName = 'index', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Index: ', - choices = c('T', 'O'), - selected = 'T', - multiple = F - ) - ) - ) - ) - - allData <- shiny::reactive({ - characterizationGetAggregateData( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - targetId = inputSelected()$targetIds, - outcomeId = inputSelected()$outcomeIds, - riskWindowStart = options$tarList[[which(options$tars == ifelse(is.null(inputSelected()$tarIds),options$tars[1],inputSelected()$tarIds))]]$riskWindowStart, - riskWindowEnd = options$tarList[[which(options$tars == ifelse(is.null(inputSelected()$tarIds),options$tars[1],inputSelected()$tarIds))]]$riskWindowEnd, - startAnchor = options$tarList[[which(options$tars == ifelse(is.null(inputSelected()$tarIds),options$tars[1],inputSelected()$tarIds))]]$startAnchor, - endAnchor = options$tarList[[which(options$tars == ifelse(is.null(inputSelected()$tarIds),options$tars[1],inputSelected()$tarIds))]]$endAnchor, - database = inputSelected()$database, - firstO = inputSelected()$firstO, - index = inputSelected()$index - ) - }) - - output$binaryPlot <- plotly::renderPlotly( - characterizationFeaturePlot( - data = allData()$binary, - valueColumn = 'averageValue' - ) - ) - output$continuousPlot <- plotly::renderPlotly( - characterizationFeaturePlot( - data = allData()$continuous, - valueColumn = 'averageValue' - ) - ) - - binaryData <- shiny::reactive({ - characterizationFeatureTable( - data = allData()$binary - ) - }) - - continuousData <- shiny::reactive({ - characterizationFeatureTable( - data = allData()$continuous - ) - }) - - binTableOutputs <- resultTableServer( - id = "binaryTable", - df = binaryData, - colDefsInput = list( - covariateName = reactable::colDef( - name = "Covariate Name", - filterable = T - ), - comp1T = reactable::colDef( - name = "T without O mean", - format = reactable::colFormat(digits = 2, percent = T) - ), - comp1sdT = reactable::colDef( - name = "T without O stdev", - format = reactable::colFormat(digits = 2) - ), - comp2T = reactable::colDef( - name = "T with O mean", - format = reactable::colFormat(digits = 2, percent = T) - ), - comp2sdT = reactable::colDef( - name = "T with O stdev", - format = reactable::colFormat(digits = 2) - ), - comp1O = reactable::colDef( - name = "O without T mean", - format = reactable::colFormat(digits = 2, percent = T) - ), - comp1sdO = reactable::colDef( - name = "O without T stdev", - format = reactable::colFormat(digits = 2) - ), - comp2O = reactable::colDef( - name = "O with T mean", - format = reactable::colFormat(digits = 2, percent = T) - ), - comp2sdO = reactable::colDef( - name = "O with T stdev", - format = reactable::colFormat(digits = 2) - ), - analysisName = reactable::colDef( # not sure this will work now - filterInput = function(values, name) { - shiny::tags$select( - # Set to undefined to clear the filter - onchange = sprintf("Reactable.setFilter('desc-bin-select', '%s', event.target.value || undefined)", name), - # "All" has an empty value to clear the filter, and is the default option - shiny::tags$option(value = "", "All"), - lapply(unique(values), shiny::tags$option), - "aria-label" = sprintf("Filter %s", name), - style = "width: 100%; height: 28px;" - ) - } - ), - standardizedMeanDiff = reactable::colDef( - format = reactable::colFormat(digits = 2) - ) - ), - addActions = NULL - ) - - conTableOutputs <- resultTableServer( - id = "continuousTable", - df = continuousData, - colDefsInput = list( - covariateName = reactable::colDef( - name = "Covariate Name", - filterable = T - ), - comp1T = reactable::colDef( - name = "T without O mean", - format = reactable::colFormat(digits = 2) - ), - comp1sdT = reactable::colDef( - name = "T without O stdev", - format = reactable::colFormat(digits = 2) - ), - comp2T = reactable::colDef( - name = "T with O mean", - format = reactable::colFormat(digits = 2) - ), - comp2sdT = reactable::colDef( - name = "T with O stdev", - format = reactable::colFormat(digits = 2) - ), - comp1O = reactable::colDef( - name = "O without T mean", - format = reactable::colFormat(digits = 2) - ), - comp1sdO = reactable::colDef( - name = "O without T stdev", - format = reactable::colFormat(digits = 2) - ), - comp2O = reactable::colDef( - name = "O with T mean", - format = reactable::colFormat(digits = 2) - ), - comp2sdO = reactable::colDef( - name = "O with T stdev", - format = reactable::colFormat(digits = 2) - ), - analysisName = reactable::colDef( - filterInput = function(values, name) { - shiny::tags$select( - # Set to undefined to clear the filter - onchange = sprintf("Reactable.setFilter('desc-cont-select', '%s', event.target.value || undefined)", name), - # "All" has an empty value to clear the filter, and is the default option - shiny::tags$option(value = "", "All"), - lapply(unique(values), shiny::tags$option), - "aria-label" = sprintf("Filter %s", name), - style = "width: 100%; height: 28px;" - ) - } - ), - standardizedMeanDiff = reactable::colDef( - format = reactable::colFormat(digits = 2) - ) - ), - addActions = NULL - ) - - #elementId = "desc-cont-select" - - - - return(invisible(NULL)) - } - ) -} - - -getAggregateFeatureOptions <- function( - connectionHandler, - resultDatabaseSettings -){ - - - shiny::withProgress(message = 'Getting feature comparison options', value = 0, { - - sql <- "SELECT DISTINCT t.COHORT_NAME as TARGET, cd.TARGET_COHORT_ID, - o.COHORT_NAME as outcome, cd.OUTCOME_COHORT_ID, - s.RISK_WINDOW_START, s.START_ANCHOR, s.RISK_WINDOW_END, s.END_ANCHOR - FROM @schema.@c_table_prefixCOHORT_DETAILS cd - inner join @schema.@c_table_prefixSETTINGS s - on cd.run_id = s.run_id and cd.database_id = s.database_id - inner join @schema.@cg_table_prefixCOHORT_DEFINITION t - on cd.TARGET_COHORT_ID = t.COHORT_DEFINITION_ID - inner join @schema.@cg_table_prefixCOHORT_DEFINITION o - on cd.OUTCOME_COHORT_ID = o.COHORT_DEFINITION_ID - WHERE cd.TARGET_COHORT_ID != 0 AND cd.OUTCOME_COHORT_ID != 0;" - - shiny::incProgress(1/2, detail = paste("Extracting options")) - - options <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - c_table_prefix = resultDatabaseSettings$cTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix - ) - - shiny::incProgress(2/2, detail = paste("Finished")) - - }) - - targets <- unique(options$targetCohortId) - names(targets) <- unique(options$target) - - outcomes <- unique(options$outcomeCohortId) - names(outcomes) <- unique(options$outcome) - - options <- unique( - options %>% - dplyr::select( - "riskWindowStart", - "riskWindowEnd", - "startAnchor", - "endAnchor" - ) - ) - - tarList <- lapply( - 1:nrow(options), - function(i){ - list( - riskWindowStart = options$riskWindowStart[i], - riskWindowEnd = options$riskWindowEnd[i], - startAnchor = options$startAnchor[i], - endAnchor = options$endAnchor[i] - ) - }) - - tars <- unlist( - lapply( - 1:nrow(options), - function(i){ - paste0( - '(',options$startAnchor[i],' + ', options$riskWindowStart[i], - ') - (', options$endAnchor[i],' + ', options$riskWindowEnd[i], - ')' - ) - }) - ) - - return( - list( - targets = targets, - outcomes = outcomes, - tars = tars, - tarList = tarList - ) - ) -} - -getAggregateFeatureDatabases <- function( - connectionHandler, - resultDatabaseSettings -){ - - shiny::withProgress(message = 'Finding databases', value = 0, { - sql <- "SELECT DISTINCT s.DATABASE_ID, d.CDM_SOURCE_ABBREVIATION as database_name - FROM @schema.@c_table_prefixCOHORT_DETAILS cd - inner join @schema.@database_table d - on cd.database_id = d.database_id - inner join @schema.@c_table_prefixSETTINGS s - on s.database_id = d.database_id - and s.run_id = cd.run_id;" - - shiny::incProgress(1/2, detail = paste("Extracting databases")) - - - databases <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - c_table_prefix = resultDatabaseSettings$cTablePrefix, - database_table = resultDatabaseSettings$databaseTable - ) - - shiny::incProgress(2/2, detail = paste("Finished")) - - } - ) - - dbs <- databases$databaseId - names(dbs) <- databases$databaseName - - return(dbs) -} - -# pulls all data for a target and outcome -# edited to only use Ts and TnOs -characterizationGetAggregateData <- function( - connectionHandler, - resultDatabaseSettings, - targetId, - outcomeId, - riskWindowStart, - riskWindowEnd, - startAnchor, - endAnchor, - database, - firstO, - index -){ - - if(is.null(targetId)){ - return(NULL) - } - - #get types based on index and first - outcomeType <- ifelse(firstO, 'firstO', 'O') - firstPart <- ifelse(index == 'T', 'T', outcomeType) - secondPart <- ifelse(index == 'T',outcomeType, 'T') - - type1 <- firstPart - type2 <- paste0(firstPart, 'n', secondPart) - - # if type is TnOc TnfirstOc the extract T minus TnO / TnOfirst - - shiny::withProgress(message = 'Getting Feature Comparison Data', value = 0, { - sql <- "SELECT s.RUN_ID, cd.COHORT_DEFINITION_ID - FROM @schema.@c_table_prefixSETTINGS s - inner join - @schema.@c_table_prefixCOHORT_DETAILS cd - on cd.database_id = s.database_id and - cd.run_id = s.run_id - WHERE cd.TARGET_COHORT_ID = @target_id and cd.OUTCOME_COHORT_ID = @outcome_id - and s.RISK_WINDOW_START = @risk_window_start and s.START_ANCHOR = '@start_anchor' - and s.RISK_WINDOW_END = @risk_window_end and s.END_ANCHOR = '@end_anchor' - and s.DATABASE_ID = '@database_id' and cd.COHORT_TYPE = '@type';" - - settingsFirst <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - c_table_prefix = resultDatabaseSettings$cTablePrefix, - target_id = ifelse(type1 %in% c('firstO','O'), 0, targetId), - outcome_id = ifelse(type1 %in% c('T', 'allT'), 0, outcomeId), - risk_window_start = riskWindowStart, - start_anchor = startAnchor, - risk_window_end = riskWindowEnd, - end_anchor = endAnchor, - database_id = database, - type = type1 - ) - - shiny::incProgress(1/5, detail = paste("Got first runId and cohortId")) - - - sql <- "SELECT s.RUN_ID, cd.COHORT_DEFINITION_ID - FROM @schema.@c_table_prefixSETTINGS s - inner join - @schema.@c_table_prefixCOHORT_DETAILS cd - on cd.database_id = s.database_id and - cd.run_id = s.run_id - WHERE cd.TARGET_COHORT_ID = @target_id and cd.OUTCOME_COHORT_ID = @outcome_id - and s.RISK_WINDOW_START = @risk_window_start and s.START_ANCHOR = '@start_anchor' - and s.RISK_WINDOW_END = @risk_window_end and s.END_ANCHOR = '@end_anchor' - and s.DATABASE_ID = '@database_id' and cd.COHORT_TYPE = '@type';" - - settingsSecond <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - c_table_prefix = resultDatabaseSettings$cTablePrefix, - target_id = ifelse(type2 %in% c('firstO','O'), 0, targetId), - outcome_id = ifelse(type2 %in% c('T', 'allT'), 0, outcomeId), - risk_window_start = riskWindowStart, - start_anchor = startAnchor, - risk_window_end = riskWindowEnd, - end_anchor = endAnchor, - database_id = database, - type = type2 - ) - - if(nrow(settingsSecond) == 0){ - print('no second setting') - settingsSecond <- settingsFirst - } - - shiny::incProgress(2/5, detail = paste("Got second runId and CohortId")) - - sql <- "SELECT - case when t.covariate_id is NULL then tno.covariate_id else t.covariate_id end covariate_id, - t.sum_value - tno.sum_value as comp1_count, - tno.sum_value as comp2_count, - case when (t.sum_value - tno.sum_value)*1.0/(cc.row_count - cctno.row_count) is NULL then 0 else (t.sum_value - tno.sum_value)*1.0/(cc.row_count - cctno.row_count) end as comp1_@index, - case when tno.average_value is NULL then 0 else tno.average_value end as comp2_@index, - sqrt( (t.sum_value - tno.sum_value)*1.0/(cc.row_count - cctno.row_count) * (1-( (t.sum_value - tno.sum_value)*1.0/(cc.row_count - cctno.row_count) )) ) as comp1sd_@index, - sqrt( (tno.average_value)*(1-(tno.average_value))) as comp2sd_@index, - cov_ref.COVARIATE_NAME, - an_ref.ANALYSIS_NAME - - FROM - - (select * FROM @schema.@c_table_prefixCOVARIATES - where - DATABASE_ID = '@database_id' and - COHORT_DEFINITION_ID = @cohort_def_1 and - RUN_ID in (@run_id_1) - ) t - full join - (select * FROM @schema.@c_table_prefixCOVARIATES - where - DATABASE_ID = '@database_id' and - COHORT_DEFINITION_ID = @cohort_def_2 and - RUN_ID in (@run_id_2) - ) tno - - on - t.covariate_id = tno.covariate_id - and t.run_id = tno.run_id - - INNER JOIN - @schema.@c_table_prefixCOHORT_COUNTS cc - on cc.cohort_definition_id = t.cohort_definition_id - and cc.run_id = t.run_id - and cc.database_id = t.database_id - - INNER JOIN - @schema.@c_table_prefixCOHORT_COUNTS cctno - on cctno.cohort_definition_id = tno.cohort_definition_id - and cctno.run_id = tno.run_id - and cctno.database_id = tno.database_id - - INNER JOIN - @schema.@c_table_prefixCOVARIATE_REF cov_ref - ON cov_ref.covariate_id = t.covariate_id - and cov_ref.run_id = case when t.run_id is NULL then tno.run_id else t.run_id end - and cov_ref.database_id = t.database_id - - INNER JOIN - @schema.@c_table_prefixANALYSIS_REF an_ref - ON an_ref.analysis_id = cov_ref.analysis_id - and an_ref.run_id = cov_ref.run_id - and an_ref.database_id = cov_ref.database_id - - ;" - - shiny::incProgress(3/5, detail = paste("Getting binary data")) - - binary <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - c_table_prefix = resultDatabaseSettings$cTablePrefix, - cohort_def_1 = settingsFirst$cohortDefinitionId[1], - cohort_def_2 = settingsSecond$cohortDefinitionId[1], - database_id = database, - run_id_1 = paste(settingsFirst$runId, collapse = ','), - run_id_2 = paste(settingsSecond$runId, collapse = ','), - index = index - ) - - shiny::incProgress(4/5, detail = paste("Getting continuous data")) - - sql <- "SELECT - case when t.covariate_id is NULL then tno.covariate_id else t.covariate_id end covariate_id, - t.count_value - tno.count_value as comp1_count, - tno.count_value as comp2_count, - case when (t.count_value*t.average_value - tno.count_value*tno.average_value)*1.0/(cc.row_count-tnocc.row_count) is NULL then 0 else (t.count_value*t.average_value - tno.count_value*tno.average_value)*1.0/(cc.row_count-tnocc.row_count) end as comp1_@index, - case when tno.average_value is NULL then 0 else tno.average_value end as comp2_@index, - sqrt( (square(t.standard_deviation)*cc.row_count - square(tno.standard_deviation)*tnocc.row_count)/ (cc.row_count - tnocc.row_count)) as comp1sd_@index, - tno.standard_deviation as comp2sd_@index, - cov_ref.COVARIATE_NAME, - an_ref.ANALYSIS_NAME - - FROM - - (select * FROM @schema.@c_table_prefixCOVARIATES_continuous - where - DATABASE_ID = '@database_id' and - COHORT_DEFINITION_ID = @cohort_def_1 and - RUN_ID in (@run_id_1) - ) t - full join - (select * FROM @schema.@c_table_prefixCOVARIATES_continuous - where - DATABASE_ID = '@database_id' and - COHORT_DEFINITION_ID = @cohort_def_2 and - RUN_ID in (@run_id_2) - ) tno - - on - t.covariate_id = tno.covariate_id - and t.run_id = tno.run_id - - INNER JOIN - @schema.@c_table_prefixCOHORT_COUNTS cc - on cc.cohort_definition_id = t.cohort_definition_id - and cc.run_id = t.run_id - and cc.database_id = t.database_id - - INNER JOIN - @schema.@c_table_prefixCOHORT_COUNTS tnocc - on tnocc.cohort_definition_id = tno.cohort_definition_id - and tnocc.run_id = tno.run_id - and tnocc.database_id = tno.database_id - - INNER JOIN - @schema.@c_table_prefixCOVARIATE_REF cov_ref - ON cov_ref.covariate_id = t.covariate_id - and cov_ref.run_id = case when t.run_id is NULL then tno.run_id else t.run_id end - and cov_ref.database_id = t.database_id - - INNER JOIN - @schema.@c_table_prefixANALYSIS_REF an_ref - ON an_ref.analysis_id = cov_ref.analysis_id - and an_ref.run_id = cov_ref.run_id - and an_ref.database_id = cov_ref.database_id - - ;" - - continuous <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - c_table_prefix = resultDatabaseSettings$cTablePrefix, - cohort_def_1 = settingsFirst$cohortDefinitionId[1], - cohort_def_2 = settingsSecond$cohortDefinitionId[1], - database_id = database, - run_id_1 = paste(settingsFirst$runId, collapse = ','), - run_id_2 = paste(settingsSecond$runId, collapse = ','), - index = index - ) - - shiny::incProgress(5/5, detail = paste("Finished")) - } - ) - - return(list( - binary = binary, - continuous = continuous - )) -} - -characterizationFeaturePlot <- function( - data, - valueColumn = 'averageValue' -){ - - if(is.null(data)){ - return(NULL) - } - - # selecting the column anmes that has _index appended to it - comp1Name <- paste0('comp1', c('O', 'T'))[paste0('comp1', c('O', 'T')) %in% colnames(data)] - comp2Name <- paste0('comp2', c('O', 'T'))[paste0('comp2', c('O', 'T')) %in% colnames(data)] - data$comp1 <- data[,comp1Name] - data$comp2 <- data[,comp2Name] - - maxval <- max(max(data$comp1),max(data$comp2)) - - plot <- plotly::plot_ly( - data = data, - x = ~.data$comp1, - y = ~.data$comp2, - showlegend = F - ) %>% - plotly::add_markers(color=factor(data$analysisName), - hoverinfo = 'text', - text = ~paste( - '\n',descGetType(data$covariateName), - '\n',descGetName(data$covariateName), - '\n',descGetTime(data$covariateName) - ), - showlegend = T - ) %>% - plotly::add_trace(x= c(0,maxval), y = c(0,maxval),mode = 'lines', - line = list(dash = "dash"), color = I('black'), - type='scatter', showlegend = FALSE) %>% - plotly::layout(#title = 'Prevalance of baseline predictors in persons with and without outcome', - xaxis = list(title = "Prevalance in selection 1"), - yaxis = list(title = "Prevalance in selection 2"), - #legend = l, showlegend = T, - legend = list(orientation = 'h', y = -0.3), showlegend = T) - - - return(plot) -} - -descGetType <- function(x){ - return(unlist(lapply(strsplit(x = x, split = ' during'), function(y){y[1]}))) -} - -descGetName <- function(x){ - return(unlist(lapply(strsplit(x = x, split = ': '), function(y){y[length(y)]}))) -} - -descGetTime <- function(x){ - part1 <- unlist(lapply(strsplit(x = x, split = ' during '), function(y){y[2]})) - return(unlist(lapply(strsplit(x = part1, split = ': '), function(y){y[1]}))) -} - - -characterizationFeatureTable <- function( - data -){ - - if(is.null(data)){ - return(NULL) - } - - # selecting the column that as _index appended to it - comp1Name <- paste0('comp1', c('O', 'T'))[paste0('comp1', c('O', 'T')) %in% colnames(data)] - comp2Name <- paste0('comp2', c('O', 'T'))[paste0('comp2', c('O', 'T')) %in% colnames(data)] - comp1sdName <- paste0('comp1sd', c('O', 'T'))[paste0('comp1sd', c('O', 'T')) %in% colnames(data)] - comp2sdName <- paste0('comp2sd', c('O', 'T'))[paste0('comp2sd', c('O', 'T')) %in% colnames(data)] - - if(sum(is.null(data[comp1sdName]))>0){ - data[comp1sdName][is.null(data[comp1sdName])] <- 0 - } - if(sum(is.null(data[comp2sdName]))>0){ - data[comp2sdName][is.null(data[comp2sdName])] <- 0 - } - - data <- data %>% - dplyr::mutate( - standardizedMeanDiff = (.data[[comp1Name]] - .data[[comp2Name]])/(sqrt((.data[[comp1sdName]]^2 + .data[[comp2sdName]]^2))) - ) %>% - dplyr::select( - "covariateName", - "analysisName", - comp1Name, - comp1sdName, - comp2Name, - comp2sdName, - "standardizedMeanDiff" - ) - - if(sum(is.null(data$standardizedMeanDiff))>0){ - data$standardizedMeanDiff[is.null(data$standardizedMeanDiff)] <- 0 - } - - if(sum(!is.finite(data$standardizedMeanDiff))>0){ - data$standardizedMeanDiff[!is.finite(data$standardizedMeanDiff)] <- 0 - } - - return(data) -} diff --git a/R/characterization-caseSeries.R b/R/characterization-caseSeries.R new file mode 100644 index 00000000..9a3ef0f4 --- /dev/null +++ b/R/characterization-caseSeries.R @@ -0,0 +1,743 @@ +# @file characterization-aggregateFeatures.R +# +# Copyright 2024 Observational Health Data Sciences and Informatics +# +# This file is part of OhdsiShinyModules +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + + +characterizationCaseSeriesViewer <- function(id) { + ns <- shiny::NS(id) + + shiny::div( + + # module that does input selection for a single row DF + shiny::uiOutput(ns("inputs")), + + shiny::conditionalPanel( + condition = 'input.generate != 0', + ns = ns, + + inputSelectionDfViewer(id = ns('inputSelected'), title = 'Selected'), + + shinydashboard::tabBox( + width = "100%", + # Title can include an icon + title = shiny::tagList(shiny::icon("gear"), "Case Series"), + shiny::tabPanel("Binary Feature Table", + resultTableViewer(ns('binaryTable')) + ), + shiny::tabPanel("Continuous Feature Table", + resultTableViewer(ns('continuousTable')) + ) + ) + ) + ) + +} + + + +characterizationCaseSeriesServer <- function( + id, + connectionHandler, + resultDatabaseSettings, + targetId, #reactive + outcomeId #reactive +) { + shiny::moduleServer( + id, + function(input, output, session) { + + # get databases + options <- shiny::reactive({ + characterizationGetCaseSeriesOptions( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetId = targetId(), + outcomeId = outcomeId() + ) + }) + + output$inputs <- shiny::renderUI({ # need to make reactive? + + shiny::div( + shiny::selectInput( + inputId = session$ns('databaseId'), + label = 'Database: ', + choices = options()$databaseIds, + selected = options()$databaseIds[1], + multiple = F + ), + + shiny::selectInput( + inputId = session$ns('tarInd'), + label = 'Time-at-risk: ', + choices = options()$tarInds, + selected = options()$tarInds[1], + multiple = F + ), + + shiny::actionButton( + inputId = session$ns('generate'), + label = 'Generate' + ) + ) + + }) + + # save the selections + selected <- shiny::reactiveVal(NULL) + + shiny::observeEvent(input$generate, { + + selected(data.frame( + database = names(options()$databaseIds)[which(input$databaseId == options()$databaseIds)], + time_at_risk = names(options()$tarInds)[which(input$tarInd == options()$tarInds)] + )) + + inputSelectionDfServer( + id = 'inputSelected', + dataFrameRow = selected, + ncol = 1 + ) + + allData <- characterizationGetCaseSeriesData( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetId = targetId(), + outcomeId = outcomeId(), + databaseId = input$databaseId, + tar = options()$tarList[[which(options()$tarInds == input$tarInd)]] + ) + + binTableOutputs <- resultTableServer( + id = "binaryTable", + df = allData$binary, + details = data.frame( + database = names(options()$databaseIds)[which(input$databaseId == options()$databaseIds)], + tar = names(options()$tarInds)[which(input$tarInd == options()$tarInds)], + target = options()$targetName, + outcome = options()$outcomeName, + description = "Case series binary features before target index, during exposure and after outcome index" + ), + downloadedFileName = 'case_series_binary', + colDefsInput = colDefsBinary( + elementId = session$ns('binary-table-filter') + ), # function below + addActions = NULL, + elementId = session$ns('binary-table-filter') + ) + + conTableOutputs <- resultTableServer( + id = "continuousTable", + df = allData$continuous, + details = data.frame( + database = names(options()$databaseIds)[which(input$databaseId == options()$databaseIds)], + tar = names(options()$tarInds)[which(input$tarInd == options()$tarInds)], + target = options()$targetName, + outcome = options()$outcomeName, + description = "Case series continuous features before target index, during exposure and after outcome index" + ), + downloadedFileName = 'case_series_continuous', + colDefsInput = colDefsContinuous( + elementId = session$ns('continuous-table-filter') + ), # function below + addActions = NULL, + elementId = session$ns('continuous-table-filter') + ) + + }) + + return(invisible(NULL)) + } + ) +} + + +characterizationGetCaseSeriesOptions <- function( + connectionHandler, + resultDatabaseSettings, + targetId, + outcomeId +){ + + sql <- "SELECT distinct s.database_id, d.CDM_SOURCE_ABBREVIATION as database_name, + s.setting_id, + s.RISK_WINDOW_START, s.RISK_WINDOW_END, + s.START_ANCHOR, s.END_ANCHOR, + ct1.cohort_name as target_name, + ct2.cohort_name as outcome_name + + from + @schema.@c_table_prefixsettings s + inner join @schema.@database_meta_table d + on s.database_id = d.database_id + inner join @schema.@c_table_prefixcohort_details cd + on s.setting_id = cd.setting_id + and s.database_id = cd.database_id + and cd.target_cohort_id = @target_id + and cd.outcome_cohort_id = @outcome_id + and cd.cohort_type = 'Cases' + + inner join + @schema.@cg_table_prefixcohort_definition ct1 + on + ct1.cohort_definition_id = cd.target_cohort_id + + inner join + @schema.@cg_table_prefixcohort_definition ct2 + on + ct2.cohort_definition_id = cd.outcome_cohort_id + + + ;" + + options <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + target_id = targetId, + outcome_id = outcomeId, + database_meta_table = resultDatabaseSettings$databaseTable, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix + ) + + outcomeName <- unique(options$outcomeName) + targetName <- unique(options$targetName) + + db <- unique(options$databaseId) + names(db) <- unique(options$databaseName) + + tar <- unique(options[,c('startAnchor','riskWindowStart', 'endAnchor', 'riskWindowEnd')]) + tarList <- lapply(1:nrow(tar), function(i) as.list(tar[i,])) + #tar <- unique(options$settingId) + tarInds <- 1:nrow(tar) + names(tarInds) <- unique(paste0('(', tar$startAnchor, ' + ', tar$riskWindowStart, ') - (', + tar$endAnchor, ' + ', tar$riskWindowEnd, ')' + )) + + return( + list( + databaseIds = db, + tarInds = tarInds, + tarList = tarList, + outcomeName = outcomeName, + targetName = targetName + ) + ) + +} + + +characterizationGetCaseSeriesData <- function( + connectionHandler, + resultDatabaseSettings, + targetId, + outcomeId, + databaseId, + tar +){ + + shiny::withProgress(message = 'Getting case series data', value = 0, { + shiny::incProgress(1/4, detail = paste("Extracting binary")) + + sql <- "SELECT + case + when cov.cohort_type = 'CasesBefore' then 'Before' + when cov.cohort_type = 'CasesBetween' then 'During' + when cov.cohort_type = 'CaseAfter' then 'After' + end as type, + cr.covariate_name, + s.min_prior_observation, s.outcome_washout_days, + s.case_post_outcome_duration, s.case_pre_target_duration, + cov.covariate_id, cov.sum_value, cov.average_value + from + @schema.@c_table_prefixcovariates cov + inner join @schema.@c_table_prefixcovariate_ref cr + on cov.setting_id = cr.setting_id and + cov.database_id = cr.database_id and + cov.covariate_id = cr.covariate_id + + inner join @schema.@c_table_prefixsettings s + on cov.setting_id = s.setting_id + and cov.database_id = s.database_id + + where cov.target_cohort_id = @target_id + and cov.outcome_cohort_id = @outcome_id + and cov.cohort_type in ('CasesBetween','CasesAfter','CasesBefore') + --and cov.setting_id = @setting_id + and s.risk_window_start = @risk_window_start + and s.risk_window_end = @risk_window_end + and s.start_anchor = '@start_anchor' + and s.end_anchor = '@end_anchor' + and cov.database_id = '@database_id' + and cr.analysis_id in (109, 110, 217, 218, 305, 417, 418, 505, 605, 713, 805, 926, 927) + ;" + + binary <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + target_id = targetId, + outcome_id = outcomeId, + risk_window_start = tar$riskWindowStart, + risk_window_end = tar$riskWindowEnd, + start_anchor = tar$startAnchor, + end_anchor = tar$endAnchor, + database_id = databaseId + ) + + # now process into table + binary <- caseSeriesTable( + data = binary + ) + + shiny::incProgress(3/4, detail = paste("Extracting continuous")) + + sql <- "SELECT + case + when cov.cohort_type = 'CasesBefore' then 'Before' + when cov.cohort_type = 'CasesBetween' then 'During' + when cov.cohort_type = 'CasesAfter' then 'After' + end as type, + cr.covariate_name, + s.min_prior_observation, s.outcome_washout_days, + s.case_post_outcome_duration, s.case_pre_target_duration, + cov.covariate_id, + cov.count_value, cov.min_value, cov.max_value, cov.average_value, + cov.standard_deviation, cov.median_value, cov.p_10_value, + cov.p_25_value, cov.p_75_value, cov.p_90_value + from + @schema.@c_table_prefixcovariates_continuous cov + inner join @schema.@c_table_prefixcovariate_ref cr + on cov.setting_id = cr.setting_id and + cov.database_id = cr.database_id and + cov.covariate_id = cr.covariate_id + + inner join @schema.@c_table_prefixsettings s + on cov.setting_id = s.setting_id + and cov.database_id = s.database_id + + where cov.target_cohort_id = @target_id + and cov.outcome_cohort_id = @outcome_id + and cov.cohort_type in ('CasesBetween','CasesAfter','CasesBefore') + and s.risk_window_start = @risk_window_start + and s.risk_window_end = @risk_window_end + and s.start_anchor = '@start_anchor' + and s.end_anchor = '@end_anchor' + and cov.database_id = '@database_id' + and cr.analysis_id in (109, 110, 217, 218, 305, 417, 418, 505, 605, 713, 805, 926, 927) + ;" + + # TODO - how to remove prior outcomes?? + continuous <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + target_id = targetId, + outcome_id = outcomeId, + risk_window_start = tar$riskWindowStart, + risk_window_end = tar$riskWindowEnd, + start_anchor = tar$startAnchor, + end_anchor = tar$endAnchor, + database_id = databaseId + ) + + shiny::incProgress(4/4, detail = paste("Done")) + + }) + + return( + list( + binary = binary, + continuous = continuous + ) + ) +} + + +# now process into table +caseSeriesTable <- function( + data +){ + + # Before Index Cases + beforeData <- data %>% + dplyr::filter(.data$type == 'Before') + Nbefore <- getCountFromFE( + sumValue = beforeData$sumValue, + averageValue = beforeData$averageValue + ) + + # After Index Cases + afterData <- data %>% + dplyr::filter(.data$type == 'After') + Nafter <- getCountFromFE( + sumValue = afterData$sumValue, + averageValue = afterData$averageValue + ) + + # During Index Cases + duringData <- data %>% + dplyr::filter(.data$type == 'During') + Nduring <- getCountFromFE( + sumValue = duringData$sumValue, + averageValue = duringData$averageValue + ) + + beforeData <- beforeData %>% + dplyr::mutate( + sumValueBefore = .data$sumValue, + averageValueBefore = .data$averageValue, + ) %>% + dplyr::select("covariateName", "covariateId", 'minPriorObservation', 'outcomeWashoutDays','casePostOutcomeDuration', 'casePreTargetDuration', "sumValueBefore", "averageValueBefore") + + afterData <-afterData %>% + dplyr::mutate( + sumValueAfter = .data$sumValue, + averageValueAfter = .data$averageValue, + ) %>% + dplyr::select("covariateName", "covariateId", 'minPriorObservation', 'outcomeWashoutDays','casePostOutcomeDuration', 'casePreTargetDuration', "sumValueAfter", "averageValueAfter") + + duringData <- duringData %>% + dplyr::mutate( + sumValueDuring = .data$sumValue, + averageValueDuring = .data$averageValue, + ) %>% + dplyr::select("covariateName", "covariateId", 'minPriorObservation', 'outcomeWashoutDays','casePostOutcomeDuration', 'casePreTargetDuration', "sumValueDuring", "averageValueDuring") + + + + allResults <- beforeData %>% + dplyr::full_join( + y = duringData, + by = c("covariateName", "covariateId", 'minPriorObservation', 'outcomeWashoutDays','casePostOutcomeDuration', 'casePreTargetDuration') + ) %>% + dplyr::full_join( + y = afterData, + by = c("covariateName", "covariateId", 'minPriorObservation', 'outcomeWashoutDays','casePostOutcomeDuration', 'casePreTargetDuration') + ) + + return(allResults) +} + +colDefsBinary <- function( + elementId + ){ + result <- list( + covariateName = reactable::colDef( + header = withTooltip("Covariate Name", + "Name of the covariate"), + filterable = T, + minWidth = 300 + ), + covariateId = reactable::colDef( + show = F + ), + minPriorObservation = reactable::colDef( + header = withTooltip("Min Prior Observation", + "Minimum prior observation time (days)"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + outcomeWashoutDays = reactable::colDef( + header = withTooltip("Outcome Washout Days", + "Number of days for the outcome washout"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + casePostOutcomeDuration = reactable::colDef( + header = withTooltip("Days Post-outcome Covariate Window", + "Number of days after the outcome we look for the covariate"), + filterable = T + ), + casePreTargetDuration = reactable::colDef( + header = withTooltip("Days Pre-exposure Covariate Window", + "Number of days before the exposure we look for the covariate"), + filterable = T + ), + sumValueBefore = reactable::colDef( + header = withTooltip("# Cases with Feature Pre-exposure", + "Number of cases with the covariate prior to exposure"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F), + cell = function(value) { + if(is.null(value)){return('< min threshold')} + if(is.na(value)){return('< min threshold')} + if (value >= 0) value else paste0('<', abs(value)) + } + ), + averageValueBefore = reactable::colDef( + header = withTooltip("% of Cases with Feature Pre-exposure", + "Percent of cases with the covariate prior to exposure"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = T) + ), + sumValueDuring = reactable::colDef( + header = withTooltip("# of Cases with Feature Between Exposure & Outcome", + "Number of cases with the covariate between the exposure and outcome"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F), + cell = function(value) { + if(is.null(value)){return('< min threshold')} + if(is.na(value)){return('< min threshold')} + if (value >= 0) value else paste0('<', abs(value)) + } + ), + averageValueDuring = reactable::colDef( + header = withTooltip("% of Cases with Feature Between Exposure & Outcome", + "Percent of cases with the covariate between the exposure and outcome"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = T) + ), + sumValueAfter = reactable::colDef( + header = withTooltip("# of Cases with Feature Post-outcome", + "Number of cases with the covariate after the outcome"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F), + cell = function(value) { + if(is.null(value)){return('< min threshold')} + if(is.na(value)){return('< min threshold')} + if (value >= 0) value else paste0('<', abs(value)) + } + ), + averageValueAfter = reactable::colDef( + header = withTooltip("% of Cases with Feature Post-outcome", + "Percent of cases with the covariate after the outcome"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = T) + ), + + analysisName = reactable::colDef( + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ) + ) + return(result) +} + +colDefsContinuous <- function( + elementId + ){ + result <- list( + cohortDefinitionId = reactable::colDef( + header = withTooltip("Cohort ID", + "Unique identifier of the cohort"), + filterable = T + ), + type = reactable::colDef( + header = withTooltip("Time of Cases Relative to Index", + "Time period relative to index date for cases for the covariate"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + covariateName = reactable::colDef( + header = withTooltip("Covariate Name", + "Name of the covariate"), + filterable = T, + minWidth = 300 + ), + covariateId = reactable::colDef( + show = F + ), + minPriorObservation = reactable::colDef( + header = withTooltip("Min Prior Observation", + "Minimum prior observation time (days)"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + outcomeWashoutDays = reactable::colDef( + header = withTooltip("Outcome Washout Days", + "Number of days for the outcome washout"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + casePostOutcomeDuration = reactable::colDef( + header = withTooltip("Days Post-outcome Covariate Window", + "Number of days after the outcome we look for the covariate"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + casePreTargetDuration = reactable::colDef( + header = withTooltip("Days Pre-exposure Covariate Window", + "Number of days before the exposure we look for the covariate"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + countValue = reactable::colDef( + header = withTooltip("# Cases with Feature", + "Number of cases with the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F), + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), + minValue = reactable::colDef( + header = withTooltip("Min Value", + "Minimum value of the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + maxValue = reactable::colDef( + header = withTooltip("Max Value", + "Maximum value of the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + averageValue = reactable::colDef( + header = withTooltip("Average Value", + "Average value of the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + standardDeviation = reactable::colDef( + header = withTooltip("SD", + "Standard deviation of the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + medianValue = reactable::colDef( + header = withTooltip("Median Value", + "Median value of the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + p10Value = reactable::colDef( + header = withTooltip("10th %tile", + "10th percentile value of the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + p25Value = reactable::colDef( + header = withTooltip("25th %tile", + "25th percentile value of the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + p75Value = reactable::colDef( + header = withTooltip("75th %tile", + "75th percentile value of the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + p90Value = reactable::colDef( + header = withTooltip("90th %tile", + "90th percentile value of the covariate"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ) + ) + return(result) +} + + + +getCountFromFE <- function( + sumValue, + averageValue +){ + + Ns <- sumValue/averageValue + if(sum(is.finite(Ns)) > 0 ){ + maxN <- max(Ns[is.finite(Ns)]) + } else{ + message('Issue calculating N') + maxN <- 0 + } + return(maxN) +} diff --git a/R/characterization-cohorts.R b/R/characterization-cohorts.R index ea314c98..20ab24c6 100644 --- a/R/characterization-cohorts.R +++ b/R/characterization-cohorts.R @@ -17,214 +17,273 @@ # limitations under the License. -#' The module viewer for exploring 1 or more cohorts features -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' -#' @return -#' The user interface to the description cohorts features -#' -#' @export -characterizationTableViewer <- function(id) { +# view two cohorts and compare +characterizationCohortComparisonViewer <- function(id) { ns <- shiny::NS(id) - shiny::div( - - infoHelperViewer( - id = "helper", - helpLocation= system.file("characterization-www", "help-targetViewer.html", package = utils::packageName()) - ), - - + # module that does input selection for a single row DF - inputSelectionViewer( - id = ns("input-selection") - ), - - shiny::conditionalPanel( - condition = 'input.generate != 0', - ns = shiny::NS(ns("input-selection")), + shiny::div( - # add basic table - resultTableViewer(id = ns('mainTable')) - + # UI for inputs + # summary table + shinydashboard::box( + collapsible = TRUE, + title = "Options", + width = "100%", + shiny::uiOutput(ns("inputs")) + ), + + # displayed inputs + shiny::conditionalPanel( + condition = "input.generate != 0", + ns = ns, + + inputSelectionDfViewer(id = ns('inputSelected'), title = 'Selected'), + + # add basic table + shiny::tabsetPanel( + type = 'pills', + shiny::tabPanel( + title = 'Counts', + resultTableViewer(id = ns('countTable'), boxTitle = 'Counts') + ), + shiny::tabPanel( + title = 'Binary', + resultTableViewer(id = ns('mainTable'), boxTitle = 'Binary') + ), + shiny::tabPanel( + title = 'Continuous', + resultTableViewer(id = ns('continuousTable'), boxTitle = 'Continuous') + ) + ) + + ) ) - ) } -#' The module server for exploring 1 or more cohorts features -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' @param connectionHandler the connection to the prediction result database -#' @param resultDatabaseSettings a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix -#' -#' @return -#' The server to the cohorts features server -#' -#' @export -characterizationTableServer <- function( + +characterizationCohortComparisonServer <- function( id, connectionHandler, - resultDatabaseSettings + resultDatabaseSettings, + options, + parents, + parentIndex, # reactive + subTargetId # reactive ) { shiny::moduleServer( id, function(input, output, session) { - inputVals <- getDecCohortsInputs( - connectionHandler, - resultDatabaseSettings - ) + inputVals <- shiny::reactive({characterizationGetCohortsInputs( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetId = subTargetId + )}) - # input selection component - inputSelected <- inputSelectionServer( - id = "input-selection", - inputSettingList = list( - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'targetIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Target: ', - choices = inputVals$cohortIds, - selected = inputVals$cohortIds, - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'databaseIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Database: ', - choices = inputVals$databaseIds, - selected = inputVals$databaseIds[1], - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) + + # initial comp chilren + comparatorOptions <- characterizationGetChildren(options, 1) + output$inputs <- shiny::renderUI({ + + shiny::div( + shinyWidgets::pickerInput( + inputId = session$ns('comparatorGroup'), + label = 'Comparator Group: ', + choices = parents, + selected = parents[1], + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + #virtualScroll = 50, + #container = "div.tabbable", + dropupAuto = FALSE ) ), - createInputSetting( - rowNumber = 2, - columnWidth = 6, - varName = 'analysisIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Covariate Type: ', - choices = inputVals$analysisIds, - selected = inputVals$analysisIds[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) + shiny::selectInput( + inputId = session$ns('comparatorId'), + label = 'Comparator: ', + choices = comparatorOptions, + selected = comparatorOptions[1], + multiple = F + ), + + shinyWidgets::pickerInput( + inputId = session$ns('databaseId'), + label = 'Database: ', + choices = inputVals()$databaseIds, + selected = inputVals()$databaseIds[1], + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + dropupAuto = F, + size = 10, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 500 ) - + ), + + shiny::actionButton( + inputId = session$ns('generate'), + label = 'Generate' + ) ) + + }) + + # update comparatorId + comparatorGroups <- shiny::reactiveVal() + comparatorIndex <- shiny::reactiveVal(1) + shiny::observeEvent(input$comparatorGroup,{ + comparatorIndex(which(input$comparatorGroup == parents)) + result <- characterizationGetChildren(options, comparatorIndex()) + comparatorGroups(result) + shiny::updateSelectInput( + session = session, + inputId = 'comparatorId', + label = 'Comparator: ', + choices = result, + selected = result[1] + ) + }) + + + # show selected inputs to user + inputSelectionDfServer( + id = 'inputSelected', + dataFrameRow = selected, + ncol = 1 ) - columns <- shiny::reactive({ + #get results + selected <- shiny::reactiveVal() + shiny::observeEvent(input$generate,{ - result <- list( - covariateId = reactable::colDef( - header = withTooltip("Covariate ID", - "Unique identifier of the covariate") - ), - covariateName = reactable::colDef( - header = withTooltip( - "Covariate Name", - "The name of the covariate" - ) - ), - analysisName = reactable::colDef( - header = withTooltip( - "Covariate Class", - "Class/type of the covariate" - ) + targetGroups <- characterizationGetChildren(options, parentIndex()) + + runTables <- TRUE + + if(is.null(subTargetId()) | is.null(input$comparatorId)){ + runTables <- FALSE + } + if(is.null(input$databaseId)){ + runTables <- FALSE + } + + if(subTargetId() == input$comparatorId){ + runTables <- FALSE + shiny::showNotification('Must select different cohorts') + } + + # ADDED + subTargetIds <- unlist(lapply(options[[parentIndex()]]$children, function(x){x$subsetId})) + subTargetNames <- unlist(lapply(options[[parentIndex()]]$children, function(x){x$subsetName})) + + selected( + data.frame( + Comparator = names(comparatorGroups())[which(comparatorGroups() == input$comparatorId)], + Database = names(inputVals()$databaseIds)[input$databaseId == inputVals()$databaseIds] ) ) - if(is.null(inputSelected()$targetIds) | is.null(inputSelected()$databaseIds)){ - return(result) - } else{ - temp <- expand.grid(inputSelected()$targetIds,inputSelected()$databaseIds) - temp[,2] <- as.double(as.character(temp[,2])) - - for(i in 1:nrow(temp)){ - - targetName = names(inputVals$cohortIds)[temp[i,1] == inputVals$cohortIds] - databaseName = names(inputVals$databaseIds)[temp[i,2] == inputVals$databaseIds] - - result[[length(result) + 1]] <- reactable::colDef( - header = withTooltip( - paste0("Count-", temp[i,1], '-',temp[i,2]), - paste0("The number of patients in database ", databaseName, ' and target ', targetName, ' who has the covariate') - ) - ) - - names(result)[length(result)] <- paste0('countT', temp[i,1], 'D', ifelse(temp[i,2] <0, 'n', ''), abs(temp[i,2]) ) - - result[[length(result) + 1]] <- reactable::colDef( - header = withTooltip( - paste0("Mean-", temp[i,1], '-', temp[i,2]), - paste0("The mean covariate value for patients in database ", databaseName, ' and target ', targetName) - ), - format = reactable::colFormat( - digits = 3 - ) - ) - names(result)[length(result)] <- paste0('averageT', temp[i,1], 'D', ifelse(temp[i,2] <0, 'n', ''), abs(temp[i,2]) ) - - } - return(result) + selection1 <- subTargetId() + + if(length(selection1) == 0){ + runTables <- FALSE + shiny::showNotification('No results for section 1') } - }) - - #get results - resultTable <- shiny::reactive({ - getCohortData( + selection2 <- input$comparatorId + + if(length(selection2) == 0){ + runTables <- FALSE + shiny::showNotification('No results for section 2') + } + + + if(runTables){ + resultTable <- characterizatonGetCohortData( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - targetIds = inputSelected()$targetIds, - databaseIds = inputSelected()$databaseIds, - analysisIds = inputSelected()$analysisIds + targetIds = c(selection1,selection2), + databaseIds = input$databaseId, + minThreshold = 0.01, + addSMD = T ) - }) - - resultTableServer( - id = 'mainTable', - df = resultTable, - colDefsInput = columns() - ) + + countTable <- characterizatonGetCohortCounts( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = c(selection1,selection2), + databaseIds = input$databaseId + ) + + continuousTable <- characterizatonGetCohortComparisonDataContinuous( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = c(selection1,selection2), + databaseIds = input$databaseId + ) + + resultTableServer( + id = 'mainTable', + df = resultTable, + details = data.frame( + Target = names(targetGroups)[which(targetGroups == subTargetId())], + Comparator = names(comparatorGroups())[which(comparatorGroups() == input$comparatorId)], + Database = names(inputVals()$databaseIds)[input$databaseId == inputVals()$databaseIds], + Analysis = 'Cohort comparison within database' + ), + downloadedFileName = 'cohort_comparison_binary', + colDefsInput = characterizationCohortsColumns( + addExtras = T, + elementId = session$ns('main-table-filter') + ), + elementId = session$ns('main-table-filter') + ) + + resultTableServer( + id = 'continuousTable', + df = continuousTable, + details = data.frame( + Target = names(targetGroups)[which(targetGroups == subTargetId())], + Comparator = names(comparatorGroups())[which(comparatorGroups() == input$comparatorId)], + Database = names(inputVals()$databaseIds)[input$databaseId == inputVals()$databaseIds], + Analysis = 'Cohort comparison within database' + ), + downloadedFileName = 'cohort_comparison_cont', + colDefsInput = characterizationCohortsColumnsContinuous( + addExtras = T, + elementId = session$ns('continuous-table-filter') + ), + elementId = session$ns('continuous-table-filter') + ) + + resultTableServer( + id = 'countTable', + df = countTable, + details = data.frame( + Target = names(targetGroups)[which(targetGroups == subTargetId())], + Comparator = names(comparatorGroups())[which(comparatorGroups() == input$comparatorId)], + Database = names(inputVals()$databaseIds)[input$databaseId == inputVals()$databaseIds], + Analysis = 'Cohort comparison within database' + ), + downloadedFileName = 'cohort_comparison_count', + colDefsInput = characteriationCountTableColDefs( + elementId = session$ns('count-table-filter') + ), + elementId = session$ns('count-table-filter') + )} + + }) return(invisible(NULL)) @@ -233,168 +292,722 @@ characterizationTableServer <- function( } -getCohortData <- function( +characterizationCohortsColumns <- function( + addExtras = F, + elementId + ){ + + res <- list( + covariateName = reactable::colDef( + header = withTooltip( + "Covariate Name", + "The name of the covariate" + ), + minWidth = 300 + ), + covariateId = reactable::colDef( + show = F, + header = withTooltip("Covariate ID", + "Unique identifier of the covariate") + ), + minPriorObservation = reactable::colDef( + header = withTooltip( + "Min Prior Obs", + "The minimum prior observation a patient in the target + population must have to be included."), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + SMD = reactable::colDef( + header = withTooltip("SMD", + "Standardized mean difference between the target and comparator percentages"), + format = reactable::colFormat(digits = 3) + ), + absSMD = reactable::colDef( + header = withTooltip("absSMD", + "Absolute standardized mean difference between the target and comparator percentages"), + format = reactable::colFormat(digits = 3), + filterable = TRUE, + filterMethod = reactable::JS("function(rows, columnId, filterValue) { + return rows.filter(function(row) { + return row.values[columnId] >= filterValue + }) + }"), + filterInput = function(values, name) { + oninput <- sprintf("Reactable.setFilter('%s', '%s', this.value)", elementId, name) + shiny::tags$input( + type = "range", + min = floor(min(values, na.rm = T)), + max = ceiling(max(values, na.rm = T)), + value = floor(min(values, na.rm = T)), + oninput = oninput, + onchange = oninput, # For IE11 support + "aria-label" = sprintf("Filter by minimum %s", name) + ) + } + ), + analysisName = reactable::colDef( + header = withTooltip( + "Covariate Class", + "Class/type of the covariate" + ) + ) + ) + + if(addExtras){ + res <- append( + res, + list( + sumValue_1 = reactable::colDef( + header = withTooltip("Target Sum", + "The total sum of the covariate for the target cohort."), + cell = function(value) { + if (value >= 0) value else '< min threshold' + } + ), + sumValue_2 = reactable::colDef( + header = withTooltip("Compatator Sum", + "The total sum of the covariate for the comparator cohort."), + cell = function(value) { + if (value >= 0) value else '< min threshold' + } + ), + averageValue_1 = reactable::colDef( + header = withTooltip("Target %", + "The percentage of the target cohort who had the covariate prior to index."), + cell = function(value) { + if (value >= 0) paste0(round(value*100, digits = 3),'%') else '< min threshold' + } + ), + averageValue_2 = reactable::colDef( + header = withTooltip("Comparator %", + "The percentage of the comparator cohort who had the covariate prior to index"), + cell = function(value) { + if (value >= 0) paste0(round(value*100, digits = 3),'%') else '< min threshold' + } + ) + ) + ) + } + return(res) +} + +characteriationCountTableColDefs <- function( + elementId + ){ + result <- list( + selection = reactable::colDef( + filterable = T + ), + cohortName = reactable::colDef( + header = withTooltip("Cohort", + "Name of the cohort"), + filterable = T + ), + minPriorObservation = reactable::colDef( + header = withTooltip( + "Min Prior Obs", + "The minimum prior observation a patient in the target + population must have to be included."), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + rowCount = reactable::colDef( + header = withTooltip("Record Count", + "Count of the number of records"), + filterable = T + ), + personCount = reactable::colDef( + header = withTooltip("Person Count", + "Count of the number of persons"), + filterable = T + ) + ) + return(result) +} + +characterizationCohortsColumnsContinuous <- function( + addExtras = F, + elementId + ){ + res <- list( + covariateName = reactable::colDef( + header = withTooltip( + "Covariate Name", + "The name of the covariate" + ), + filterable = T, + minWidth = 300, + ), + databaseName = reactable::colDef( + header = withTooltip( + "Database", + "The name of the database" + ), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + covariateId = reactable::colDef( + show = F, + header = withTooltip("Covariate ID", + "Unique identifier of the covariate") + ), + minPriorObservation = reactable::colDef( + header = withTooltip( + "Min Prior Obs", + "The minimum prior observation a patient in the target + population must have to be included."), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + outcomeWashoutPeriod = reactable::colDef( + show = F + ), + countValue = reactable::colDef( + header = withTooltip("Count", + "Number of people with the covariate in the cohort."), + cell = function(value) { + if (value >= 0) value else '< min threshold' + }, + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + averageValue = reactable::colDef( + header = withTooltip("Mean", + "The mean value of the covariate in the cohort"), + cell = function(value) { + if (value >= 0) round(value, digits = 3) else '< min threshold' + } + ), + standardDeviation = reactable::colDef( + header = withTooltip("StDev", + "The standard deviation value of the covariate in the cohort"), + cell = function(value) { + if (value >= 0) round(value, digits = 3) else '< min threshold' + } + ), + medianValue = reactable::colDef( + header = withTooltip("Median", + "The median value of the covariate in the cohort."), + cell = function(value) { + round(value, digits = 3) + } + ), + minValue = reactable::colDef( + header = withTooltip("Min Value", + "Minimum value of the covariate in the cohort"), + format = reactable::colFormat(digits = 3) + ), + maxValue = reactable::colDef( + header = withTooltip("Max Value", + "Maximum value the covariate in the cohort"), + format = reactable::colFormat(digits = 3) + ), + p25Value = reactable::colDef( + header = withTooltip("25th %tile", + "25th percentile value of the covariate in the cohort"), + format = reactable::colFormat(digits = 3) + ), + p75Value = reactable::colDef( + header = withTooltip("75th %tile", + "75th percentile value of the covariate in the cohort"), + format = reactable::colFormat(digits = 3) + ), + p10Value = reactable::colDef( + header = withTooltip("10th %tile", + "10th percentile value of the covariate in the cohort"), + format = reactable::colFormat(digits = 3) + ), + p90Value = reactable::colDef( + header = withTooltip("90th %tile", + "90th percentile value of the covariate in the cohort"), + format = reactable::colFormat(digits = 3) + ) + ) + + if(addExtras){ + res <- append( + res, + list( + SMD = reactable::colDef( + header = withTooltip("SMD", + "Standardized mean difference"), + format = reactable::colFormat(digits = 3) + ), + absSMD = reactable::colDef( + header = withTooltip("absSMD", + "Absolute standardized mean difference"), + format = reactable::colFormat(digits = 3) + ), + countValue_1 = reactable::colDef( + header = withTooltip("Target Count", + "Number of people with the covariate for the target cohort."), + cell = function(value) { + if (value >= 0) value else '< min threshold' + } + ), + countValue_2 = reactable::colDef( + header = withTooltip("Comparator Count", + "Number of people with the covariate for the comparator cohort."), + cell = function(value) { + if (value >= 0) value else '< min threshold' + } + ), + averageValue_1 = reactable::colDef( + header = withTooltip("Target Mean", + "The mean of the covariate for the target cohort."), + cell = function(value) { + if (value >= 0) round(value, digits = 3) else '< min threshold' + } + ), + averageValue_2 = reactable::colDef( + header = withTooltip("Comparator Mean", + "The mean of the covariate for the comparator cohort."), + cell = function(value) { + if (value >= 0) round(value, digits = 3) else '< min threshold' + } + ), + standardDeviation_1 = reactable::colDef( + header = withTooltip("Target StDev", + "The standard deviation of the covariate for the target cohort."), + cell = function(value) { + if (value >= 0) round(value, digits = 3) else '< min threshold' + } + ), + standardDeviation_2 = reactable::colDef( + header = withTooltip("Comparator StDev", + "The standard deviation of the covariate for the comparator cohort."), + cell = function(value) { + if (value >= 0) round(value, digits = 3) else '< min threshold' + } + ), + medianValue_1 = reactable::colDef( + header = withTooltip("Target Median", + "The median of the covariate for the target cohort."), + cell = function(value) { + round(value, digits = 3) + } + ), + medianValue_2 = reactable::colDef( + header = withTooltip("Comparator Median", + "The median of the covariate for the comparator cohort."), + cell = function(value) { + round(value, digits = 3) + } + ), + minValue_2 = reactable::colDef( + header = withTooltip("Comparator Min Value", + "Minimum value of the comparator cohort"), + format = reactable::colFormat(digits = 3) + ), + minValue_1 = reactable::colDef( + header = withTooltip("Target Min Value", + "Minimum value of the target cohort"), + format = reactable::colFormat(digits = 3) + ), + maxValue_2 = reactable::colDef( + header = withTooltip("Comparator Max Value", + "Maximum value of the comparator cohort"), + format = reactable::colFormat(digits = 3) + ), + maxValue_1 = reactable::colDef( + header = withTooltip("Target Max Value", + "Maximum value of the target cohort"), + format = reactable::colFormat(digits = 3) + ), + p25Value_2 = reactable::colDef( + header = withTooltip("Comparator 25th %tile", + "25th percentile value of the comparator cohort"), + format = reactable::colFormat(digits = 3) + ), + p25Value_1 = reactable::colDef( + header = withTooltip("Target 25th %tile", + "25th percentile value of the target cohort"), + format = reactable::colFormat(digits = 3) + ), + p75Value_2 = reactable::colDef( + header = withTooltip("Comparator 75th %tile", + "75th percentile value of the comparator cohort"), + format = reactable::colFormat(digits = 3) + ), + p75Value_1 = reactable::colDef( + header = withTooltip("Target 75th %tile", + "75th percentile value of the target cohort"), + format = reactable::colFormat(digits = 3) + ) + ) + ) + } + + return(res) +} + + +characterizatonGetCohortCounts <- function( connectionHandler, resultDatabaseSettings, targetIds, - databaseIds, - analysisIds + databaseIds ){ - if(is.null(targetIds) | is.null(databaseIds)){ - return(NULL) + start <- Sys.time() + result <- connectionHandler$queryDb( + sql = " +select distinct + cc.target_cohort_id as cohort_definition_id, + cc.min_prior_observation, + cc.row_count, + cc.person_count, + d.cdm_source_abbreviation as database_name, + d.database_id, + cg.cohort_name + from + @schema.@database_table d + inner join + @schema.@c_table_prefixcohort_counts cc + on d.database_id = cc.database_id + inner join + @schema.@cg_table_prefixcohort_definition cg + on cg.cohort_definition_id = cc.target_cohort_id + + where + cc.target_cohort_id in (@target_ids) + and cc.cohort_type = 'Target' + AND cc.database_id in (@database_ids) + ; + ", + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix, + target_ids = paste0(targetIds, collapse= ','), + database_ids = paste0("'",databaseIds,"'",collapse= ','), + database_table = resultDatabaseSettings$databaseTable + ) + end <- Sys.time() - start + message(paste0('Extracting ', nrow(result) ,' cohort count rows took: ', round(end, digits = 2), ' ', units(end))) + + if(length(targetIds)>1){ + result <- merge( + x = result, + y = data.frame( + selection = c('Target','Comparator'), + cohortDefinitionId = targetIds + ), + by = 'cohortDefinitionId' + ) + } else{ + result$selection <- result$databaseName } - combinations <- expand.grid(targetIds, databaseIds) - combinations[,2] <- as.double(as.character(combinations[,2])) + result <- result %>% dplyr::select( + 'selection', + 'cohortName', + 'minPriorObservation', + 'rowCount', + 'personCount' + ) -sql <- paste0( -"select ref.covariate_id, ref.covariate_name, an.analysis_name,", - -paste( - lapply(1:nrow(combinations), function(i){ - paste0( -"max(case when temp.selection_id = ",i," then temp.sum_value else 0 end) as count_t",combinations[i,1],'_d',ifelse(combinations[i,2] <0, 'n', ''),abs(combinations[i,2]),",", -"max(case when temp.selection_id = ",i," then temp.average_value else 0 end) as average_t",combinations[i,1],'_d',ifelse(combinations[i,2] <0, 'n', ''),abs(combinations[i,2]) -)}), collapse = ','), - -" from @schema.@c_table_prefixcovariate_ref ref - inner join @schema.@c_table_prefixanalysis_ref an - on an.RUN_ID = ref.RUN_ID and - an.analysis_id = ref.analysis_id and - ref.analysis_id in (@analysis_ids) + return(result) +} - left join -( ", - +characterizatonGetCohortData <- function( + connectionHandler, + resultDatabaseSettings, + targetIds, + databaseIds, + minThreshold = 0.01, + addSMD = F +){ -paste( - lapply(1:nrow(combinations), function(i){ - paste0( - " - select - co",i,".run_id, co",i,".COVARIATE_ID, co",i,".SUM_VALUE, co",i,".AVERAGE_VALUE, - ",i," as selection_id - from - @schema.@c_table_prefixCOVARIATES co",i," - inner join - (select * from @schema.@c_table_prefixcohort_details - where DATABASE_ID = '@database",i,"' and - TARGET_COHORT_ID = @target",i," and COHORT_TYPE = 'T' - ) as cd",i," - on co",i,".COHORT_DEFINITION_ID = cd",i,".COHORT_DEFINITION_ID - and co",i,".DATABASE_ID = cd",i,".DATABASE_ID" - ) - - }), collapse = ' union '), - -") temp -on ref.run_id = temp.run_id and -ref.covariate_id = temp.covariate_id + if(is.null(targetIds) | is.null(databaseIds)){ + warning('Ids cannot be NULL') + return(NULL) + } -group by -ref.covariate_id, ref.covariate_name, an.analysis_name -" + shiny::withProgress(message = 'characterizatonGetCohortData', value = 0, { + + shiny::incProgress(1/4, detail = paste("Setting types")) + + types <- data.frame( + type = 1:(length(targetIds)*length(databaseIds)), + cohortDefinitionId = rep(targetIds, length(databaseIds)), + databaseId = rep(databaseIds, length(targetIds)) + ) + + shiny::incProgress(2/4, detail = paste("Extracting data")) + + sql <- "select ref.covariate_name, + s.min_prior_observation, + cov.target_cohort_id as cohort_definition_id, + cov.* from + @schema.@c_table_prefixCOVARIATES cov + inner join + @schema.@c_table_prefixcovariate_ref ref + on cov.covariate_id = ref.covariate_id + and cov.setting_id = ref.setting_id + and cov.database_id = ref.database_id + inner join + @schema.@c_table_prefixsettings s + on s.database_id = cov.database_id + and s.setting_id = cov.setting_id + + where + cov.target_cohort_id in (@target_ids) + and cov.cohort_type = 'Target' + AND cov.database_id in (@database_ids) + AND cov.average_value >= @min_threshold;" + + start <- Sys.time() + # settings.min_characterization_mean needed? + res <- connectionHandler$queryDb( + sql = sql, + target_ids = paste0(targetIds, collapse = ','), + database_ids = paste0("'",databaseIds,"'", collapse = ','), + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + min_threshold = minThreshold + ) + end <- Sys.time() - start + shiny::incProgress(3/4, detail = paste("Extracted data")) + message(paste0('Extracting ', nrow(res) ,' characterization cohort rows took: ', round(end, digits = 2), ' ', units(end))) + + # add the first/section type + res <- merge(res, types, by = c('cohortDefinitionId','databaseId')) + + # pivot + result <- tidyr::pivot_wider( + data = res, + id_cols = c('covariateName', 'covariateId','minPriorObservation'), + names_from = 'type', + values_from = c('sumValue', 'averageValue'), + values_fn = mean, + values_fill = -1 + ) + + if(addSMD == T){ + # TODO get min_characterization_mean from settings table + # minCharacterizationMean <- minThreshold + # add SMD + if(sum(c('averageValue_1','averageValue_2') %in% colnames(result)) == 2){ + convertMissing <- function(vec){sapply(vec, function(x) ifelse(x==-1, minThreshold, x))} + + Ns <- c() + for(minPriorObservation in unique(result$minPriorObservation)){ + ind <- result$minPriorObservation == minPriorObservation + Ns <- rbind(Ns, + data.frame( + minPriorObservation = minPriorObservation, + N_1 = max(result$sumValue_1[ind]/result$averageValue_1[ind], na.rm = T), + N_2 = max(result$sumValue_2[ind]/result$averageValue_2[ind], na.rm = T) + ) + ) + } + result <- merge(result, Ns, by = 'minPriorObservation') + result$firstVar <- ((convertMissing(result$averageValue_1)-1)^2*result$sumValue_1 + (convertMissing(result$averageValue_1)-0)^2*(result$N_1-result$sumValue_1))/result$N_1 + result$secondVar <- ((convertMissing(result$averageValue_2)-1)^2*result$sumValue_2 + (convertMissing(result$averageValue_2)-0)^2*(result$N_2-result$sumValue_2))/result$N_2 + result$SMD <- (convertMissing(result$averageValue_1) - convertMissing(result$averageValue_2))/(sqrt((result$firstVar+result$secondVar)/2)) + result$absSMD <- abs(result$SMD) + result <- result %>% dplyr::select(-"firstVar",-"secondVar", -"N_1", -"N_2") + + } else{ + shiny::showNotification('Unable to add SMD due to missing columns') + } + } + shiny::incProgress(4/4, detail = paste("Done")) + }) + + return(result) +} -) +characterizatonGetCohortComparisonDataContinuous <- function( + connectionHandler, + resultDatabaseSettings, + targetIds, + databaseIds, + pivot = T +){ - inputs <- c( - as.character(combinations$Var2), - as.character(combinations$Var1), - resultDatabaseSettings$schema, - resultDatabaseSettings$cTablePrefix, - paste0(analysisIds, collapse = ',') - ) - names(inputs) <- c( - paste0('database', 1:nrow(combinations)), - paste0('target', 1:nrow(combinations)), - 'schema', - 'c_table_prefix', - 'analysis_ids' - ) - inputs <- as.list(inputs) - inputs$sql <- sql + if(is.null(targetIds) | is.null(databaseIds)){ + warning('Ids cannot be NULL') + return(NULL) + } + targetIds <- unique(targetIds) + databaseIds <- unique(databaseIds) - result <- do.call(connectionHandler$queryDb, inputs) - -return(result) + shiny::withProgress(message = 'characterizatonGetCohortDataContinuous', value = 0, { + + shiny::incProgress(1/4, detail = paste("Setting types")) + + types <- data.frame( + type = 1:(length(targetIds)*length(databaseIds)), + cohortDefinitionId = rep(targetIds, length(databaseIds)), + databaseId = rep(databaseIds, length(targetIds)) + ) + + shiny::incProgress(2/4, detail = paste("Extracting data")) + + sql <- "select ref.covariate_name, + s.min_prior_observation, + cov.target_cohort_id as cohort_definition_id, + cov.*, + d.CDM_SOURCE_ABBREVIATION as database_name + + from + @schema.@c_table_prefixCOVARIATES_continuous cov + inner join + @schema.@c_table_prefixcovariate_ref ref + on cov.covariate_id = ref.covariate_id + and cov.setting_id = ref.setting_id + and cov.database_id = ref.database_id + inner join + @schema.@c_table_prefixsettings s + on cov.setting_id = s.setting_id + and cov.database_id = s.database_id + inner join @schema.@database_meta_table d + on s.database_id = d.database_id + + where + cov.target_cohort_id in (@target_ids) + and cov.cohort_type = 'Target' + AND cov.database_id in (@database_ids);" + + start <- Sys.time() + # settings.min_characterization_mean needed? + res <- connectionHandler$queryDb( + sql = sql, + target_ids = paste0(targetIds, collapse = ','), + database_ids = paste0("'",databaseIds,"'", collapse = ','), + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + database_meta_table = resultDatabaseSettings$databaseTable + ) + end <- Sys.time() - start + shiny::incProgress(3/4, detail = paste("Extracted data")) + message(paste0('Extracting ', nrow(res) ,' continuous characterization cohort rows took: ', round(end, digits = 2), ' ', units(end))) + + # add the first/section type + res <- merge(res, types, by = c('cohortDefinitionId','databaseId')) + + if(pivot){ + # if pivot + res <- tidyr::pivot_wider( + data = res, + id_cols = c('covariateName', 'covariateId','minPriorObservation'), + names_from = 'type', + values_from = c('countValue', 'averageValue', 'standardDeviation', 'medianValue','minValue', 'maxValue', 'p25Value','p75Value'), + values_fn = mean, + values_fill = -1 + ) + + # if both have results then add SMD + if(length(unique(res$type)) == 2){ + res <- res %>% + dplyr::mutate( + SMD = (.data$averageValue_1-.data$averageValue_2)/(sqrt((.data$standardDeviation_1^2 + .data$standardDeviation_2^2)/2)) + ) %>% + dplyr::mutate( + absSMD = abs(.data$SMD) + ) + } + } else{ + # if multiple databases make the type the databaseName + res$type <- res$databaseName + res <- res %>% dplyr::select(-"cohortDefinitionId", -"databaseId", -"type", + -"settingId", -"targetCohortId", -"outcomeCohortId", + -"cohortType") %>% + dplyr::relocate("databaseName", .after = "covariateName") + } + + shiny::incProgress(4/4, detail = paste("Done")) + }) + + return(res) } - - -getDecCohortsInputs <- function( +characterizationGetCohortsInputs <- function( connectionHandler, - resultDatabaseSettings + resultDatabaseSettings, + targetId # reactive ) { - #shiny::withProgress(message = 'Getting target comparison inputs', value = 0, { - - - sql <- - ' select distinct c.cohort_definition_id, c.cohort_name from - @schema.@cg_table_prefixcohort_definition c - inner join - (select distinct TARGET_COHORT_ID as id - from @schema.@c_table_prefixcohort_details - ) ids - on ids.id = c.cohort_definition_id - ;' - - #shiny::incProgress(1/4, detail = paste("Extracting targetIds")) - - idVals <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - c_table_prefix = resultDatabaseSettings$cTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix - ) - ids <- idVals$cohortDefinitionId - names(ids) <- idVals$cohortName - - #shiny::incProgress(2/4, detail = paste("Extracted targetIds")) - - sql <- 'select d.database_id, d.cdm_source_abbreviation as database_name - from @schema.@database_table d;' + sql <- "select distinct + d.database_id, d.cdm_source_abbreviation as database_name + from @schema.@database_table d - #shiny::incProgress(3/4, detail = paste("Extracting databaseIds")) + inner join + @schema.@c_table_prefixcohort_details cd + on d.database_id = cd.database_id + where cd.target_cohort_id = @target_id + and cd.cohort_type = 'Target' + ;" database <- connectionHandler$queryDb( sql = sql, schema = resultDatabaseSettings$schema, - database_table = resultDatabaseSettings$databaseTable + database_table = resultDatabaseSettings$databaseTable, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + target_id = targetId() ) databaseIds <- database$databaseId names(databaseIds) <- database$databaseName - - sql <- 'select distinct analysis_id, analysis_name - from @schema.@c_table_prefixanalysis_ref order by analysis_name desc;' - - #shiny::incProgress(3/4, detail = paste("Extracting databaseIds")) - - analyses <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - c_table_prefix = resultDatabaseSettings$cTablePrefix - ) - analysisIds <- analyses$analysisId - names(analysisIds) <- analyses$analysisName - - - #shiny::incProgress(4/4, detail = paste("Done")) - - # }) - return( list( - cohortIds = ids, - databaseIds = databaseIds, - analysisIds = analysisIds + databaseIds = databaseIds ) ) - -} +} \ No newline at end of file diff --git a/R/characterization-database.R b/R/characterization-database.R new file mode 100644 index 00000000..46b3b31e --- /dev/null +++ b/R/characterization-database.R @@ -0,0 +1,320 @@ +# @file characterization-timeToEvent.R +# +# Copyright 2024 Observational Health Data Sciences and Informatics +# +# This file is part of OhdsiShinyModules +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# view two cohorts and compare +characterizationDatabaseComparisonViewer <- function(id) { + ns <- shiny::NS(id) + + shiny::div( + + # UI for inputs + # summary table + shinydashboard::box( + collapsible = TRUE, + title = "Options", + width = "100%", + shiny::uiOutput(ns("inputs")) + ), + + # displayed inputs + shiny::conditionalPanel( + condition = "input.generate != 0", + ns = ns, + + inputSelectionDfViewer(id = ns('inputSelected'), title = 'Selected'), + + # add basic table + shiny::tabsetPanel( + type = 'pills', + shiny::tabPanel( + title = 'Counts', + resultTableViewer(id = ns('countTable'), boxTitle = 'Counts') + ), + shiny::tabPanel( + title = 'Binary', + resultTableViewer(id = ns('mainTable'), boxTitle = 'Binary') + ), + shiny::tabPanel( + title = 'Continuous', + resultTableViewer(id = ns('continuousTable'), boxTitle = 'Continuous') + ) + ) + ) + ) +} + + + +characterizationDatabaseComparisonServer <- function( + id, + connectionHandler, + resultDatabaseSettings, + options, + parents, + parentIndex, # reactive + subTargetId # reactive +) { + shiny::moduleServer( + id, + function(input, output, session) { + + # TODO react to subTargetId + inputVals <- shiny::reactive({ + characterizationGetCohortsInputs( + connectionHandler, + resultDatabaseSettings, + targetId = subTargetId + )}) + + output$inputs <- shiny::renderUI({ + + shiny::div( + shiny::selectInput( + inputId = session$ns('databaseIds'), + label = 'Databases: ', + choices = inputVals()$databaseIds, + selected = inputVals()$databaseIds[1], + multiple = T + ), + + shiny::sliderInput( + inputId = session$ns('minThreshold'), + label = 'Covariate Threshold', + min = 0, + max = 1, + value = 0.01, + step = 0.01, + ticks = F + ), + + shiny::actionButton( + inputId = session$ns('generate'), + label = 'Generate' + ) + ) + + }) + + + # show selected inputs to user + inputSelectionDfServer( + id = 'inputSelected', + dataFrameRow = selected, + ncol = 1 + ) + + #get results + selected <- shiny::reactiveVal() + shiny::observeEvent(input$generate,{ + + if(is.null(input$databaseIds)){ + shiny::showNotification('No databases selected') + return(NULL) + } + if(length(input$databaseIds) == 0 ){ + shiny::showNotification('No databases selected') + return(NULL) + } + + selectedDatabases <- paste0( + names(inputVals()$databaseIds)[which(inputVals()$databaseIds %in% input$databaseIds)], + collapse = ',' + ) + + selected( + data.frame( + Databases = selectedDatabases, + `Minimum Covariate Threshold` = input$minThreshold + ) + ) + + + #get results + results <- list( + table = data.frame(), + databaseNames = data.frame( + id = 1, + databaseName = 'None' + ) + ) + continuousTable <- data.frame() + countTable <- data.frame() + + if(length(input$databaseIds) > 0){ + + countTable <- characterizatonGetCohortCounts( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = subTargetId(), + databaseIds = input$databaseIds + ) + + result <- characterizatonGetDatabaseComparisonData( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = subTargetId(), + databaseIds = input$databaseIds, + minThreshold = input$minThreshold + ) + + continuousTable <- characterizatonGetCohortComparisonDataContinuous( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = subTargetId(), + databaseIds = input$databaseIds, + pivot = F + ) + + } else{ + shiny::showNotification('No results') + } + + + databaseNames <- result$databaseNames + + meanColumns <- lapply(1:nrow(databaseNames), function(i){ + reactable::colDef( + header = withTooltip( + paste0(databaseNames$databaseName[i], ' %'), + paste0("The percentage of the target population in database ", databaseNames$databaseName[i], ' who had the covariate prior.') + ), + cell = function(value) { + if (value >= 0) paste0(round(value*100, digits = 3),' %') else '< min threshold' + } + ) + }) + names(meanColumns) <- unlist(lapply(1:nrow(databaseNames), function(i) paste0('averageValue_',databaseNames$id[i]))) + + sumColumns <- lapply(1:nrow(databaseNames), function(i){ + reactable::colDef( + header = withTooltip( + paste0(databaseNames$databaseName[i], " Count"), + paste0("The number of people in the target cohort in database ", databaseNames$databaseName[i], ' who have the covariate prior.') + ), + cell = function(value) { + if (value >= 0) value else '< min threshold' + } + ) + }) + names(sumColumns) <- unlist(lapply(1:nrow(databaseNames), function(i) paste0('sumValue_',databaseNames$id[i]))) + + targetGroups <- characterizationGetChildren(options, parentIndex()) + + resultTableServer( + id = 'countTable', + df = countTable, + details = data.frame( + Target = names(targetGroups)[which(targetGroups == subTargetId())], + Databases = selectedDatabases, + `Minimum Covariate Threshold` = input$minThreshold, + Analysis = 'Cohort comparison across databases' + ), + downloadedFileName = 'database_comparison_counts', + colDefsInput = characteriationCountTableColDefs( + elementId = session$ns('count-table-filter') + ), + elementId = session$ns('count-table-filter') + ) + resultTableServer( + id = 'mainTable', + df = result$table, + details = data.frame( + Target = names(targetGroups)[which(targetGroups == subTargetId())], + Databases = selectedDatabases, + `Minimum Covariate Threshold` = input$minThreshold, + Analysis = 'Cohort comparison across databases' + ), + downloadedFileName = 'database_comparison_binary', + colDefsInput = append( + characterizationCohortsColumns( + elementId = session$ns('main-table-filter') + ), + append( + sumColumns, + meanColumns + ) + ), + elementId = session$ns('main-table-filter') + ) + + resultTableServer( + id = 'continuousTable', + df = continuousTable, + details = data.frame( + Target = names(targetGroups)[which(targetGroups == subTargetId())], + Databases = selectedDatabases, + `Minimum Covariate Threshold` = input$minThreshold, + Analysis = 'Cohort comparison across databases' + ), + downloadedFileName = 'database_comparison_cont', + colDefsInput = characterizationCohortsColumnsContinuous( + elementId = session$ns('continuous-table-filter') + ), + elementId = session$ns('continuous-table-filter') + ) + }) + + + return(invisible(NULL)) + + }) + +} + +characterizatonGetDatabaseComparisonData <- function( + connectionHandler, + resultDatabaseSettings, + targetIds, + databaseIds, + minThreshold +){ + + result <- characterizatonGetCohortData( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + databaseIds = databaseIds, + minThreshold = minThreshold, + addSMD = length(databaseIds) == 2 + ) + + databaseNames <- connectionHandler$queryDb( + sql = "select cdm_source_abbreviation as database_name, database_id + from @schema.@database_table;", + schema = resultDatabaseSettings$schema, + database_table = resultDatabaseSettings$databaseTable + ) + + databaseNames <- merge( + databaseNames, + data.frame( + id = 1:length(databaseIds), + databaseId = databaseIds + ), + by = 'databaseId' + ) + + return( + list( + table = result, + databaseNames = databaseNames + ) + ) + +} diff --git a/R/characterization-dechallengeRechallenge.R b/R/characterization-dechallengeRechallenge.R index 3ecbb464..57e7b4a4 100644 --- a/R/characterization-dechallengeRechallenge.R +++ b/R/characterization-dechallengeRechallenge.R @@ -17,164 +17,254 @@ # limitations under the License. -#' The module viewer for exploring Dechallenge Rechallenge results -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' -#' @return -#' The user interface to the description Dechallenge Rechallenge module -#' -#' @export characterizationDechallengeRechallengeViewer <- function(id) { ns <- shiny::NS(id) shiny::div( - # helper component module - infoHelperViewer( - id = "helper", - helpLocation= system.file("characterization-www", "help-dechallengeRechallenge.html", package = utils::packageName()) - ), - - # input component module - inputSelectionViewer(id = ns('input-selection')), - - shiny::conditionalPanel( - condition = 'input.generate != 0', - ns = shiny::NS(ns("input-selection")), - - resultTableViewer(ns('tableResults')) + shiny::uiOutput(ns('warning')), - - ) + shinydashboard::box( + status = 'info', + width = '100%', + solidHeader = TRUE, + resultTableViewer(ns('tableResults')) + ) ) } - -#' The module server for exploring Dechallenge Rechallenge results -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' @param connectionHandler the connection to the prediction result database -#' @param resultDatabaseSettings a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix -#' -#' @return -#' The server to the Dechallenge Rechallenge module -#' -#' @export characterizationDechallengeRechallengeServer <- function( id, connectionHandler, - resultDatabaseSettings + resultDatabaseSettings, + targetId, + outcomeId ) { shiny::moduleServer( id, function(input, output, session) { - - # get the possible target ids - bothIds <- dechalRechalGetIds( - connectionHandler, - resultDatabaseSettings - ) - # input selection component - inputSelected <- inputSelectionServer( - id = "input-selection", - inputSettingList = list( - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'targetId', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Target: ', - choices = bothIds$targetIds, - #choicesOpt = list(style = rep_len("color: black;", 999)), - selected = bothIds$targetIds[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'outcomeId', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Outcome: ', - choices = bothIds$outcomeIds, - #choicesOpt = list(style = rep_len("color: black;", 999)), - selected = bothIds$outcomeIds[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ) + options <- shiny::reactive({ + characterizationGetCaseSeriesOptions( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetId = targetId(), + outcomeId = outcomeId() ) - ) + }) + + # fetch data when targetId changes allData <-shiny::reactive({ getDechalRechalInputsData( - targetId = inputSelected()$targetId, - outcomeId = inputSelected()$outcomeId, + targetId = targetId(), + outcomeId = outcomeId(), connectionHandler = connectionHandler, resultDatabaseSettings ) }) + + + # warning when not unique + targetUniquePeople <- shiny::reactive({ + isCohortUniquePeople( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + cohortId = targetId() + ) + }) + + outcomeUniquePeople <- shiny::reactive({ + isCohortUniquePeople( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + cohortId = outcomeId() + ) + }) + + output$warning <- shiny::renderUI( + if(targetUniquePeople() || outcomeUniquePeople()){ + shinydashboard::box( + status = 'warning', + width = '100%', + title = shiny::span( shiny::icon("triangle-exclamation"),'Warnings'), + solidHeader = TRUE, + shiny::p( + ifelse(targetUniquePeople(), + 'WARNING: The target cohort does not have multiple records per person, so observing rechallenge attempts not possible.', + '') + ), + shiny::p( + ifelse(outcomeUniquePeople(), + 'WARNING: The outcome cohort does not have multiple records per person, so observing rechallenge attempts not possible.', + '') + ) + ) + } else{ + shiny::renderUI(shiny::div()) + } + ) - #databases(allData$databaseId) - #dechallengeStopInterval(allData$dechallengeStopInterval) - #dechallengeEvaluationWindow(allData$dechallengeEvaluationWindow) - - tableOutputs <- resultTableServer( - id = "tableResults", - df = allData, - colDefsInput = list( - targetCohortDefinitionId = reactable::colDef(show = F), - databaseId = reactable::colDef(show = F), - outcomeCohortDefinitionId = reactable::colDef(show = F), - - databaseName = reactable::colDef(name = 'Database'), - + + characteriationDechalRechalColDefs <- function(){ + result <- list( + databaseName = reactable::colDef( + header = withTooltip("Database", + "Name of the database"), + filterable = T + ), + databaseId = reactable::colDef( + show = F + ), + targetCohortDefinitionId = reactable::colDef( + show = F + ), + outcomeCohortDefinitionId = reactable::colDef( + show = F + ), + dechallengeStopInterval = reactable::colDef( + header = withTooltip("Dechallenge Stop Interval", + "An integer specifying the how much time to add to the cohort_end when determining whether the event starts during cohort and ends after"), + filterable = T + ), + dechallengeEvaluationWindow = reactable::colDef( + header = withTooltip("Dechallenge Evaluation Window", + "A period of time evaluated for outcome recurrence after discontinuation of exposure, among patients with challenge outcomes"), + filterable = T + ), + numExposureEras = reactable::colDef( + header = withTooltip("# of Exposure Eras", + "Distinct number of exposure events (i.e. drug eras) in a given target cohort"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), + numPersonsExposed = reactable::colDef( + header = withTooltip("# of Exposed Persons", + "Distinct nuber of people exposed in target cohort. A person must have at least 1 day exposure to be included"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), + numCases = reactable::colDef( + header = withTooltip("# of Cases", + "Distinct number of persons in outcome cohort. A person must have at least 1 day of observation time to be included"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), + dechallengeAttempt = reactable::colDef( + header = withTooltip("# of Dechallenge Attempts", + "Distinct count of people with observable time after discontinuation of the exposure era during which the challenge outcome occurred"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), + dechallengeFail = reactable::colDef( + header = withTooltip("# of Dechallenge Fails", + "Among people with challenge outcomes, the distinct number of people with outcomes during dechallengeEvaluationWindow"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), + dechallengeSuccess = reactable::colDef( + header = withTooltip("# of Dechallenge Successes", + "Among people with challenge outcomes, the distinct number of people without outcomes during the dechallengeEvaluationWindow"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), + rechallengeAttempt = reactable::colDef( + header = withTooltip("# of Rechallenge Attempts", + "Number of people with a new exposure era after the occurrence of an outcome during a prior exposure era"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), + rechallengeFail = reactable::colDef( + header = withTooltip("# of Rechallenge Fails", + "Number of people with a new exposure era during which an outcome occurred, after the occurrence of an outcome during a prior exposure era"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), + rechallengeSuccess = reactable::colDef( + header = withTooltip("# of Rechallenge Successes", + "Number of people with a new exposure era during which an outcome did not occur, after the occurrence of an outcome during a prior exposure era"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else abs(value) + } + ), pctDechallengeAttempt = reactable::colDef( + header = withTooltip("% of Dechallenge Attempts", + "Percent of people with observable time after discontinuation of the exposure era during which the challenge outcome occurred"), + filterable = T, + #format = reactable::colFormat(digits = 2, percent = T), format = reactable::colFormat(digits = 2, percent = T) ), pctDechallengeSuccess = reactable::colDef( + header = withTooltip("% of Dechallenge Success", + "Among people with challenge outcomes, the percent of people with outcomes during dechallengeEvaluationWindow"), + filterable = T, format = reactable::colFormat(digits = 2, percent = T) ), pctDechallengeFail = reactable::colDef( + header = withTooltip("% of Dechallenge Fail", + "Among people with challenge outcomes, the percent of people without outcomes during the dechallengeEvaluationWindow"), + filterable = T, format = reactable::colFormat(digits = 2, percent = T) ), pctRechallengeAttempt = reactable::colDef( + header = withTooltip("% of Rechallenge Attempts", + "Percent of people with a new exposure era after the occurrence of an outcome during a prior exposure era"), + filterable = T, format = reactable::colFormat(digits = 2, percent = T) ), pctRechallengeSuccess = reactable::colDef( + header = withTooltip("% of Rechallenge Success", + "Percent of people with a new exposure era during which an outcome occurred, after the occurrence of an outcome during a prior exposure era"), + filterable = T, format = reactable::colFormat(digits = 2, percent = T) ), pctRechallengeFail = reactable::colDef( + header = withTooltip("% of Rechallenge Fail", + "Percent of people with a new exposure era during which an outcome did not occur, after the occurrence of an outcome during a prior exposure era"), + filterable = T, format = reactable::colFormat(digits = 2, percent = T) ) - ), + ) + return(result) + } + + tableOutputs <- resultTableServer( + id = "tableResults", + df = allData, + details = data.frame( + target = options()$targetName, + outcome = options()$outcomeName, + Analysis = 'Exposed Cases Summary - Dechallenge-Rechallenge' + ), + downloadedFileName = 'dechallege-rechallenge', + colDefsInput = characteriationDechalRechalColDefs(), addActions = c('fails') ) @@ -183,8 +273,8 @@ characterizationDechallengeRechallengeServer <- function( if(!is.null(tableOutputs$actionType())){ if(tableOutputs$actionType() == 'fails'){ result <- getDechalRechalFailData( - targetId = inputSelected()$targetId, - outcomeId = inputSelected()$outcomeId, + targetId = targetId(), + outcomeId = outcomeId(), databaseId = allData()$databaseId[tableOutputs$actionIndex()$index], # update? dechallengeStopInterval = allData()$dechallengeStopInterval[tableOutputs$actionIndex()$index], dechallengeEvaluationWindow = allData()$dechallengeEvaluationWindow[tableOutputs$actionIndex()$index], @@ -205,7 +295,7 @@ characterizationDechallengeRechallengeServer <- function( ) ) } else{ - showNotification("No fails to display") + shiny::showNotification("No fails to display") } } } @@ -227,6 +317,7 @@ characterizationDechallengeRechallengeServer <- function( ) } +# can delete? dechalRechalGetIds <- function( connectionHandler, resultDatabaseSettings @@ -376,6 +467,33 @@ getDechalRechalFailData <- function( } +isCohortUniquePeople <- function( + connectionHandler, + resultDatabaseSettings, + cohortId +) { + + sql <- "SELECT + cc.database_id, cc.cohort_id, cc.cohort_entries, cc.cohort_subjects + FROM @schema.@cg_table_prefixCOHORT_COUNT cc + where cc.cohort_id = @cohort_id + ;" + res <- tryCatch({connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix, + cohort_id = cohortId + )}, error = function(e){return(NULL)} + ) + + # if table is missing the warning will not happen + if(is.null(res)){ + return(T) + }else{ + return(sum(res$cohortEntries == res$cohortSubjects) == nrow(res)) + } +} + plotDechalRechal <- function( dechalRechalData, i = 1 diff --git a/R/characterization-incidence.R b/R/characterization-incidence.R index 8987653a..60a2d20b 100644 --- a/R/characterization-incidence.R +++ b/R/characterization-incidence.R @@ -38,43 +38,6 @@ as_ggplot <- function(x){ } -# is_null_unit <- function (x) -# { -# if (!grid::is.unit(x)) { -# return(FALSE) -# } -# all(grid::unitType(x) == "null") -# } -# -# force_panelsizes <- function(rows = NULL, cols = NULL, respect = NULL, total_width = NULL, total_height = NULL) { -# if (!is.null(rows) & !grid::is.unit(rows)) { -# rows <- grid::unit(rows, "null") -# } -# if (!is.null(cols) & !grid::is.unit(cols)) { -# cols <- grid::unit(cols, "null") -# } -# if (!is.null(total_width)) { -# if (grid::is.unit(cols) && !is_null_unit(cols)) { -# stop("Cannot set {.arg total_width} when {.arg cols} is not relative.") -# } -# if (!grid::is.unit(total_width)) { -# stop("{.arg total_width} must be a {.cls unit} object.") -# } -# rlang::arg_match0(grid::unitType(total_width), c("cm", "mm", "inches", "points")) -# } -# if (!is.null(total_height)) { -# if (grid::is.unit(rows) && !is_null_unit(rows)) { -# stop("Cannot set {.arg total_height} when {.arg rows} is not relative.") -# } -# if (!grid::is.unit(total_height)) { -# stop("{.arg total_height} must be a {.cls unit} object.") -# } -# rlang::arg_match0(grid::unitType(total_height), c("cm", "mm", "inches", "points")) -# } -# structure(list(rows = rows, cols = cols, respect = respect, total_width = total_width, total_height = total_height), class = "forcedsize") -# } - - # Define the custom age sorting function custom_age_sort <- function(age_categories) { # Extract the largest integer from each category @@ -87,29 +50,19 @@ custom_age_sort <- function(age_categories) { return(custom_order) } -base_breaks <- function(n = 10){ - function(x) { - axisTicks(log10(range(x, na.rm = TRUE)), log = TRUE, n = n) - } -} - break_setter = function(n = 5) { function(lims) {pretty(x = as.numeric(lims), n = n)} } - - - - #' The module viewer for exploring incidence results #' #' @details #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {Characterization} #' @return #' The user interface to the description incidence module #' @@ -117,19 +70,12 @@ break_setter = function(n = 5) { characterizationIncidenceViewer <- function(id) { ns <- shiny::NS(id) shiny::div( - - infoHelperViewer( - id = "helper", - helpLocation= system.file("characterization-www", "help-incidenceRate.html", package = utils::packageName()) - ), - inputSelectionViewer( - id = ns("input-selection-results") - ), + shiny::uiOutput(ns("inputOptions")), shiny::conditionalPanel( condition = 'input.generate != 0', - ns = shiny::NS(ns("input-selection-results")), + ns = ns, shiny::tabsetPanel( type = 'pills', @@ -284,7 +230,11 @@ characterizationIncidenceViewer <- function(id) { #' @param id the unique reference id for the module #' @param connectionHandler the connection to the prediction result database #' @param resultDatabaseSettings a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix -#' +#' @param parents a list of parent cohorts +#' @param parentIndex an integer specifying the parent index of interest +#' @param outcomes a reactive object specifying the outcomes of interest +#' @param targetIds a reactive vector of integer specifying the targetIds of interest +#' @family {Characterization} #' @return #' The server to the prediction incidence module #' @@ -292,494 +242,442 @@ characterizationIncidenceViewer <- function(id) { characterizationIncidenceServer <- function( id, connectionHandler, - resultDatabaseSettings + resultDatabaseSettings, + #options, # this gets overwritten in code below - why here? + parents, + parentIndex, # reactive + outcomes, # reactive + targetIds # reactive ) { shiny::moduleServer( id, function(input, output, session) { - ## ns <- session$ns - - options <- getIncidenceOptions( # written using getTargetOutcomes - connectionHandler, - resultDatabaseSettings - ) - - sortedAges <- custom_age_sort(options$ageGroupName) - - # Extract the integers from each TAR string - tarIntegers <- as.integer(gsub("[^0-9]", "", options$tar)) - # Sort the vector based on the extracted integers - sortedTars <- options$tar[order(tarIntegers)] - - # input selection component - inputSelectedResults <- inputSelectionServer( - id = "input-selection-results", - inputSettingList = list( - createInputSetting( - rowNumber = 1, - columnWidth = 12, - varName = 'firsttext', - inputReturn = T, - uiFunction = 'shiny::div', - uiInputs = list( - "Select Your Results", - style = "font-weight: bold; font-size: 20px; text-align: center; margin-bottom: 20px;" - ) - ), - createInputSetting( - rowNumber = 2, - columnWidth = 6, - varName = 'targetIds', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'Target: ', - choices = options$targetIds, - selected = options$targetIds[1], #default should be just one (the first) - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - createInputSetting( - rowNumber = 2, - columnWidth = 6, - varName = 'outcomeIds', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'Outcome: ', - choices = options$outcomeIds, - selected = options$outcomeIds[1], #default should be just one (the first) - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - - # third row - createInputSetting( - rowNumber = 3, - columnWidth = 3, - varName = 'incidenceRateAgeFilter', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - collapse = T, - uiInputs = list( - label = 'Filter By Age Group: ', - choices = sortedAges, - selected = sortedAges, - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - - - createInputSetting( - rowNumber = 3, - columnWidth = 3, - varName = 'incidenceRateGenderFilter', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - collapse = T, - uiInputs = list( - label = 'Filter By Sex: ', - choices = sort(options$genderName, decreasing = F), - selected = options$genderName, - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - - createInputSetting( - rowNumber = 3, - columnWidth = 3, - varName = 'incidenceRateCalendarFilter', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - collapse = T, - uiInputs = list( - label = 'Filter By Start Year: ', - choices = sort(options$startYear, decreasing = T), - selected = options$startYear, - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - - createInputSetting( - rowNumber = 3, - columnWidth = 3, - varName = 'incidenceRateTarFilter', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'Select Time at risk (TAR)', - choices = sortedTars, - selected = sortedTars[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) + + ciOptions <- getIncidenceOptions(connectionHandler, resultDatabaseSettings) + + output$inputOptions <- shiny::renderUI({ + shinydashboard::box( + collapsible = TRUE, + title = "Options", + width = "100%", + + shiny::div( + "Select Your Results", + style = "font-weight: bold; font-size: 20px; text-align: center; margin-bottom: 20px;" + ), + + shiny::selectInput( + inputId = session$ns('outcomeIds'), + label = 'Outcome: ', + choices = outcomes(), + selected = 1, + multiple = T, + selectize = TRUE, + width = NULL, + size = NULL + ), + + shinyWidgets::pickerInput( + inputId = session$ns('databaseSelector'), + label = 'Filter By Database: ', + choices = ciOptions$databases, + selected = ciOptions$databases, + multiple = T, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 + ) + ), + + shinyWidgets::pickerInput( + inputId = session$ns('ageIds'), + label = 'Filter By Age Group: ', + choices = ciOptions$ageGroup, + selected = ciOptions$ageGroup, + multiple = T, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 + ) + ), + + shinyWidgets::pickerInput( + inputId = session$ns('sexIds'), + label = 'Filter By Sex: ', + choices = ciOptions$sex, + selected = ciOptions$sex, + multiple = T, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 + ) + ), + + shinyWidgets::pickerInput( + inputId = session$ns('startYears'), + label = 'Filter By Start Year: ', + choices = ciOptions$startYear, + selected = ciOptions$startYear, + multiple = T, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 ) + ), + + shinyWidgets::pickerInput( + inputId = session$ns('tars'), + label = 'Select Time at risk (TAR)', + choices = ciOptions$tar, + selected = ciOptions$tar[1], + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 + ) + ), + + shiny::actionButton( + inputId = session$ns('generate'), + label = 'Generate', + icon = shiny::icon('redo') ) ) - - # 4th row text - inputSelectedCustomPlot <- inputSelectionServer( - id = "input-selection-custom-plot", - inputSettingList = list( - createInputSetting( - rowNumber = 4, - columnWidth = 12, - varName = 'secondtext', - inputReturn = T, - uiFunction = 'shiny::div', - uiInputs = list( - "Configure Your Plot", - style = "font-weight: bold; font-size: 20px; text-align: center; margin-bottom: 20px; margin-top: 20px; " - ) - ), - - # plotting settings 5th row - - createInputSetting( - rowNumber = 5, - columnWidth = 3, - varName = 'plotYAxis', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'Y Axis (Numeric) ', - choices = options$irPlotNumericChoices, - selected = "incidenceRateP100py", - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) + }) + + outcomeIds <- shiny::reactiveVal(NULL) + incidenceRateTarFilter <- shiny::reactiveVal(NULL) + incidenceRateCalendarFilter <- shiny::reactiveVal(NULL) + incidenceRateAgeFilter <- shiny::reactiveVal(NULL) + incidenceRateGenderFilter <- shiny::reactiveVal(NULL) + incidenceRateDbFilter <- shiny::reactiveVal(NULL) + shiny::observeEvent(input$generate,{ + incidenceRateTarFilter(names(ciOptions$tar)[(ciOptions$tar == input$tars)]) # filter needs actual value + incidenceRateCalendarFilter(input$startYears) + incidenceRateAgeFilter(input$ageIds) + incidenceRateGenderFilter(input$sexIds) + incidenceRateDbFilter(input$databaseSelector) + outcomeIds(input$outcomeIds) + }) + + + inputSelectedCustomPlot <- inputSelectionServer( + id = "input-selection-custom-plot", + inputSettingList = list( + createInputSetting( + rowNumber = 4, + columnWidth = 12, + varName = 'secondtext', + inputReturn = T, + uiFunction = 'shiny::div', + uiInputs = list( + "Configure Your Plot", + style = "font-weight: bold; font-size: 20px; text-align: center; margin-bottom: 20px; margin-top: 20px; " + ) + ), + + # plotting settings 5th row + + createInputSetting( + rowNumber = 5, + columnWidth = 3, + varName = 'plotYAxis', + uiFunction = 'shinyWidgets::pickerInput', + updateFunction = 'shinyWidgets::updatePickerInput', + uiInputs = list( + label = 'Y Axis (Numeric) ', + choices = ciOptions$irPlotNumericChoices, + selected = "incidenceRateP100py", + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 ) - ), - - createInputSetting( - rowNumber = 5, - columnWidth = 3, - varName = 'plotXAxis', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'X Axis (Categorical) ', - choices = options$irPlotCategoricalChoices, - selected = "startYear", - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) + ) + ), + + createInputSetting( + rowNumber = 5, + columnWidth = 3, + varName = 'plotXAxis', + uiFunction = 'shinyWidgets::pickerInput', + updateFunction = 'shinyWidgets::updatePickerInput', + uiInputs = list( + label = 'X Axis (Categorical) ', + choices = ciOptions$irPlotCategoricalChoices, + selected = "startYear", + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 ) - ), - - createInputSetting( - rowNumber = 5, - columnWidth = 3, - varName = 'plotXTrellis', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'Row Trellis (Categorical) ', - choices = options$irPlotCategoricalChoices, - selected = "targetName", - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) + ) + ), + + createInputSetting( + rowNumber = 5, + columnWidth = 3, + varName = 'plotXTrellis', + uiFunction = 'shinyWidgets::pickerInput', + updateFunction = 'shinyWidgets::updatePickerInput', + uiInputs = list( + label = 'Row Trellis (Categorical) ', + choices = ciOptions$irPlotCategoricalChoices, + selected = "targetName", + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 ) - ), - - createInputSetting( - rowNumber = 5, - columnWidth = 3, - varName = 'plotYTrellis', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'Column Trellis (Categorical)', - choices = options$irPlotCategoricalChoices, - selected = "outcomeName", - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) + ) + ), + + createInputSetting( + rowNumber = 5, + columnWidth = 3, + varName = 'plotYTrellis', + uiFunction = 'shinyWidgets::pickerInput', + updateFunction = 'shinyWidgets::updatePickerInput', + uiInputs = list( + label = 'Column Trellis (Categorical)', + choices = ciOptions$irPlotCategoricalChoices, + selected = "outcomeName", + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 ) - ), - - # row 6 - - createInputSetting( - rowNumber = 6, - columnWidth = 3, - varName = 'plotColor', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'Color (Categorical)', - choices = options$irPlotCategoricalChoices, - selected = "cdmSourceAbbreviation", - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) + ) + ), + + # row 6 + + createInputSetting( + rowNumber = 6, + columnWidth = 3, + varName = 'plotColor', + uiFunction = 'shinyWidgets::pickerInput', + updateFunction = 'shinyWidgets::updatePickerInput', + uiInputs = list( + label = 'Color (Categorical)', + choices = ciOptions$irPlotCategoricalChoices, + selected = "cdmSourceAbbreviation", + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 ) - ), - createInputSetting( - rowNumber = 6, - columnWidth = 3, - varName = 'plotSize', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'Plot Point Size (Numeric)', - choices = options$irPlotNumericChoices, - selected = "outcomes", - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) + ) + ), + createInputSetting( + rowNumber = 6, + columnWidth = 3, + varName = 'plotSize', + uiFunction = 'shinyWidgets::pickerInput', + updateFunction = 'shinyWidgets::updatePickerInput', + uiInputs = list( + label = 'Plot Point Size (Numeric)', + choices = ciOptions$irPlotNumericChoices, + selected = "outcomes", + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 ) - ), - createInputSetting( - rowNumber = 6, - columnWidth = 3, - varName = 'plotShape', - uiFunction = 'shinyWidgets::pickerInput', - updateFunction = 'shinyWidgets::updatePickerInput', - uiInputs = list( - label = 'Plot Point Shape (Categorical)', - choices = options$irPlotCategoricalChoices, - selected = "genderName", - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - dropupAuto = TRUE, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) + ) + ), + createInputSetting( + rowNumber = 6, + columnWidth = 3, + varName = 'plotShape', + uiFunction = 'shinyWidgets::pickerInput', + updateFunction = 'shinyWidgets::updatePickerInput', + uiInputs = list( + label = 'Plot Point Shape (Categorical)', + choices = ciOptions$irPlotCategoricalChoices, + selected = "genderName", + multiple = F, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + dropupAuto = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 ) - ), - - createInputSetting( - rowNumber = 6, - columnWidth = 3, - varName = 'irYscaleFixed', - uiFunction = 'shiny::checkboxInput', - uiInputs = list( - label = "Use same y-axis scale across plots?" - ) ) - + ), - + createInputSetting( + rowNumber = 6, + columnWidth = 3, + varName = 'irYscaleFixed', + uiFunction = 'shiny::checkboxInput', + uiInputs = list( + label = "Use same y-axis scale across plots?" ) + ) ) + ) - filteredData <- shiny::reactive( - { - if (is.null(inputSelectedResults()$targetIds) | - is.null(inputSelectedResults()$outcomeIds) - ) { - return(data.frame()) - } - - else if(inputSelectedResults()$targetIds==inputSelectedResults()$outcomeIds && - length(inputSelectedResults()$targetIds)==1 && length(inputSelectedResults()$outcomeIds)==1 - ){ - shiny::validate("Target and outcome cohorts must differ from each other. Make a different selection.") - } - - else { - getIncidenceData(targetIds = inputSelectedResults()$targetIds, - outcomeIds = inputSelectedResults()$outcomeIds, - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) %>% + extractedData <- shiny::reactiveVal() + shiny::observeEvent(input$generate , + { + if (is.null(targetIds()) | + is.null(outcomeIds()) + ) { + shiny::validate("Please wait...") + } + + else if(targetIds()[1] == outcomeIds()[1] && + length(targetIds())==1 && length(outcomeIds())==1 + ){ + shiny::validate("Target and outcome cohorts must differ from each other. Make a different selection.") + } + + else { + result <- getIncidenceData(targetIds = targetIds(), + outcomeIds = outcomeIds(), + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings + ) + extractedData(result) + } + } + ) + + filteredData <- shiny::reactive({ + shiny::req(nrow(extractedData() > 0)) + if(nrow(extractedData()) > 0){ + extractedData() %>% dplyr::relocate("tar", .before = "outcomes") %>% dplyr::mutate(incidenceProportionP100p = as.numeric(.data$incidenceProportionP100p), incidenceRateP100py = as.numeric(.data$incidenceRateP100py), dplyr::across(dplyr::where(is.numeric), round, 4), - targetIdShort = paste("C", .data$targetCohortDefinitionId, sep = "-"), - outcomeIdShort = paste("C", .data$outcomeCohortDefinitionId, sep = "-")) %>% - dplyr::filter(.data$ageGroupName %in% !!inputSelectedResults()$incidenceRateAgeFilter & - .data$genderName %in% !!inputSelectedResults()$incidenceRateGenderFilter & - .data$startYear %in% !!inputSelectedResults()$incidenceRateCalendarFilter + targetNameShort = paste("C", .data$targetCohortDefinitionId, sep = "-"), + outcomeNameShort = paste("C", .data$outcomeCohortDefinitionId, sep = "-")) %>% + dplyr::filter(.data$ageGroupId %in% !! incidenceRateAgeFilter() & + .data$genderId %in% !! incidenceRateGenderFilter() & + .data$startYear %in% !! incidenceRateCalendarFilter() & + .data$tar %in% incidenceRateTarFilter() & + .data$cdmSourceAbbreviation %in% !! incidenceRateDbFilter() ) %>% - dplyr::relocate("targetIdShort", .after = "targetName") %>% - dplyr::relocate("outcomeIdShort", .after = "outcomeName") - } + dplyr::relocate("targetName", .after = "cdmSourceAbbreviation") %>% + dplyr::relocate("outcomeName", .after = "targetName") %>% + dplyr::relocate("targetNameShort", .after = "targetName") %>% + dplyr::relocate("outcomeNameShort", .after = "outcomeName") } - ) - - filteredDataAggregateForPlot <- shiny::reactive( - { - if (is.null(inputSelectedResults()$targetIds) | - is.null(inputSelectedResults()$outcomeIds) - ) { - return(data.frame()) - } - - else if(inputSelectedResults()$targetIds==inputSelectedResults()$outcomeIds && - length(inputSelectedResults()$targetIds)==1 && length(inputSelectedResults()$outcomeIds)==1 - ){ - shiny::validate("Target and outcome cohorts must differ from each other. Make a different selection.") - } - - else { - getIncidenceData(targetIds = inputSelectedResults()$targetIds, - outcomeIds = inputSelectedResults()$outcomeIds, - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) %>% - dplyr::relocate("tar", .before = "outcomes") %>% - dplyr::mutate(incidenceProportionP100p = as.numeric(.data$incidenceProportionP100p), - incidenceRateP100py = as.numeric(.data$incidenceRateP100py), - dplyr::across(dplyr::where(is.numeric), round, 4), - targetIdShort = paste("C", .data$targetCohortDefinitionId, sep = "-"), - outcomeIdShort = paste("C", .data$outcomeCohortDefinitionId, sep = "-")) %>% - dplyr::relocate("targetIdShort", .after = "targetName") %>% - dplyr::relocate("outcomeIdShort", .after = "outcomeName") + }) - } + filteredDataAggregateForPlot <- shiny::reactive({ + if(nrow(extractedData()) > 0){ + extractedData() %>% + dplyr::relocate("tar", .before = "outcomes") %>% + dplyr::mutate(incidenceProportionP100p = as.numeric(.data$incidenceProportionP100p), + incidenceRateP100py = as.numeric(.data$incidenceRateP100py), + dplyr::across(dplyr::where(is.numeric), round, 4), + targetNameShort = paste("C", .data$targetCohortDefinitionId, sep = "-"), + outcomeNameShort = paste("C", .data$outcomeCohortDefinitionId, sep = "-")) %>% + dplyr::relocate("targetNameShort", .after = "targetName") %>% + dplyr::relocate("outcomeNameShort", .after = "outcomeName") } - ) - - - - incidenceColList <- ParallelLogger::loadSettingsFromJson( - system.file("components-columnInformation", - "characterization-incidence-colDefs.json", - package = "OhdsiShinyModules" - ) - ) + }) - ## CHECK - caused error for me but it is in Nate's latest code - class(incidenceColList$genderName$filterMethod) <- "JS_EVAL" - - renderIrTable <- shiny::reactive( - { - filteredData() - } - ) + incidenceColList <- .createCiColDefList() resultTableServer( id = "incidenceRateTable", - df = renderIrTable, - selectedCols = c("cdmSourceAbbreviation", "targetName", "targetIdShort", "outcomeName", "outcomeIdShort", + df = filteredData, + selectedCols = c("cdmSourceAbbreviation", "targetName", "targetNameShort", "outcomeName", "outcomeNameShort", "ageGroupName", "genderName", "startYear", "tar", "outcomes", "incidenceProportionP100p", "incidenceRateP100py"), sortedCols = c("ageGroupName", "genderName", "startYear", "incidenceRateP100py"), elementId = "incidence-select", colDefsInput = incidenceColList, downloadedFileName = "incidenceRateTable-" - ) - + ) + '%!in%' <- function(x,y)!('%in%'(x,y)) - + #ir plots - irPlotCustom <- shiny::reactive( + irPlotCustom <- shiny::reactive( # observeEvent generate instead? { - if (is.null(inputSelectedResults()$targetIds) | - is.null(inputSelectedResults()$outcomeIds)) { + if (is.null(targetIds()) | + is.null(outcomeIds())) { return(data.frame()) } + if(nrow(filteredData()) == 0){ + return(FALSE) + } - ifelse(inputSelectedResults()$incidenceRateTarFilter %in% filteredData()$tar, - plotData <- filteredData() %>% - dplyr::filter(.data$tar %in% inputSelectedResults()$incidenceRateTarFilter), - shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") + ifelse(incidenceRateTarFilter() %in% filteredData()$tar, + plotData <- filteredData() %>% + dplyr::filter(.data$tar %in% incidenceRateTarFilter()) %>% + dplyr::mutate(targetLabel = paste(.data$targetNameShort, " = ", .data$targetName), + outcomeLabel = paste(.data$outcomeNameShort, " = ", .data$outcomeName) + ), + shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") ) - + # Take the specific tar value you want to plot tar_value <- unique(plotData$tar)[1] @@ -787,9 +685,9 @@ characterizationIncidenceServer <- function( plotData$tooltip <- with(plotData, paste( "Incidence Rate:", incidenceRateP100py, "
", "Incidence Proportion:", incidenceProportionP100p, "
", - "Outcome ID:", outcomeIdShort, "
", + "Outcome ID:", outcomeNameShort, "
", "Outcome Name:", outcomeName, "
", - "Target ID:", targetIdShort, "
", + "Target ID:", targetNameShort, "
", "Target Name:", targetName, "
", "Data Source:", cdmSourceAbbreviation, "
", "Calendar Year:", startYear, "
", @@ -801,7 +699,7 @@ characterizationIncidenceServer <- function( "Outcomes:", outcomes )) - + # Check if color, size, shape, and trellis variables are selected, and set aesthetics accordingly color_aesthetic <- NULL size_aesthetic <- NULL @@ -809,19 +707,29 @@ characterizationIncidenceServer <- function( trellis_aesthetic_x <- NULL trellis_aesthetic_y <- NULL + # Get unique target and outcome labels + unique_target_labels <- strwrap(unique(plotData$targetLabel), width = 300) + unique_outcome_labels <- strwrap(unique(plotData$outcomeLabel), width = 300) + + # Combine all unique values into a final vector + final_unique_values <- unique(c(unique_target_labels, unique_outcome_labels)) + + # Create the caption text with line breaks + caption_text <- paste(final_unique_values, collapse = "\n") + if (inputSelectedCustomPlot()$plotColor == "Target Cohort" | inputSelectedCustomPlot()$plotColor == "Outcome Cohort") { color_aesthetic <- if (inputSelectedCustomPlot()$plotColor == "Target Cohort") { - dplyr::vars(.data$targetIdShort) + dplyr::vars(.data$targetNameShort) } else if (inputSelectedCustomPlot()$plotColor == "Outcome Cohort") { - dplyr::vars(.data$outcomeIdShort) + dplyr::vars(.data$outcomeNameShort) } } if (inputSelectedCustomPlot()$plotShape == "Target Cohort" | inputSelectedCustomPlot()$plotShape == "Outcome Cohort") { shape_aesthetic <- if (inputSelectedCustomPlot()$plotShape == "Target Cohort") { - dplyr::vars(.data$targetIdShort) + dplyr::vars(.data$targetNameShort) } else if (inputSelectedCustomPlot()$plotShape == "Outcome Cohort") { - dplyr::vars(.data$outcomeIdShort) + dplyr::vars(.data$outcomeNameShort) } } @@ -868,7 +776,7 @@ characterizationIncidenceServer <- function( else if (inputSelectedCustomPlot()$plotXTrellis!="(None)" & inputSelectedCustomPlot()$plotXTrellis=="targetName" & inputSelectedCustomPlot()$plotXTrellis!="outcomeName" & inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis!="targetName" & inputSelectedCustomPlot()$plotYTrellis!="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( - rows = dplyr::vars(.data$targetIdShort), + rows = dplyr::vars(.data$targetNameShort), cols = dplyr::vars(.data[[inputSelectedCustomPlot()$plotYTrellis]]), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + @@ -879,7 +787,7 @@ characterizationIncidenceServer <- function( else if (inputSelectedCustomPlot()$plotXTrellis!="(None)" & inputSelectedCustomPlot()$plotXTrellis!="targetName" & inputSelectedCustomPlot()$plotXTrellis=="outcomeName" & inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis!="targetName" & inputSelectedCustomPlot()$plotYTrellis!="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( - rows = dplyr::vars(.data$outcomeIdShort), + rows = dplyr::vars(.data$outcomeNameShort), cols = dplyr::vars(.data[[inputSelectedCustomPlot()$plotYTrellis]]), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + @@ -891,7 +799,7 @@ characterizationIncidenceServer <- function( inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis=="targetName" & inputSelectedCustomPlot()$plotYTrellis!="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( rows = dplyr::vars(.data[[inputSelectedCustomPlot()$plotXTrellis]]), - cols = dplyr::vars(.data$targetIdShort), + cols = dplyr::vars(.data$targetNameShort), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + ggplot2::theme(strip.background = ggplot2::element_blank(), @@ -902,7 +810,7 @@ characterizationIncidenceServer <- function( inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis!="targetName" & inputSelectedCustomPlot()$plotYTrellis=="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( rows = dplyr::vars(.data[[inputSelectedCustomPlot()$plotXTrellis]]), - cols = dplyr::vars(.data$outcomeIdShort), + cols = dplyr::vars(.data$outcomeNameShort), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + ggplot2::theme(strip.background = ggplot2::element_blank(), @@ -912,8 +820,8 @@ characterizationIncidenceServer <- function( else if (inputSelectedCustomPlot()$plotXTrellis!="(None)" & inputSelectedCustomPlot()$plotXTrellis=="targetName" & inputSelectedCustomPlot()$plotXTrellis!="outcomeName" & inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis=="targetName" & inputSelectedCustomPlot()$plotYTrellis!="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( - rows = dplyr::vars(.data$targetIdShort), - cols = dplyr::vars(.data$targetIdShort), + rows = dplyr::vars(.data$targetNameShort), + cols = dplyr::vars(.data$targetNameShort), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + ggplot2::theme(strip.background = ggplot2::element_blank(), @@ -923,8 +831,8 @@ characterizationIncidenceServer <- function( else if (inputSelectedCustomPlot()$plotXTrellis!="(None)" & inputSelectedCustomPlot()$plotXTrellis=="targetName" & inputSelectedCustomPlot()$plotXTrellis!="outcomeName" & inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis!="targetName" & inputSelectedCustomPlot()$plotYTrellis=="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( - rows = dplyr::vars(.data$targetIdShort), - cols = dplyr::vars(.data$outcomeIdShort), + rows = dplyr::vars(.data$targetNameShort), + cols = dplyr::vars(.data$outcomeNameShort), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + ggplot2::theme(strip.background = ggplot2::element_blank(), @@ -934,8 +842,8 @@ characterizationIncidenceServer <- function( else if (inputSelectedCustomPlot()$plotXTrellis!="(None)" & inputSelectedCustomPlot()$plotXTrellis!="targetName" & inputSelectedCustomPlot()$plotXTrellis=="outcomeName" & inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis=="targetName" & inputSelectedCustomPlot()$plotYTrellis!="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( - rows = dplyr::vars(.data$outcomeIdShort), - cols = dplyr::vars(.data$targetIdShort), + rows = dplyr::vars(.data$outcomeNameShort), + cols = dplyr::vars(.data$targetNameShort), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + ggplot2::theme(strip.background = ggplot2::element_blank(), @@ -945,8 +853,8 @@ characterizationIncidenceServer <- function( else if (inputSelectedCustomPlot()$plotXTrellis!="(None)" & inputSelectedCustomPlot()$plotXTrellis!="targetName" & inputSelectedCustomPlot()$plotXTrellis=="outcomeName" & inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis!="targetName" & inputSelectedCustomPlot()$plotYTrellis=="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( - rows = dplyr::vars(.data$outcomeIdShort), - cols = dplyr::vars(.data$outcomeIdShort), + rows = dplyr::vars(.data$outcomeNameShort), + cols = dplyr::vars(.data$outcomeNameShort), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + ggplot2::theme(strip.background = ggplot2::element_blank(), @@ -957,7 +865,7 @@ characterizationIncidenceServer <- function( inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis!="targetName" & inputSelectedCustomPlot()$plotYTrellis=="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( rows = NULL, - cols = dplyr::vars(.data$outcomeIdShort), + cols = dplyr::vars(.data$outcomeNameShort), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + ggplot2::theme(strip.background = ggplot2::element_blank(), @@ -968,7 +876,7 @@ characterizationIncidenceServer <- function( inputSelectedCustomPlot()$plotYTrellis!="(None)" & inputSelectedCustomPlot()$plotYTrellis=="targetName" & inputSelectedCustomPlot()$plotYTrellis!="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( rows = NULL, - cols = dplyr::vars(.data$targetIdShort), + cols = dplyr::vars(.data$targetNameShort), scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + ggplot2::theme(strip.background = ggplot2::element_blank(), @@ -1000,7 +908,7 @@ characterizationIncidenceServer <- function( else if (inputSelectedCustomPlot()$plotXTrellis!="(None)" & inputSelectedCustomPlot()$plotXTrellis=="targetName" & inputSelectedCustomPlot()$plotXTrellis!="outcomeName" & inputSelectedCustomPlot()$plotYTrellis=="(None)" & inputSelectedCustomPlot()$plotYTrellis!="targetName" & inputSelectedCustomPlot()$plotYTrellis!="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( - rows = dplyr::vars(.data$targetIdShort), + rows = dplyr::vars(.data$targetNameShort), cols = NULL, scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + @@ -1011,7 +919,7 @@ characterizationIncidenceServer <- function( else if (inputSelectedCustomPlot()$plotXTrellis!="(None)" & inputSelectedCustomPlot()$plotXTrellis!="targetName" & inputSelectedCustomPlot()$plotXTrellis=="outcomeName" & inputSelectedCustomPlot()$plotYTrellis=="(None)" & inputSelectedCustomPlot()$plotYTrellis!="targetName" & inputSelectedCustomPlot()$plotYTrellis!="outcomeName") { base_plot <- base_plot + ggplot2::facet_grid( - rows = dplyr::vars(.data$outcomeIdShort), + rows = dplyr::vars(.data$outcomeNameShort), cols = NULL, scales = if (inputSelectedCustomPlot()$irYscaleFixed) "fixed" else "free_y" ) + @@ -1031,15 +939,17 @@ characterizationIncidenceServer <- function( } + + # Rest of your ggplot code remains the same base_plot <- base_plot + ggplot2::labs( title = paste("Incidence Rate for TAR:", tar_value), - x = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% inputSelectedCustomPlot()$plotXAxis]), - y = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% inputSelectedCustomPlot()$plotYAxis]), - color = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% inputSelectedCustomPlot()$plotColor]), - size = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% inputSelectedCustomPlot()$plotSize]), - shape = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% inputSelectedCustomPlot()$plotShape] - ) + x = names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% inputSelectedCustomPlot()$plotXAxis]), + y = names(ciOptions$irPlotNumericChoices[ciOptions$irPlotNumericChoices %in% inputSelectedCustomPlot()$plotYAxis]), + color = names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% inputSelectedCustomPlot()$plotColor]), + size = names(ciOptions$irPlotNumericChoices[ciOptions$irPlotNumericChoices %in% inputSelectedCustomPlot()$plotSize]), + shape = names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% inputSelectedCustomPlot()$plotShape]), + caption = caption_text ) + ggplot2::scale_y_log10(breaks = scales::breaks_log(n=6)) + ggplot2::guides(alpha = "none") + # Remove the alpha legend @@ -1054,45 +964,14 @@ characterizationIncidenceServer <- function( legend.box.spacing = ggplot2::unit(3, "pt"), legend.text = ggplot2::element_text(size=10), legend.title = ggplot2::element_text(size=16, face = "bold"), - #plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12), - #legend.spacing.x = ggplot2::unit(2.0, 'cm'), - # legend.box = "horizontal", - # legend.key.size = ggplot2::unit(3, 'points'), #change legend key size - # legend.title = ggplot2::element_text(size=30), #change legend title font size - # legend.text = ggplot2::element_text(size=20), panel.spacing = ggplot2::unit(2, "lines"), - # strip.background = ggplot2::element_blank(), - strip.text = ggplot2::element_text(face="bold", size = 14) + strip.text = ggplot2::element_text(face="bold", size = 14), + plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12, + margin = ggplot2::margin(t = 20)) ) + ggplot2::guides(shape = ggplot2::guide_legend(override.aes = list(size = 6)), color = ggplot2::guide_legend(override.aes = list(size = 6))) - # - # # Create a custom color scale - # color_scale <- RColorBrewer::colorRampPalette(brewer.pal(9, "YlOrRd"))(100) - # - # # Create a faceted heatmap by outcome and data source - # p <- ggplot2::ggplot(data = plotData, aes(x = targetIdShort, y = ageGroupName, - # text = paste("Outcome ID:", outcomeIdShort, "
Outcome:", outcomeName, - # "
Target ID:", targetIdShort, "
Target:", targetName, - # "
TAR:", tar, "
Age:", ageGroupName, "
Sex:", genderName, - # "
TAR:", - # "
Incidence Rate:", incidenceRateP100py))) + - # ggplot2::geom_tile(aes(fill = incidenceRateP100py), color = "white") + - # ggplot2::scale_fill_gradient(colors = color_scale, name = "Incidence Rate") + - # ggplot2::labs(title = "Incidence Rate by Strata Variables", - # x = "Target Population Cohort", - # y = "Age Category") + - # ggplot2::theme_minimal() + - # ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1), - # plot.title = element_text(hjust = 0.5)) + - # ggplot2::facet_grid(outcome ~ data_source, scales = "free_x", space = "free_x") - # - # # Convert the ggplot plot to a Plotly plot - # p <- plotly::ggplotly(p) - # - # - } @@ -1109,52 +988,55 @@ characterizationIncidenceServer <- function( #render the event reactive incidence plot without legend renderIrPlotCustomNoLegend <- shiny::reactive( { - if (is.null(inputSelectedResults()$targetIds) | - is.null(inputSelectedResults()$outcomeIds)) { + if (is.null(targetIds()) | + is.null(outcomeIds())) { shiny::validate("Please select at least one target and one outcome.") } + if(nrow(filteredData()) == 0){ + shiny::validate("No results.") + } else { - plotData <- filteredData() %>% - dplyr::filter(.data$tar %in% inputSelectedResults()$incidenceRateTarFilter) - - # Get the number of facets in both rows and columns - num_rows <- length(unique(plotData[[inputSelectedCustomPlot()$plotXTrellis]])) - num_cols <- length(unique(plotData[[inputSelectedCustomPlot()$plotYTrellis]])) - - max_length <- max(nchar(unique(inputSelectedCustomPlot()$plotXAxis))) - - base_plot <- irPlotCustom() - - p <- base_plot + - ggplot2::guides(shape = FALSE, color = FALSE, size = FALSE) - - # Convert the ggplot to a plotly object - p <- plotly::ggplotly(p, tooltip = "text") - - # Center the main plot title - p <- p %>% plotly::layout(title = list(x = 0.5, xanchor = "center"), - margin = list(t = 75, b = 150, l = 125, r = 25), - #add several xaxis placeholders in case row trellis has several distinct values (this is a workaround) - xaxis = list(tickangle = 45), - xaxis2 = list(tickangle = 45), - xaxis3 = list(tickangle = 45), - xaxis4 = list(tickangle = 45), - xaxis5 = list(tickangle = 45), - xaxis6 = list(tickangle = 45), - xaxis7 = list(tickangle = 45), - xaxis8 = list(tickangle = 45), - xaxis9 = list(tickangle = 45), - xaxis10 = list(tickangle = 45), - xaxis11 = list(tickangle = 45), - xaxis12 = list(tickangle = 45), - xaxis13 = list(tickangle = 45), - xaxis14 = list(tickangle = 45), - xaxis15 = list(tickangle = 45) - ) - - return(p) - + plotData <- filteredData() %>% + dplyr::filter(.data$tar %in% incidenceRateTarFilter()) + + # Get the number of facets in both rows and columns + num_rows <- length(unique(plotData[[inputSelectedCustomPlot()$plotXTrellis]])) + num_cols <- length(unique(plotData[[inputSelectedCustomPlot()$plotYTrellis]])) + + max_length <- max(nchar(unique(inputSelectedCustomPlot()$plotXAxis))) + + base_plot <- irPlotCustom() + + p <- base_plot + + ggplot2::guides(shape = FALSE, color = FALSE, size = FALSE) + + # Convert the ggplot to a plotly object + p <- plotly::ggplotly(p, tooltip = "text") + + # Center the main plot title + p <- p %>% plotly::layout(title = list(x = 0.5, xanchor = "center"), + margin = list(t = 75, b = 150, l = 125, r = 25), + #add several xaxis placeholders in case row trellis has several distinct values (this is a workaround) + xaxis = list(tickangle = 45), + xaxis2 = list(tickangle = 45), + xaxis3 = list(tickangle = 45), + xaxis4 = list(tickangle = 45), + xaxis5 = list(tickangle = 45), + xaxis6 = list(tickangle = 45), + xaxis7 = list(tickangle = 45), + xaxis8 = list(tickangle = 45), + xaxis9 = list(tickangle = 45), + xaxis10 = list(tickangle = 45), + xaxis11 = list(tickangle = 45), + xaxis12 = list(tickangle = 45), + xaxis13 = list(tickangle = 45), + xaxis14 = list(tickangle = 45), + xaxis15 = list(tickangle = 45) + ) + + return(p) + } } @@ -1163,14 +1045,17 @@ characterizationIncidenceServer <- function( #render the event reactive incidence plot without legend renderIrPlotCustom <- shiny::reactive( { - if (is.null(inputSelectedResults()$targetIds) | - is.null(inputSelectedResults()$outcomeIds)) { + if (is.null(targetIds()) | + is.null(outcomeIds())) { shiny::validate("Please select at least one target and one outcome.") } + if(nrow(filteredData()) == 0){ + shiny::validate("No results.") + } else { plotData <- filteredData() %>% - dplyr::filter(.data$tar %in% inputSelectedResults()$incidenceRateTarFilter) + dplyr::filter(.data$tar %in% incidenceRateTarFilter()) # Get the number of facets in both rows and columns num_rows <- length(unique(plotData[[inputSelectedCustomPlot()$plotXTrellis]])) @@ -1224,126 +1109,124 @@ characterizationIncidenceServer <- function( #by age - renderIrPlotStandardAge <- shiny::reactive( - { - - if (is.null(inputSelectedResults()$targetIds) | - is.null(inputSelectedResults()$outcomeIds)) { - return(data.frame()) - } - - ifelse(inputSelectedResults()$incidenceRateTarFilter %in% filteredData()$tar, - plotData <- filteredData() %>% - dplyr::filter(.data$tar %in% inputSelectedResults()$incidenceRateTarFilter), - shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") - ) - - #add check to make sure facetted plots fit nicely in plotting window (600px). this is currently nrow * ncol in facet_wrap() - ifelse(length(inputSelectedResults()$targetId) * length(inputSelectedResults()$outcomeId) <= 10, - plotData <- filteredData(), - shiny::validate("Too many Target-Outcome pairs selected to plot efficiently. Please choose fewer targets and/or outcomes.") + renderIrPlotStandardAge <- shiny::reactive({ + + + if (is.null(targetIds()) | + is.null(outcomeIds())) { + return(data.frame()) + } + if(nrow(filteredData()) == 0){ + shiny::validate("No results.") + } + + ifelse(incidenceRateTarFilter() %in% filteredData()$tar, + plotData <- filteredData() %>% + dplyr::filter(.data$tar %in% incidenceRateTarFilter()), + shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") + ) + + #add check to make sure facetted plots fit nicely in plotting window (600px). this is currently nrow * ncol in facet_wrap() + ifelse(length(targetIds()) * length(outcomeIds()) <= 10, + plotData <- filteredData(), + shiny::validate("Too many Target-Outcome pairs selected to plot efficiently. Please choose fewer targets and/or outcomes.") + ) + + #add check to make sure "> 1 distinct age is selected for by age plot"any" is in selection for year and sex + ifelse("Any" %in% incidenceRateCalendarFilter() & "Any" %in% incidenceRateGenderFilter(), + plotData <- filteredData(), + shiny::validate("This standard plot is designed to show results aggregated over all (`Any`) year and sex categories. Please make sure you have selected `Any` in the `Select your results` section above for these variables.") + ) + + plotData <- plotData %>% + dplyr::filter(#ageGroupName != "Any" & + .data$genderName == "Any" & + .data$startYear == "Any") %>% + dplyr::mutate(targetLabel = paste(.data$targetNameShort, " = ", .data$targetName), + outcomeLabel = paste(.data$outcomeNameShort, " = ", .data$outcomeName), + ageGroupName = factor(.data$ageGroupName, levels = custom_age_sort(.data$ageGroupName), ordered = TRUE) + ) %>% + dplyr::rename(Target = "targetNameShort", + Outcome = "outcomeNameShort", + Age = "ageGroupName") + + # Get unique target and outcome labels + unique_target_labels <- strwrap(unique(plotData$targetLabel), width = 300) + unique_outcome_labels <- strwrap(unique(plotData$outcomeLabel), width = 300) + + # Combine all unique values into a final vector + final_unique_values <- unique(c(unique_target_labels, unique_outcome_labels)) + + # Create the caption text with line breaks + caption_text <- paste(final_unique_values, collapse = "\n") + + + # Take the specific tar value you want to plot + tar_value <- unique(plotData$tar)[1] + + + base_plot <- ggplot2::ggplot( + data = plotData, + ggplot2::aes(x = .data$Age, + y = .data$incidenceRateP100py, + color = .data$cdmSourceAbbreviation ) - - plotData <- plotData %>% - dplyr::filter(ageGroupName != "Any" & - genderName == "Any" & - startYear == "Any") %>% - dplyr::mutate(targetLabel = paste(targetIdShort, " = ", targetName), - outcomeLabel = paste(outcomeIdShort, " = ", outcomeName), - ageGroupName = factor(ageGroupName, levels = custom_age_sort(ageGroupName), ordered = TRUE) - ) %>% - dplyr::rename("Target" = targetIdShort, - "Outcome" = outcomeIdShort, - "Age" = ageGroupName) - - # plotHeightStandardAgeSex <- shiny::reactive({ - # paste(sum(length(unique(plotData$targetLabel)), length(unique(plotData$Age)), -3)*100, "px", sep="") - # }) - - # Get unique target and outcome labels - unique_target_labels <- strwrap(unique(plotData$targetLabel), width = 300) - unique_outcome_labels <- strwrap(unique(plotData$outcomeLabel), width = 300) - - # Combine all unique values into a final vector - final_unique_values <- unique(c(unique_target_labels, unique_outcome_labels)) - - # Create the caption text with line breaks - caption_text <- paste(final_unique_values, collapse = "\n") - - - # Take the specific tar value you want to plot - tar_value <- unique(plotData$tar)[1] - - - base_plot <- ggplot2::ggplot( - data = plotData, - ggplot2::aes(x = Age, - y = incidenceRateP100py, - color = cdmSourceAbbreviation - ) + ) + + ggplot2::geom_point( + ggplot2::aes(size = 3) ) + - ggplot2::geom_point( - ggplot2::aes(size = 3) - ) + - #geom_jitter() + - #scale_size_continuous(range = c(5,15)) + - ggplot2::scale_colour_brewer(palette = "Dark2") + - ggplot2::facet_wrap( - Target~Outcome, - labeller = "label_both", - scales = "free_x", - nrow = 2, - ncol = 5 - #, - #strip.position = "right" - ) + - ggplot2::scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), - n.breaks = 4) - - base_plot <- base_plot + ggplot2::labs( - title = paste("Incidence Rate for Time at Risk:", tar_value), - x = paste(names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "ageGroupName"]), "\n"), - y = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "incidenceRateP100py"]), - color = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "cdmSourceAbbreviation"]), - #size = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "outcomes"]), - #shape = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "genderName"]), - caption = caption_text - ) + - ggplot2::guides(alpha = "none", size = "none") + # Remove the alpha and size legend - ggplot2::theme_bw() + - ggplot2::theme( - plot.title = ggplot2::element_text(margin = ggplot2::margin(b = 10), hjust = 0.5, size = 25, face="bold"), - plot.subtitle = ggplot2::element_text(margin = ggplot2::margin(b = 20), hjust = 0.5, size = 16), - axis.title.x = ggplot2::element_text(margin = ggplot2::margin(t = 25), size = 18), - axis.title.y = ggplot2::element_text(margin = ggplot2::margin(r = 25), size = 18), - axis.text.x = ggplot2::element_text(size = 14, angle = 45, hjust = 0.5, vjust = 0.25), - axis.text.y = ggplot2::element_text(size = 14), - legend.position = "bottom", - legend.box.spacing = ggplot2::unit(3, "pt"), - legend.text = ggplot2::element_text(size=10), - legend.title = ggplot2::element_text(size=16, face = "bold"), - legend.title.align = 0.5, - plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12, - margin = ggplot2::margin(t = 20)), - #legend.spacing.x = ggplot2::unit(2.0, 'cm'), - # legend.box = "horizontal", - # legend.key.size = ggplot2::unit(3, 'points'), #change legend key size - # legend.title = ggplot2::element_text(size=30), #change legend title font size - # legend.text = ggplot2::element_text(size=20), - panel.spacing = ggplot2::unit(2, "lines"), - # strip.background = ggplot2::element_blank(), - strip.text = ggplot2::element_text(face="bold", size = 14), - strip.background = ggplot2::element_blank(), - strip.clip = "off" - ) + - ggplot2::guides(#shape = ggplot2::guide_legend(override.aes = list(size = 6)), - color = ggplot2::guide_legend(override.aes = list(size = 6)) - ) - - return(base_plot) - - } - ) + #geom_jitter() + + #scale_size_continuous(range = c(5,15)) + + ggplot2::scale_colour_brewer(palette = "Paired") + + ggplot2::facet_wrap( + Target~Outcome, + labeller = "label_both", + scales = "free_x", + nrow = 2, + ncol = 5 + #, + #strip.position = "right" + ) + + ggplot2::scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), + n.breaks = 4) + + base_plot <- base_plot + ggplot2::labs( + title = paste("Incidence Rate for Time at Risk:", tar_value), + x = paste(names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "ageGroupName"]), "\n"), + y = names(ciOptions$irPlotNumericChoices[ciOptions$irPlotNumericChoices %in% "incidenceRateP100py"]), + color = names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "cdmSourceAbbreviation"]), + #size = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "outcomes"]), + #shape = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "genderName"]), + caption = caption_text + ) + + ggplot2::guides(alpha = "none", size = "none") + # Remove the alpha and size legend + ggplot2::theme_bw() + + ggplot2::theme( + plot.title = ggplot2::element_text(margin = ggplot2::margin(b = 10), hjust = 0.5, size = 25, face="bold"), + plot.subtitle = ggplot2::element_text(margin = ggplot2::margin(b = 20), hjust = 0.5, size = 16), + axis.title.x = ggplot2::element_text(margin = ggplot2::margin(t = 25), size = 18), + axis.title.y = ggplot2::element_text(margin = ggplot2::margin(r = 25), size = 18), + axis.text.x = ggplot2::element_text(size = 14, angle = 45, hjust = 0.5, vjust = 0.25), + axis.text.y = ggplot2::element_text(size = 14), + legend.position = "bottom", + legend.box.spacing = ggplot2::unit(3, "pt"), + legend.text = ggplot2::element_text(size=10), + legend.title = ggplot2::element_text(size=16, face = "bold"), + legend.title.align = 0.5, + plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12, + margin = ggplot2::margin(t = 20)), + panel.spacing = ggplot2::unit(2, "lines"), + strip.text = ggplot2::element_text(face="bold", size = 14), + strip.background = ggplot2::element_blank(), + strip.clip = "off" + ) + + ggplot2::guides(#shape = ggplot2::guide_legend(override.aes = list(size = 6)), + color = ggplot2::guide_legend(override.aes = list(size = 6)) + ) + + return(base_plot) + + }) output$incidencePlotStandardAge<- shiny::renderPlot({ @@ -1360,606 +1243,568 @@ characterizationIncidenceServer <- function( cowplot::save_plot(file, plot = renderIrPlotStandardAge(), base_height = 12) } ) + + #by age and sex + renderIrPlotStandardAgeSex <- shiny::reactive({ + + if (is.null(targetIds()) | + is.null(outcomeIds())) { + return(data.frame()) + } + if(nrow(filteredData()) == 0){ + return(data.frame()) + } + + ifelse(incidenceRateTarFilter() %in% filteredData()$tar, + plotData <- filteredData() %>% + dplyr::filter(.data$tar %in% incidenceRateTarFilter()), + shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") + ) + + #add check to make sure facetted plots fit nicely in plotting window (600px). this is currently nrow * ncol in facet_wrap() + ifelse(length(targetIds()) * length(outcomeIds()) <= 10, + plotData <- filteredData(), + shiny::validate("Too many Target-Outcome pairs selected to plot efficiently. Please choose fewer targets and/or outcomes.") + ) + + #add check to make sure "Any" is in the year filter + ifelse("Any" %in% incidenceRateCalendarFilter(), + plotData <- filteredData(), + shiny::validate("This standard plot is designed to show results aggregated over all (`Any`) year categories. Please make sure you have selected `Any` in the `Select your results` section above for this variable.") + ) + + #add check to make sure males and females are included + ifelse(8507 %in% incidenceRateGenderFilter() & 8532 %in% incidenceRateGenderFilter(), + plotData <- filteredData(), + shiny::validate("This standard plot is designed to show results stratified by male and female biological sex. Please make sure you have both `Male` and `Female` selected above and try again.") + ) + + plotData <- plotData %>% + dplyr::filter( #ageGroupName != "Any" & + .data$genderName != "Any" & + .data$startYear == "Any") %>% + dplyr::mutate(targetLabel = paste(.data$targetNameShort, " = ", .data$targetName), + outcomeLabel = paste(.data$outcomeNameShort, " = ", .data$outcomeName), + ageGroupName = factor(.data$ageGroupName, levels = custom_age_sort(.data$ageGroupName), ordered = TRUE) + ) %>% + dplyr::rename(Target = "targetNameShort", + Outcome = "outcomeNameShort", + Age = "ageGroupName") + + # Get unique target and outcome labels + unique_target_labels <- strwrap(unique(plotData$targetLabel), width = 300) + unique_outcome_labels <- strwrap(unique(plotData$outcomeLabel), width = 300) + + # Combine all unique values into a final vector + final_unique_values <- unique(c(unique_target_labels, unique_outcome_labels)) + + # Create the caption text with line breaks + caption_text <- paste(final_unique_values, collapse = "\n") + + + # Take the specific tar value you want to plot + tar_value <- unique(plotData$tar)[1] + + base_plot <- ggplot2::ggplot( + data = plotData, + ggplot2::aes(x = .data$Age, + y = .data$incidenceRateP100py, + shape = .data$genderName, + color = .data$cdmSourceAbbreviation + ) + ) + + ggplot2::geom_point( + ggplot2::aes(size = 3) + ) + + #geom_jitter() + + #scale_size_continuous(range = c(5,15)) + + ggplot2::scale_colour_brewer(palette = "Paired") + + ggplot2::facet_wrap( + Target~Outcome, + labeller = "label_both", + scales = "free_x", + nrow = 2, + ncol = 5 + #, + #strip.position = "right" + ) + + ggplot2::scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), + n.breaks = 4) + + base_plot <- base_plot + ggplot2::labs( + title = paste("Incidence Rate for Time at Risk:", tar_value), + x = paste(names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "ageGroupName"]), "\n"), + y = names(ciOptions$irPlotNumericChoices[ciOptions$irPlotNumericChoices %in% "incidenceRateP100py"]), + color = names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "cdmSourceAbbreviation"]), + #size = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "outcomes"]), + shape = names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "genderName"]), + caption = caption_text + ) + + ggplot2::guides(alpha = "none", size = "none") + # Remove the alpha and size legend + ggplot2::theme_bw() + + ggplot2::theme( + plot.title = ggplot2::element_text(margin = ggplot2::margin(b = 10), hjust = 0.5, size = 25, face="bold"), + plot.subtitle = ggplot2::element_text(margin = ggplot2::margin(b = 20), hjust = 0.5, size = 16), + axis.title.x = ggplot2::element_text(margin = ggplot2::margin(t = 25), size = 18), + axis.title.y = ggplot2::element_text(margin = ggplot2::margin(r = 25), size = 18), + axis.text.x = ggplot2::element_text(size = 14, angle = 45, hjust = 0.5, vjust = 0.25), + axis.text.y = ggplot2::element_text(size = 14), + legend.position = "bottom", + legend.box.spacing = ggplot2::unit(3, "pt"), + legend.text = ggplot2::element_text(size=10), + legend.title = ggplot2::element_text(size=16, face = "bold"), + legend.title.align = 0.5, + plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12, + margin = ggplot2::margin(t = 20)), + panel.spacing = ggplot2::unit(2, "lines"), + strip.text = ggplot2::element_text(face="bold", size = 14), + strip.background = ggplot2::element_blank(), + strip.clip = "off" + ) + + ggplot2::guides(shape = ggplot2::guide_legend(override.aes = list(size = 6)), + color = ggplot2::guide_legend(override.aes = list(size = 6)) + ) + + return(base_plot) + + }) - - - #by age and sex - -renderIrPlotStandardAgeSex <- shiny::reactive( - { - - if (is.null(inputSelectedResults()$targetIds) | - is.null(inputSelectedResults()$outcomeIds)) { - return(data.frame()) - } - - ifelse(inputSelectedResults()$incidenceRateTarFilter %in% filteredData()$tar, - plotData <- filteredData() %>% - dplyr::filter(.data$tar %in% inputSelectedResults()$incidenceRateTarFilter), - shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") - ) - - #add check to make sure facetted plots fit nicely in plotting window (600px). this is currently nrow * ncol in facet_wrap() - ifelse(length(inputSelectedResults()$targetId) * length(inputSelectedResults()$outcomeId) <= 10, - plotData <- filteredData(), - shiny::validate("Too many Target-Outcome pairs selected to plot efficiently. Please choose fewer targets and/or outcomes.") - ) - - plotData <- plotData %>% - dplyr::filter(ageGroupName != "Any" & - genderName != "Any" & - startYear == "Any") %>% - dplyr::mutate(targetLabel = paste(targetIdShort, " = ", targetName), - outcomeLabel = paste(outcomeIdShort, " = ", outcomeName), - ageGroupName = factor(ageGroupName, levels = custom_age_sort(ageGroupName), ordered = TRUE) - ) %>% - dplyr::rename("Target" = targetIdShort, - "Outcome" = outcomeIdShort, - "Age" = ageGroupName) - - # plotHeightStandardAgeSex <- shiny::reactive({ - # paste(sum(length(unique(plotData$targetLabel)), length(unique(plotData$Age)), -3)*100, "px", sep="") - # }) - - # Get unique target and outcome labels - unique_target_labels <- strwrap(unique(plotData$targetLabel), width = 300) - unique_outcome_labels <- strwrap(unique(plotData$outcomeLabel), width = 300) - - # Combine all unique values into a final vector - final_unique_values <- unique(c(unique_target_labels, unique_outcome_labels)) - - # Create the caption text with line breaks - caption_text <- paste(final_unique_values, collapse = "\n") - - - # Take the specific tar value you want to plot - tar_value <- unique(plotData$tar)[1] - - base_plot <- ggplot2::ggplot( - data = plotData, - ggplot2::aes(x = Age, - y = incidenceRateP100py, - shape = genderName, - color = cdmSourceAbbreviation - ) - ) + - ggplot2::geom_point( - ggplot2::aes(size = 3) - ) + - #geom_jitter() + - #scale_size_continuous(range = c(5,15)) + - ggplot2::scale_colour_brewer(palette = "Dark2") + - ggplot2::facet_wrap( - Target~Outcome, - labeller = "label_both", - scales = "free_x", - nrow = 2, - ncol = 5 - #, - #strip.position = "right" - ) + - ggplot2::scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), - n.breaks = 4) - - base_plot <- base_plot + ggplot2::labs( - title = paste("Incidence Rate for Time at Risk:", tar_value), - x = paste(names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "ageGroupName"]), "\n"), - y = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "incidenceRateP100py"]), - color = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "cdmSourceAbbreviation"]), - #size = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "outcomes"]), - shape = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "genderName"]), - caption = caption_text - ) + - ggplot2::guides(alpha = "none", size = "none") + # Remove the alpha and size legend - ggplot2::theme_bw() + - ggplot2::theme( - plot.title = ggplot2::element_text(margin = ggplot2::margin(b = 10), hjust = 0.5, size = 25, face="bold"), - plot.subtitle = ggplot2::element_text(margin = ggplot2::margin(b = 20), hjust = 0.5, size = 16), - axis.title.x = ggplot2::element_text(margin = ggplot2::margin(t = 25), size = 18), - axis.title.y = ggplot2::element_text(margin = ggplot2::margin(r = 25), size = 18), - axis.text.x = ggplot2::element_text(size = 14, angle = 45, hjust = 0.5, vjust = 0.25), - axis.text.y = ggplot2::element_text(size = 14), - legend.position = "bottom", - legend.box.spacing = ggplot2::unit(3, "pt"), - legend.text = ggplot2::element_text(size=10), - legend.title = ggplot2::element_text(size=16, face = "bold"), - legend.title.align = 0.5, - plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12, - margin = ggplot2::margin(t = 20)), - #legend.spacing.x = ggplot2::unit(2.0, 'cm'), - # legend.box = "horizontal", - # legend.key.size = ggplot2::unit(3, 'points'), #change legend key size - # legend.title = ggplot2::element_text(size=30), #change legend title font size - # legend.text = ggplot2::element_text(size=20), - panel.spacing = ggplot2::unit(2, "lines"), - # strip.background = ggplot2::element_blank(), - strip.text = ggplot2::element_text(face="bold", size = 14), - strip.background = ggplot2::element_blank(), - strip.clip = "off" - ) + - ggplot2::guides(shape = ggplot2::guide_legend(override.aes = list(size = 6)), - color = ggplot2::guide_legend(override.aes = list(size = 6)) - ) - - return(base_plot) - - } -) + output$incidencePlotStandardAgeSex<- + shiny::renderPlot({ + renderIrPlotStandardAgeSex() + }) -output$incidencePlotStandardAgeSex<- - shiny::renderPlot({ - renderIrPlotStandardAgeSex() - }) - - -# Define a function to save the plot as an image -output$downloadPlotStandardAgeSex <- shiny::downloadHandler( - filename = function() { - paste("standard-age-sex-ir-plot-", Sys.Date(), ".png", sep = "") - }, - content = function(file) { - cowplot::save_plot(file, plot = renderIrPlotStandardAgeSex(), base_height = 12) - } -) - - - -# by calendar year -renderIrPlotStandardYear <- shiny::reactive( - { - - if (is.null(inputSelectedResults()$targetIds) | - is.null(inputSelectedResults()$outcomeIds)) { - return(data.frame()) - } - - ifelse(inputSelectedResults()$incidenceRateTarFilter %in% filteredData()$tar, - plotData <- filteredData() %>% - dplyr::filter(.data$tar %in% inputSelectedResults()$incidenceRateTarFilter), - shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") - ) - - ifelse(length(inputSelectedResults()$incidenceRateTarFilter %in% filteredData()$tar) == 1, - plotData <- filteredData() %>% - dplyr::filter(.data$tar %in% inputSelectedResults()$incidenceRateTarFilter), - shiny::validate("Please select only one TAR at a time to view yearly plots.") - ) - - ifelse((length(inputSelectedResults()$targetIds) == 1) & - (length(inputSelectedResults()$outcomeIds) == 1), - plotData <- plotData, - shiny::validate("Please select only one Target and Outcome at a time to view yearly plots.") - ) - - - - plotData <- plotData %>% - dplyr::filter(genderName != "Any" & - startYear != "Any") %>% - dplyr::mutate(targetLabel = paste(targetIdShort, " = ", targetName), - outcomeLabel = paste(outcomeIdShort, " = ", outcomeName), - ageGroupName = factor(ageGroupName, levels = custom_age_sort(ageGroupName), ordered = TRUE) - ) %>% - dplyr::rename("Target" = targetIdShort, - "Outcome" = outcomeIdShort, - "Age" = ageGroupName) - - #get unique shorthand cohort name - unique_target <- unique(plotData$Target) - unique_outcome <- unique(plotData$Outcome) - - - # Get unique target and outcome labels - unique_target_labels <- strwrap(unique(plotData$targetLabel), width = 300) - unique_outcome_labels <- strwrap(unique(plotData$outcomeLabel), width = 300) - - # Combine all unique values into a final vector - final_unique_values <- unique(c(unique_target_labels, unique_outcome_labels)) - - # Create the caption text with line breaks - caption_text <- paste(final_unique_values, collapse = "\n") - - - # Take the specific tar value you want to plot - tar_value <- unique(plotData$tar)[1] - - base_plot <- ggplot2::ggplot( - data = plotData, - ggplot2::aes(x = startYear, - y = incidenceRateP100py, - shape = genderName, - color = cdmSourceAbbreviation, - group = interaction(cdmSourceAbbreviation, genderName) + + # Define a function to save the plot as an image + output$downloadPlotStandardAgeSex <- shiny::downloadHandler( + filename = function() { + paste("standard-age-sex-ir-plot-", Sys.Date(), ".png", sep = "") + }, + content = function(file) { + cowplot::save_plot(file, plot = renderIrPlotStandardAgeSex(), base_height = 12) + } ) - ) + - ggplot2::geom_point( - ggplot2::aes(size = 2.5) - ) + - ggplot2::geom_line(ggplot2::aes(linetype = genderName)) + - ggplot2::scale_colour_brewer(palette = "Dark2") + - #geom_jitter() + - #scale_size_continuous(range = c(5,15)) + - # ggplot2::scale_colour_brewer(palette = "Dark2") + - # ggplot2::facet_grid( - # rows = dplyr::vars(Outcome), - # cols = dplyr::vars(Age), - # labeller = ggplot2::labeller(.rows = outcomeLabeller, - # .cols = ageLabeller), - # scales = "free_y" - # ) + - ggplot2::facet_wrap( - ~Age, - labeller = "label_both", - scales = "free_x", - nrow = 2 - ) + - # scale_y_continuous(#breaks = base_breaks(), - # trans = 'log10') - ggplot2::scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), - n.breaks = 3) - - base_plot <- base_plot + ggplot2::labs( - title = paste("Incidence Rate for Time at Risk:", tar_value), - subtitle = paste("Target = ", unique_target, "; Outcome = ", unique_outcome, sep = ""), - x = paste(names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "startYear"]), "\n"), - y = paste(names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "incidenceRateP100py"]), " (log10 scale)"), - color = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "cdmSourceAbbreviation"]), - #size = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "outcomes"]), - shape = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "genderName"]), - #linetype = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "genderName"]), - caption = caption_text - ) + - ggplot2::guides(alpha = "none", size = "none", linetype = "none") + # Remove the alpha and size legend - ggplot2::theme_bw() + - ggplot2::theme( - plot.title = ggplot2::element_text(margin = ggplot2::margin(b = 10), hjust = 0.5, size = 25, face="bold"), - plot.subtitle = ggplot2::element_text(margin = ggplot2::margin(b = 20), hjust = 0.5, size = 16), - axis.title.x = ggplot2::element_text(margin = ggplot2::margin(t = 25), size = 18), - axis.title.y = ggplot2::element_text(margin = ggplot2::margin(r = 25), size = 18), - axis.text.x = ggplot2::element_text(size = 14, angle = 45, hjust = 0.5, vjust = 0.25), - axis.text.y = ggplot2::element_text(size = 14), - legend.position = "bottom", - legend.box.spacing = ggplot2::unit(3, "pt"), - legend.text = ggplot2::element_text(size=10), - legend.title = ggplot2::element_text(size=16, face = "bold"), - legend.title.align = 0.5, - plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12, - margin = ggplot2::margin(t = 20)), - #legend.spacing.x = ggplot2::unit(2.0, 'cm'), - # legend.box = "horizontal", - # legend.key.size = ggplot2::unit(3, 'points'), #change legend key size - # legend.title = ggplot2::element_text(size=30), #change legend title font size - # legend.text = ggplot2::element_text(size=20), - panel.spacing = ggplot2::unit(2, "lines"), - # strip.background = ggplot2::element_blank(), - strip.text = ggplot2::element_text(face="bold", size = 14), - strip.background = ggplot2::element_blank(), - strip.clip = "off" - ) + - ggplot2::guides(shape = ggplot2::guide_legend(override.aes = list(size = 6)), - color = ggplot2::guide_legend(override.aes = list(size = 6)) + + + + # by calendar year + renderIrPlotStandardYear <- shiny::reactive({ + + if (is.null(targetIds()) | + is.null(outcomeIds())) { + return(data.frame()) + } + if(nrow(filteredData()) == 0){ + return(data.frame()) + } + + ifelse(incidenceRateTarFilter() %in% filteredData()$tar, + plotData <- filteredData() %>% + dplyr::filter(.data$tar %in% incidenceRateTarFilter()), + shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") + ) + + ifelse(length(incidenceRateTarFilter() %in% filteredData()$tar) == 1, + plotData <- filteredData() %>% + dplyr::filter(.data$tar %in% incidenceRateTarFilter()), + shiny::validate("Please select only one TAR at a time to view yearly plots.") + ) + + ifelse((length(targetIds()) == 1) & + (length(outcomeIds()) == 1), + plotData <- plotData, + shiny::validate("Please select only one Target and Outcome at a time to view yearly plots.") + ) + + ifelse((length(incidenceRateCalendarFilter()) == 1) & + (incidenceRateCalendarFilter() == "Any"), + shiny::validate("Please select at least one start year besides `Any`. This plot depicts calendar trends over time on the x-axis, so at least one distinct year is required."), + plotData <- plotData + ) + + + + plotData <- plotData %>% + dplyr::filter(.data$genderName != "Any" & + .data$startYear != "Any") %>% + dplyr::mutate(targetLabel = paste(.data$targetNameShort, " = ", .data$targetName), + outcomeLabel = paste(.data$outcomeNameShort, " = ", .data$outcomeName), + ageGroupName = factor(.data$ageGroupName, levels = custom_age_sort(.data$ageGroupName), ordered = TRUE) + ) %>% + dplyr::rename(Target = "targetNameShort", + Outcome = "outcomeNameShort", + Age = "ageGroupName") + + #get unique shorthand cohort name + unique_target <- unique(plotData$Target) + unique_outcome <- unique(plotData$Outcome) + + + # Get unique target and outcome labels + unique_target_labels <- strwrap(unique(plotData$targetLabel), width = 300) + unique_outcome_labels <- strwrap(unique(plotData$outcomeLabel), width = 300) + + # Combine all unique values into a final vector + final_unique_values <- unique(c(unique_target_labels, unique_outcome_labels)) + + # Create the caption text with line breaks + caption_text <- paste(final_unique_values, collapse = "\n") + + + # Take the specific tar value you want to plot + tar_value <- unique(plotData$tar)[1] + + plotData <- plotData %>% + dplyr::filter("Any" %!in% .data$startYear) %>% + dplyr::mutate(startYear = as.Date(paste0(.data$startYear, "-01-01")) + ) + + base_plot <- ggplot2::ggplot( + data = plotData, + ggplot2::aes(x = .data$startYear, + y = .data$incidenceRateP100py, + shape = .data$genderName, + color = .data$cdmSourceAbbreviation, + group = interaction(.data$cdmSourceAbbreviation, .data$genderName) + ) + ) + + ggplot2::geom_point( + ggplot2::aes(size = 2.5) + ) + + ggplot2::geom_line(ggplot2::aes(linetype = .data$genderName)) + + ggplot2::scale_colour_brewer(palette = "Paired") + + ggplot2::facet_wrap( + ~Age, + labeller = "label_both", + scales = "free_x", + nrow = 2 + ) + + ggplot2::scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), + n.breaks = 3) + + ggplot2::scale_x_date(breaks= seq(min(plotData$startYear), max(plotData$startYear), by = "3 years"), + date_labels = "%Y", + limits = c(min(plotData$startYear), + max(plotData$startYear)) + ) + + base_plot <- base_plot + ggplot2::labs( + title = paste("Incidence Rate for Time at Risk:", tar_value), + subtitle = paste("Target = ", unique_target, "; Outcome = ", unique_outcome, sep = ""), + x = paste(names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "startYear"]), "\n"), + y = paste(names(ciOptions$irPlotNumericChoices[ciOptions$irPlotNumericChoices %in% "incidenceRateP100py"]), " (log10 scale)"), + color = names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "cdmSourceAbbreviation"]), + shape = names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "genderName"]), + caption = caption_text + ) + + ggplot2::guides(alpha = "none", size = "none", linetype = "none") + # Remove the alpha and size legend + ggplot2::theme_bw() + + ggplot2::theme( + plot.title = ggplot2::element_text(margin = ggplot2::margin(b = 10), hjust = 0.5, size = 25, face="bold"), + plot.subtitle = ggplot2::element_text(margin = ggplot2::margin(b = 20), hjust = 0.5, size = 16), + axis.title.x = ggplot2::element_text(margin = ggplot2::margin(t = 25), size = 18), + axis.title.y = ggplot2::element_text(margin = ggplot2::margin(r = 25), size = 18), + axis.text.x = ggplot2::element_text(size = 14, angle = 45, hjust = 0.5, vjust = 0.25), + axis.text.y = ggplot2::element_text(size = 14), + legend.position = "bottom", + legend.box.spacing = ggplot2::unit(3, "pt"), + legend.text = ggplot2::element_text(size=10), + legend.title = ggplot2::element_text(size=16, face = "bold"), + legend.title.align = 0.5, + plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12, + margin = ggplot2::margin(t = 20)), + panel.spacing = ggplot2::unit(2, "lines"), + strip.text = ggplot2::element_text(face="bold", size = 14), + strip.background = ggplot2::element_blank(), + strip.clip = "off" + ) + + ggplot2::guides(shape = ggplot2::guide_legend(override.aes = list(size = 6)), + color = ggplot2::guide_legend(override.aes = list(size = 6)) + ) + + return(base_plot) + + }) + + output$incidencePlotStandardYear<- + shiny::renderPlot({ + renderIrPlotStandardYear() + }) + + + # Define a function to save the plot as an image + output$downloadPlotStandardYear <- shiny::downloadHandler( + filename = function() { + paste("standard-yearly-ir-plot-", Sys.Date(), ".png", sep = "") + }, + content = function(file) { + cowplot::save_plot(file, plot = renderIrPlotStandardYear(), base_height = 24) + } ) - - return(base_plot) - - } -) - -output$incidencePlotStandardYear<- - shiny::renderPlot({ - renderIrPlotStandardYear() - }) - - -# Define a function to save the plot as an image -output$downloadPlotStandardYear <- shiny::downloadHandler( - filename = function() { - paste("standard-yearly-ir-plot-", Sys.Date(), ".png", sep = "") - }, - content = function(file) { - cowplot::save_plot(file, plot = renderIrPlotStandardYear(), base_height = 24) - } -) - - - - - - - - -#aggregate (unstratified) + -renderIrPlotStandardAggregate <- shiny::reactive( - { - - if (is.null(inputSelectedResults()$targetIds) | - is.null(inputSelectedResults()$outcomeIds)) { - return(data.frame()) - } - - ifelse(inputSelectedResults()$incidenceRateTarFilter %in% filteredData()$tar, - plotData <- filteredDataAggregateForPlot() %>% - dplyr::filter(.data$tar %in% inputSelectedResults()$incidenceRateTarFilter), - shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") - ) - - #add check to make sure facetted plots fit nicely in plotting window (600px). this is currently nrow * ncol in facet_wrap() - ifelse(length(inputSelectedResults()$targetId) * length(inputSelectedResults()$outcomeId) <= 10, - plotData <- filteredData(), - shiny::validate("Too many Target-Outcome pairs selected to plot efficiently. Please choose fewer targets and/or outcomes.") - ) - - plotData <- plotData %>% - dplyr::filter(ageGroupName == "Any" & - genderName == "Any") %>% - dplyr::mutate(targetLabel = paste(targetIdShort, " = ", targetName), - outcomeLabel = paste(outcomeIdShort, " = ", outcomeName) - ) %>% - dplyr::rename("Target" = targetIdShort, - "Outcome" = outcomeIdShort, - "Age" = ageGroupName) - - # Get unique target and outcome labels - unique_target_labels <- strwrap(unique(plotData$targetLabel), width = 300) - unique_outcome_labels <- strwrap(unique(plotData$outcomeLabel), width = 300) - - # Combine all unique values into a final vector - final_unique_values <- unique(c(unique_target_labels, unique_outcome_labels)) - - # Create the caption text with line breaks - caption_text <- paste(final_unique_values, collapse = "\n") - - - # Take the specific tar value you want to plot - tar_value <- unique(plotData$tar)[1] - - base_plot <- ggplot2::ggplot( - data = plotData, - ggplot2::aes(x = startYear, - y = incidenceRateP100py, - #shape = genderName, - color = cdmSourceAbbreviation + #aggregate (unstratified) + + renderIrPlotStandardAggregate <- shiny::reactive({ + + if (is.null(targetIds()) | + is.null(outcomeIds())) { + return(data.frame()) + } + if(nrow(filteredData()) == 0){ + return(data.frame()) + } + + ifelse(incidenceRateTarFilter() %in% filteredData()$tar, + plotData <- filteredDataAggregateForPlot() %>% + dplyr::filter(.data$tar %in% incidenceRateTarFilter()), + shiny::validate("Selected TAR is not found in your result data. Revise input selections or select a different TAR.") + ) + + #add check to make sure facetted plots fit nicely in plotting window (600px). this is currently nrow * ncol in facet_wrap() + ifelse(length(targetIds()) * length(outcomeIds()) <= 10, + plotData <- filteredData(), + shiny::validate("Too many Target-Outcome pairs selected to plot efficiently. Please choose fewer targets and/or outcomes.") + ) + + ifelse("Any" %in% incidenceRateAgeFilter() & + "Any" %in% incidenceRateGenderFilter() & + "Any" %in% incidenceRateCalendarFilter(), + plotData <- filteredData(), + shiny::validate("This plot requires the `Any` category to be selected to aggregate over all ages, sexes, and years. Please ensure `Any` is selected in each of these inputs above and try again.") + ) + + plotData <- plotData %>% + dplyr::filter(.data$ageGroupName == "Any" & + .data$genderName == "Any") %>% + dplyr::mutate(targetLabel = paste(.data$targetNameShort, " = ", .data$targetName), + outcomeLabel = paste(.data$outcomeNameShort, " = ", .data$outcomeName) + ) %>% + dplyr::rename(Target = "targetNameShort", + Outcome = "outcomeNameShort", + Age = "ageGroupName") + + # Get unique target and outcome labels + unique_target_labels <- strwrap(unique(plotData$targetLabel), width = 300) + unique_outcome_labels <- strwrap(unique(plotData$outcomeLabel), width = 300) + + # Combine all unique values into a final vector + final_unique_values <- unique(c(unique_target_labels, unique_outcome_labels)) + + # Create the caption text with line breaks + caption_text <- paste(final_unique_values, collapse = "\n") + + + # Take the specific tar value you want to plot + tar_value <- unique(plotData$tar)[1] + + base_plot <- ggplot2::ggplot( + data = plotData, + ggplot2::aes(x = .data$startYear, + y = .data$incidenceRateP100py, + color = .data$cdmSourceAbbreviation + ) + ) + + ggplot2::geom_point( + ggplot2::aes(size = 3) + ) + + #ggplot2::geom_jitter() + + #scale_size_continuous(range = c(5,15)) + + ggplot2::scale_colour_brewer(palette = "Paired") + + ggplot2::facet_wrap( + Target~Outcome, + labeller = "label_both", + scales = "free_x", + nrow = 2, + ncol = 5 + #, + #strip.position = "right" + ) + + ggplot2::scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), + n.breaks = 4) + + base_plot <- base_plot + ggplot2::labs( + title = paste("Incidence Rate for Time at Risk:", tar_value), + x = paste(names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "startYear"]), "\n"), + y = names(ciOptions$irPlotNumericChoices[ciOptions$irPlotNumericChoices %in% "incidenceRateP100py"]), + color = names(ciOptions$irPlotCategoricalChoices[ciOptions$irPlotCategoricalChoices %in% "cdmSourceAbbreviation"]), + caption = caption_text + ) + + ggplot2::guides(alpha = "none", size = "none") + # Remove the alpha and size legend + ggplot2::theme_bw() + + ggplot2::theme( + plot.title = ggplot2::element_text(margin = ggplot2::margin(b = 10), hjust = 0.5, size = 25, face="bold"), + plot.subtitle = ggplot2::element_text(margin = ggplot2::margin(b = 20), hjust = 0.5, size = 16), + axis.title.x = ggplot2::element_text(margin = ggplot2::margin(t = 25), size = 18), + axis.title.y = ggplot2::element_text(margin = ggplot2::margin(r = 25), size = 18), + axis.text.x = ggplot2::element_text(size = 14, angle = 45, hjust = 0.5, vjust = 0.25), + axis.text.y = ggplot2::element_text(size = 14), + legend.position = "bottom", + legend.box.spacing = ggplot2::unit(3, "pt"), + legend.text = ggplot2::element_text(size=10), + legend.title = ggplot2::element_text(size=16, face = "bold"), + legend.title.align = 0.5, + plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12, + margin = ggplot2::margin(t = 20)), + panel.spacing = ggplot2::unit(2, "lines"), + strip.text = ggplot2::element_text(face="bold", size = 14), + strip.background = ggplot2::element_blank(), + strip.clip = "off" + ) + + ggplot2::guides(shape = ggplot2::guide_legend(override.aes = list(size = 6)), + color = ggplot2::guide_legend(override.aes = list(size = 6))) + + return(base_plot) + + }) + + output$incidencePlotStandardAggregate <- + shiny::renderPlot({ + renderIrPlotStandardAggregate() + }) + + + # Define a function to save the plot as an image + output$downloadPlotStandardAggregate <- shiny::downloadHandler( + filename = function() { + paste("standard-aggregate-ir-plot-", Sys.Date(), ".png", sep = "") + }, + content = function(file) { + cowplot::save_plot(file, plot = renderIrPlotStandardAggregate(), base_height = 12) + } ) - ) + - ggplot2::geom_point( - ggplot2::aes(size = 3) - ) + - #ggplot2::geom_jitter() + - #scale_size_continuous(range = c(5,15)) + - ggplot2::scale_colour_brewer(palette = "Dark2") + - ggplot2::facet_wrap( - Target~Outcome, - labeller = "label_both", - scales = "free_x", - nrow = 2, - ncol = 5 - #, - #strip.position = "right" - ) + - ggplot2::scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), - n.breaks = 4) - - base_plot <- base_plot + ggplot2::labs( - title = paste("Incidence Rate for Time at Risk:", tar_value), - x = paste(names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "ageGroupName"]), "\n"), - y = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "incidenceRateP100py"]), - color = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "cdmSourceAbbreviation"]), - #size = names(options$irPlotNumericChoices[options$irPlotNumericChoices %in% "outcomes"]), - #shape = names(options$irPlotCategoricalChoices[options$irPlotCategoricalChoices %in% "genderName"]), - caption = caption_text - ) + - ggplot2::guides(alpha = "none", size = "none") + # Remove the alpha and size legend - ggplot2::theme_bw() + - ggplot2::theme( - plot.title = ggplot2::element_text(margin = ggplot2::margin(b = 10), hjust = 0.5, size = 25, face="bold"), - plot.subtitle = ggplot2::element_text(margin = ggplot2::margin(b = 20), hjust = 0.5, size = 16), - axis.title.x = ggplot2::element_text(margin = ggplot2::margin(t = 25), size = 18), - axis.title.y = ggplot2::element_text(margin = ggplot2::margin(r = 25), size = 18), - axis.text.x = ggplot2::element_text(size = 14, angle = 45, hjust = 0.5, vjust = 0.25), - axis.text.y = ggplot2::element_text(size = 14), - legend.position = "bottom", - legend.box.spacing = ggplot2::unit(3, "pt"), - legend.text = ggplot2::element_text(size=10), - legend.title = ggplot2::element_text(size=16, face = "bold"), - legend.title.align = 0.5, - plot.caption = ggplot2::element_text(hjust = 0, face = "italic", size = 12, - margin = ggplot2::margin(t = 20)), - #legend.spacing.x = ggplot2::unit(2.0, 'cm'), - # legend.box = "horizontal", - # legend.key.size = ggplot2::unit(3, 'points'), #change legend key size - # legend.title = ggplot2::element_text(size=30), #change legend title font size - # legend.text = ggplot2::element_text(size=20), - panel.spacing = ggplot2::unit(2, "lines"), - # strip.background = ggplot2::element_blank(), - strip.text = ggplot2::element_text(face="bold", size = 14), - strip.background = ggplot2::element_blank(), - strip.clip = "off" - ) + - ggplot2::guides(shape = ggplot2::guide_legend(override.aes = list(size = 6)), - color = ggplot2::guide_legend(override.aes = list(size = 6))) - - return(base_plot) - - } -) - -output$incidencePlotStandardAggregate <- - shiny::renderPlot({ - renderIrPlotStandardAggregate() - }) - - -# Define a function to save the plot as an image -output$downloadPlotStandardAggregate <- shiny::downloadHandler( - filename = function() { - paste("standard-aggregate-ir-plot-", Sys.Date(), ".png", sep = "") - }, - content = function(file) { - cowplot::save_plot(file, plot = renderIrPlotStandardAggregate(), base_height = 12) - } -) - - - + return(invisible(NULL)) ############# end of server }) } - - - - - - - - - - - - +#------------ #------------ Fetching data functions - +#------------ + getIncidenceData <- function( targetIds, outcomeIds, connectionHandler, resultDatabaseSettings ){ - if(!is.null(targetIds) & !is.null(outcomeIds)){ - #shiny::withProgress(message = 'Getting incidence data', value = 0, { + print(targetIds) + print(outcomeIds) + + shiny::withProgress(message = 'Getting incidence data', value = 0, { - sql <- 'select d.cdm_source_abbreviation, i.* - from @result_schema.@incidence_table_prefixINCIDENCE_SUMMARY i - inner join @result_schema.@database_table_name d + sql <- 'select d.cdm_source_abbreviation, i.*, ct1.cohort_name as target_name, ct2.cohort_name as outcome_name +from ( + select od.outcome_cohort_definition_id, od.clean_window, agd.age_group_name, + tad.tar_start_with, tad.tar_start_offset, tad.tar_end_with, tad.tar_end_offset, + sd.subgroup_name, i.* + from @result_schema.@incidence_table_prefixINCIDENCE_SUMMARY i + join @result_schema.@incidence_table_prefixOUTCOME_DEF od on i.outcome_id = od.outcome_id + and i.ref_id = od.ref_id + join @result_schema.@incidence_table_prefixTAR_DEF tad on i.tar_id = tad.tar_id + and i.ref_id = tad.ref_id + join @result_schema.@incidence_table_prefixSUBGROUP_DEF sd on i.subgroup_id = sd.subgroup_id + and i.ref_id = sd.ref_id + left join @result_schema.@incidence_table_prefixAGE_GROUP_DEF agd on i.age_group_id = agd.age_group_id + and i.ref_id = agd.ref_id +) i +inner join @result_schema.@database_table_name d on d.database_id = i.database_id - where target_cohort_definition_id in (@target_ids) - and outcome_cohort_definition_id in (@outcome_ids) - ;' +inner join @result_schema.@cg_table_prefixcohort_definition ct1 + on ct1.cohort_definition_id = i.target_cohort_definition_id +inner join @result_schema.@cg_table_prefixcohort_definition ct2 + on ct2.cohort_definition_id = i.outcome_cohort_definition_id +where i.target_cohort_definition_id in (@target_ids) + and i.outcome_cohort_definition_id in (@outcome_ids);' - #shiny::incProgress(1/2, detail = paste("Created SQL - Extracting...")) + shiny::incProgress(1/2, detail = paste("Created SQL - Extracting...")) resultTable <- connectionHandler$queryDb( sql = sql, result_schema = resultDatabaseSettings$schema, incidence_table_prefix = resultDatabaseSettings$incidenceTablePrefix, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix, target_ids = paste(as.double(targetIds), collapse = ','), outcome_ids = paste(as.double(outcomeIds), collapse = ','), database_table_name = resultDatabaseSettings$databaseTable ) - #shiny::incProgress(2/2, detail = paste("Done...")) + shiny::incProgress(2/2, detail = paste("Extracted ", nrow(resultTable)," rows")) - #}) - - # format the tar - resultTable$tar <- paste0('(',resultTable$tarStartWith, " + ", resultTable$tarStartOffset, ') - (', resultTable$tarEndWith, " + ", resultTable$tarEndOffset, ')') - resultTable <- resultTable %>% - dplyr::select(-c("tarStartWith","tarStartOffset","tarEndWith","tarEndOffset", "tarId", "subgroupName")) - - resultTable[is.na(resultTable)] <- 'Any' - resultTable <- unique(resultTable) + }) + if(nrow(resultTable)>0){ + + # format the tar + ##Jenna edit resultTable$tar <- paste0('(',resultTable$tarStartWith, " + ", resultTable$tarStartOffset, ') - (', resultTable$tarEndWith, " + ", resultTable$tarEndOffset, ')') + resultTable$tar <- cohortIncidenceFormatTar(resultTable) + + resultTable <- resultTable %>% + dplyr::select(-c("tarStartWith","tarStartOffset","tarEndWith","tarEndOffset", "tarId", "subgroupName")) + + resultTable[is.na(resultTable)] <- 'Any' + resultTable <- unique(resultTable) + } return(resultTable) } else{ return(NULL) } } +# Jenna added +cohortIncidenceFormatTar <- function(x){ + result <- paste0('(',x$tarStartWith, " + ", x$tarStartOffset, ') - (', x$tarEndWith, " + ", x$tarEndOffset, ')') + return(result) +} -getIncidenceOptions <- function( - connectionHandler, - resultDatabaseSettings -){ - - # shiny::withProgress(message = 'Getting incidence inputs', value = 0, { - - sql <- 'select distinct target_cohort_definition_id, target_name - from @result_schema.@incidence_table_prefixINCIDENCE_SUMMARY;' - - #shiny::incProgress(1/3, detail = paste("Created SQL - Extracting targets")) - - targets <- connectionHandler$queryDb( - sql = sql, - result_schema = resultDatabaseSettings$schema, - incidence_table_prefix = resultDatabaseSettings$incidenceTablePrefix - ) - targetIds <- targets$targetCohortDefinitionId - names(targetIds) <- targets$targetName - - sql <- 'select distinct outcome_cohort_definition_id, outcome_name - from @result_schema.@incidence_table_prefixINCIDENCE_SUMMARY;' - - #shiny::incProgress(2/3, detail = paste("Created SQL - Extracting outcomes")) - - outcomes <- connectionHandler$queryDb( - sql = sql, - result_schema = resultDatabaseSettings$schema, - incidence_table_prefix = resultDatabaseSettings$incidenceTablePrefix - ) - - outcomeIds <- outcomes$outcomeCohortDefinitionId - names(outcomeIds) <- outcomes$outcomeName - - sql <- 'select distinct tar_id, tar_start_with, tar_start_offset, tar_end_with, tar_end_offset - from @result_schema.@incidence_table_prefixINCIDENCE_SUMMARY;' - - #shiny::incProgress(1/3, detail = paste("Created SQL - Extracting targets")) - - tars <- connectionHandler$queryDb( - sql = sql, +getIncidenceOptions <- function(connectionHandler, + resultDatabaseSettings){ + + # database options + databaseDf <- connectionHandler$queryDb( + sql = 'select database_id, cdm_source_abbreviation from @result_schema.@database_table_name;', result_schema = resultDatabaseSettings$schema, - incidence_table_prefix = resultDatabaseSettings$incidenceTablePrefix + database_table_name = resultDatabaseSettings$databaseTable ) - tar <- paste0('(',tars$tarStartWith, " + ", tars$tarStartOffset, ') - (', tars$tarEndWith, " + ", tars$tarEndOffset, ')') - #tar <- tars$tarId - names(tar) <- paste0('(',tars$tarStartWith, " + ", tars$tarStartOffset, ') - (', tars$tarEndWith, " + ", tars$tarEndOffset, ')') - - sql <- 'select distinct age_group_name - from @result_schema.@incidence_table_prefixINCIDENCE_SUMMARY;' - - result <- connectionHandler$queryDb( - sql = sql, + databases <- databaseDf$cdmSourceAbbreviation + + # Age Gruop Options + ageGroupDf <- connectionHandler$queryDb( + sql = 'select age_group_id, age_group_name from @result_schema.@incidence_table_prefixAGE_GROUP_DEF;', result_schema = resultDatabaseSettings$schema, incidence_table_prefix = resultDatabaseSettings$incidenceTablePrefix ) + ageGroupDf <- rbind(data.frame(ageGroupId = 'Any', ageGroupName = 'Any'), ageGroupDf) + ageGroup <- ageGroupDf$ageGroupId + names(ageGroup) <- ageGroupDf$ageGroupName - ageGroupName <- result$ageGroupName - ageGroupName[is.na(ageGroupName)] <- 'Any' - ageGroupName <- sort(ageGroupName) + sex <- c(8507, 8532 , 'Any') + names(sex) <- c('Male', 'Female', 'Any') - sql <- 'select distinct gender_name - from @result_schema.@incidence_table_prefixINCIDENCE_SUMMARY;' + startYear <- c('Any', format(Sys.Date(), "%Y"):1990) + names(startYear) <- c('Any', format(Sys.Date(), "%Y"):1990) - result <- connectionHandler$queryDb( - sql = sql, - result_schema = resultDatabaseSettings$schema, - incidence_table_prefix = resultDatabaseSettings$incidenceTablePrefix - ) + # get tar and then call cohortIncidenceFormatTar() - genderName <- result$genderName - genderName[is.na(genderName)] <- 'Any' - genderName <- sort(genderName) + tarDf <- characterizationGetCiTars(connectionHandler,resultDatabaseSettings) - sql <- 'select distinct start_year - from @result_schema.@incidence_table_prefixINCIDENCE_SUMMARY;' + tar <- tarDf$tarId + names(tar) <- cohortIncidenceFormatTar(tarDf) - result <- connectionHandler$queryDb( + sql <- ' +select outcome_id, outcome_name +from @result_schema.@incidence_table_prefixOUTCOME_DEF +' + outcomeDf <- connectionHandler$queryDb( sql = sql, result_schema = resultDatabaseSettings$schema, incidence_table_prefix = resultDatabaseSettings$incidenceTablePrefix ) - - startYear <- result$startYear - startYear[is.na(startYear)] <- 'Any' - startYear <- sort(startYear) - - # shiny::incProgress(3/3, detail = paste("Done")) - # }) + outcomes <- outcomeDf$outcomeId + names(outcomes) <- outcomeDf$outcomeName irPlotCategoricalChoices <- list( "cdmSourceAbbreviation", @@ -2013,16 +1858,41 @@ getIncidenceOptions <- function( return( list( - targetIds = targetIds, - outcomeIds = outcomeIds, + databases = databases, + ageGroup = ageGroup, + sex = sex, + startYear = startYear, tar = tar, + outcomes = outcomes, irPlotNumericChoices = irPlotNumericChoices, - irPlotCategoricalChoices = irPlotCategoricalChoices, - ageGroupName = ageGroupName, - genderName = genderName, - startYear = startYear + irPlotCategoricalChoices = irPlotCategoricalChoices ) ) } +characterizationGetCiTars <- function(connectionHandler, + resultDatabaseSettings){ + sql <- "SELECT TAR_ID, TAR_START_WITH, TAR_START_OFFSET, + TAR_END_WITH, TAR_END_OFFSET + from @schema.@ci_table_prefixtar_def;" + tars <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + ci_table_prefix = resultDatabaseSettings$incidenceTablePrefix + ) + return(tars) +} + +.createCiColDefList <- function() { + colDefCsv <- readr::read_csv(system.file("components-columnInformation", + "characterization-incidence-colDefs.csv", + package = "OhdsiShinyModules"), + show_col_types = FALSE) + + createCustomColDefList( + rawColNames = colDefCsv$colName, + niceColNames = colDefCsv$niceName, + tooltipText = colDefCsv$toolTip + ) +} \ No newline at end of file diff --git a/R/characterization-main.R b/R/characterization-main.R index 1cf1b21a..9f84f52f 100644 --- a/R/characterization-main.R +++ b/R/characterization-main.R @@ -21,7 +21,7 @@ #' #' @details #' Returns the location of the characterization helper file -#' +#' @family {Characterization} #' @return #' string location of the characterization helper file #' @@ -37,7 +37,7 @@ characterizationHelperFile <- function(){ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {Characterization} #' @return #' The user interface to the characterization viewer module #' @@ -46,14 +46,28 @@ characterizationViewer <- function(id=1) { ns <- shiny::NS(id) shinydashboard::box( - status = 'info', width = 12, + status = 'info', width = '100%', title = shiny::span( shiny::icon("table"), "Characterization Viewer"), solidHeader = TRUE, - - shiny::tabsetPanel( - type = 'pills', - id = ns('mainPanel') - ) + + # pick a targetId of interest + shinydashboard::box( + title = 'Target Of Interest', + width = '100%', + status = "primary", + collapsible = T, + shiny::uiOutput(ns("targetSelection")) + ), + + shiny::conditionalPanel( + condition = 'input.targetSelect', + ns = ns, + inputSelectionDfViewer(id = ns('targetSelected'), title = 'Selected Target'), + shiny::tabsetPanel( + type = 'pills', + id = ns('mainPanel') + ) + ) ) } @@ -66,7 +80,7 @@ characterizationViewer <- function(id=1) { #' @param id the unique reference id for the module #' @param connectionHandler a connection to the database with the results #' @param resultDatabaseSettings a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix -#' +#' @family {Characterization} #' @return #' The server for the characterization module #' @@ -79,7 +93,8 @@ characterizationServer <- function( shiny::moduleServer( id, function(input, output, session) { - + + # this function checks tables exist for the tabs # and returns the tabs that should be displayed # as the tables exist @@ -88,107 +103,427 @@ characterizationServer <- function( resultDatabaseSettings = resultDatabaseSettings ) - # add the tabs based on results - types <- list( - c("Target Viewer","characterizationTableViewer", "descriptiveTableTab"), - c("Outcome Stratified", "characterizationAggregateFeaturesViewer", "aggregateFeaturesTab"), - c("Incidence Rate", "characterizationIncidenceViewer", "incidenceTab"), - c("Time To Event", "characterizationTimeToEventViewer", "timeToEventTab"), - c("Dechallenge Rechallenge", 'characterizationDechallengeRechallengeViewer', 'dechallengeRechallengeTab') + #================================================ + # GETTING OPTIONS FOR INPTUS + #================================================ + #TODO add time-to-event and dechal-rechal options + options <- characterizationGetOptions( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + includeAggregate = "Risk Factor" %in% charTypes$subPanel, + includeIncidence = "Incidence Results" %in% charTypes$subPanel + ) + + #================================================ + # PARENT TARGET SELECTION UI + #================================================ + parents <- characterizationGetParents(options) + parentIndex <- shiny::reactiveVal(1) + subTargets <- shiny::reactiveVal() + + # add an input for all char that lets you select cohort of interest + output$targetSelection <- shiny::renderUI({ + shiny::div( + shinyWidgets::pickerInput( + inputId = session$ns('targetId'), + label = 'Target Group: ', + choices = parents, + selected = parents[1], + multiple = FALSE, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + dropupAuto = F, + size = 10, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 500 + ) + ), + shiny::selectInput( + inputId = session$ns('subTargetId'), + label = 'Target: ', + choices = characterizationGetChildren(options,1), + selected = 1, + multiple = FALSE, + selectize = TRUE, + width = NULL, + size = NULL + ), + shiny::actionButton( + inputId = session$ns('targetSelect'), + label = 'Select', + icon = shiny::icon('redo') + ) + ) + }) + + #================================================ + # UPDATE TARGET BASED ON TARGET GROUP + #================================================ + shiny::observeEvent(input$targetId,{ + parentIndex(which(parents == input$targetId)) + subTargets(characterizationGetChildren(options,which(parents == input$targetId))) + shiny::updateSelectInput( + inputId = 'subTargetId', + label = 'Target: ', + choices = subTargets(), + selected = subTargets()[1] + ) + }) + + #================================================ + # PARENT TARGET SELECTION ACTION + #================================================ + # reactives updated when parent target is selected + outcomes <- shiny::reactiveVal() + targetSelected <- shiny::reactiveVal() + subTargetId <- shiny::reactiveVal() + # output the selected target + shiny::observeEvent(input$targetSelect, { + + # First create input dataframe and add to the inputServer to display + targetSelected( + data.frame( + `Target group` = names(parents)[parents == input$targetId], + `Target` = names(subTargets())[subTargets() == input$subTargetId] + ) + ) + inputSelectionDfServer( + id = 'targetSelected', + dataFrameRow = targetSelected, + ncol = 1 + ) + + subTargetId(input$subTargetId) + + # update the outcomes for the selected parent target id + outcomes(characterizationGetOutcomes(options, parentIndex())) + + # create the outcome selector for the case exposure tabs + output$outcomeSelection <- shiny::renderUI({ + shinydashboard::box( + collapsible = TRUE, + title = "Options", + width = "100%", + + shinyWidgets::pickerInput( + inputId = session$ns('outcomeId'), + label = 'Outcome: ', + choices = outcomes(), + selected = 1, + multiple = FALSE, + options = shinyWidgets::pickerOptions( + actionsBox = F, + dropupAuto = F, + size = 10, + liveSearch = TRUE, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search" + ) + ), + shiny::actionButton( + inputId = session$ns('outcomeSelect'), + label = 'Select', + icon = shiny::icon('redo') + ) + ) + }) + + }) + + #================================================ + # OUTCOME SELECTION ACTION + #================================================ + # used by the case exposure tabs + # show the selected outcome + outcomeSelected <- shiny::reactiveVal() + outcomeId <- shiny::reactiveVal() + #subTargetId <- shiny::reactiveVal() + + shiny::observeEvent(input$outcomeSelect, { + outcomeSelected( + data.frame( + #Target = names(subTargets())[subTargets() == input$subTargetId], + Outcome = names(outcomes())[outcomes() == input$outcomeId] + ) + ) + + # store the outcome and subTargetIds for the case exposure tabs + outcomeId(input$outcomeId) + #subTargetId(input$subTargetId) + + inputSelectionDfServer( + id = 'outcomeSelected', + dataFrameRow = outcomeSelected, + ncol = 1 + ) + }) + + + #================================================ + # CREATE TABS BASED ON RESULTS TABLES + #================================================ + + # MAIN PANELS + #first populate the mainPanel + typesMainPanel <- list( + list( + title = 'Cohort Summary', + shiny::tabsetPanel( + type = 'pills', + id = session$ns('cohortSummaryPanel') + ) + ), + list( + title = 'Exposed Cases Summary', + shiny::uiOutput(session$ns("outcomeSelection")), + shiny::conditionalPanel( + condition = 'input.outcomeSelect', + ns = session$ns, + inputSelectionDfViewer(id = session$ns('outcomeSelected'), title = 'Selected'), + shiny::tabsetPanel( + type = 'pills', + id = session$ns('exposedCasesPanel') + ) + ) + ), + list( + title = 'Cohort Incidence', + shiny::tabsetPanel( + type = 'pills', + id = session$ns('cohortIncidencePanel') + ) ) + ) + selectVal <- T - for( type in types){ - if(type[1] %in% charTypes){ + for( type in typesMainPanel){ + if(type$title %in% charTypes$mainPanel){ shiny::insertTab( - inputId = "mainPanel", - tab = shiny::tabPanel( - type[1], - do.call(what = type[2], args = list(id = session$ns(type[3]))) - ), + inputId = 'mainPanel', + tab = do.call( + what = shiny::tabPanel, + args = type + ), select = selectVal ) + selectVal = F } - selectVal = F } + + # SUB PANELS + # now populate the subpanel + # add the tabs based on results + types <- rbind( + c("Database Comparison","characterizationDatabaseComparisonViewer", "databaseComparisonTab", "cohortSummaryPanel"), + c("Cohort Comparison", "characterizationCohortComparisonViewer", "cohortComparisonTab", "cohortSummaryPanel"), - previouslyLoaded <- shiny::reactiveVal(c()) + c("Risk Factor", "characterizationRiskFactorViewer", "riskFactorTab", "exposedCasesPanel"), + c("Case Series", "characterizationCaseSeriesViewer", "caseSeriesTab", "exposedCasesPanel"), + c("Time-to-event", "characterizationTimeToEventViewer", "timeToEventTab", "exposedCasesPanel"), + c("Dechallenge Rechallenge", 'characterizationDechallengeRechallengeViewer', 'dechallengeRechallengeTab', "exposedCasesPanel"), + + c("Incidence Results", "characterizationIncidenceViewer", "cohortIncidenceTab", "cohortIncidencePanel") + ) + colnames(types) <- c('c1', 'c2', 'c3', 'c4') + types <- as.data.frame(types) - # only render the tab when selected + for(subPanel in c("cohortSummaryPanel", "exposedCasesPanel", "cohortIncidencePanel")){ + typesOfInterest <- types %>% dplyr::filter(.data$c4 == subPanel) + if(nrow(typesOfInterest)>0){ + selectVal <- T + for( i in 1:nrow(typesOfInterest)){ + if(typesOfInterest[i,1] %in% charTypes$subPanel){ + shiny::insertTab( + inputId = typesOfInterest[i,4], + tab = shiny::tabPanel( + typesOfInterest[i,1], + do.call(what = typesOfInterest[i,2], args = list(id = session$ns(typesOfInterest[i,3]))) + ), + select = selectVal + ) + selectVal = F + } + } + } + } + + + # ============================= + # TRACK CURRENT TAB + # ============================= + # set the current tab + mainPanel <- shiny::reactiveVal('None') shiny::observeEvent(input$mainPanel,{ + mainPanel(input$mainPanel) + }) + cohortSummaryPanel <- shiny::reactiveVal('None') + shiny::observeEvent(input$cohortSummaryPanel,{ + cohortSummaryPanel(input$cohortSummaryPanel) + }) + exposedCasesPanel <- shiny::reactiveVal('None') + shiny::observeEvent(input$exposedCasesPanel,{ + exposedCasesPanel(input$exposedCasesPanel) + }) + + currentTab <- shiny::reactive({ + if(mainPanel() == "Cohort Summary" & cohortSummaryPanel() == 'Cohort Comparison'){ + return('Cohort Comparison') + } + if(mainPanel() == "Cohort Summary" & cohortSummaryPanel() == 'Database Comparison'){ + return('Database Comparison') + } + if(mainPanel() == "Exposed Cases Summary" & exposedCasesPanel() == 'Risk Factor'){ + return('Risk Factor') + } + if(mainPanel() == "Exposed Cases Summary" & exposedCasesPanel() == 'Case Series'){ + return('Case Series') + } + if(mainPanel() == "Exposed Cases Summary" & exposedCasesPanel() == 'Time-to-event'){ + return('Time-to-event') + } + if(mainPanel() == "Exposed Cases Summary" & exposedCasesPanel() == 'Dechallenge Rechallenge'){ + return('Dechallenge Rechallenge') + } + if(mainPanel() == "Cohort Incidence"){ + return("Cohort Incidence") + } + + return('None') + }) # ============================= - # Table of cohorts + # MODULE SERVERS # ============================= - if(input$mainPanel == "Target Viewer"){ - if(!"Target Viewer" %in% previouslyLoaded()){ - characterizationTableServer( - id = 'descriptiveTableTab', + # store what servers have been loaded and only load them the first time + # when the corresponding tab is loaded + previouslyLoaded <- shiny::reactiveVal(c()) + + # only render the tab when selected + shiny::observeEvent(currentTab(), { + # ============================= + # Cohort Comparison + # ============================= + if(currentTab() == 'Cohort Comparison'){ + if(!"Cohort Comparison" %in% previouslyLoaded()){ + characterizationCohortComparisonServer( + id = 'cohortComparisonTab', connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings + resultDatabaseSettings = resultDatabaseSettings, + options = options, + parents = parents, + parentIndex = parentIndex, + subTargetId = subTargetId ) - previouslyLoaded(c(previouslyLoaded(), "Target Viewer")) + previouslyLoaded(c(previouslyLoaded(), "Cohort Comparison")) } } - - - # ============================= - # Aggregrate Features - # ============================= - if(input$mainPanel == "Outcome Stratified"){ - if(!"Outcome Stratified" %in% previouslyLoaded()){ - characterizationAggregateFeaturesServer( - id = 'aggregateFeaturesTab', + + # ============================= + # Database Comparison + # ============================= + if(currentTab() == "Database Comparison"){ + if(!"Database Comparison" %in% previouslyLoaded()){ + characterizationDatabaseComparisonServer( + id = 'databaseComparisonTab', connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings + resultDatabaseSettings = resultDatabaseSettings, + options = options, + parents = parents, + parentIndex = parentIndex, + subTargetId = subTargetId ) - previouslyLoaded(c(previouslyLoaded(), "Outcome Stratified")) + previouslyLoaded(c(previouslyLoaded(), "Database Comparison")) } } - + # ============================= - # Incidence + # Risk Factor # ============================= - if(input$mainPanel == "Incidence Rate"){ - if(!"Incidence Rate" %in% previouslyLoaded()){ - characterizationIncidenceServer( - id = 'incidenceTab', + if(currentTab() == "Risk Factor"){ + if(!"Risk Factor" %in% previouslyLoaded()){ + characterizationRiskFactorServer( + id = 'riskFactorTab', connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings + resultDatabaseSettings = resultDatabaseSettings, + targetId = subTargetId, + outcomeId = outcomeId ) - previouslyLoaded(c(previouslyLoaded(), "Incidence Rate")) + previouslyLoaded(c(previouslyLoaded(), "Risk Factor")) } } - - - # ============================= - # Time To Event - # ============================= - if(input$mainPanel == "Time To Event"){ - if(!"Time To Event" %in% previouslyLoaded()){ + + # ============================= + # Case Series + # ============================= + if(currentTab() == 'Case Series'){ + if(!"Case Series" %in% previouslyLoaded()){ + characterizationCaseSeriesServer( + id = 'caseSeriesTab', + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetId = subTargetId, + outcomeId = outcomeId + ) + previouslyLoaded(c(previouslyLoaded(), "Case Series")) + } + } + + # ============================= + # Time-to-event + # ============================= + if(currentTab() == 'Time-to-event'){ + if(!"Time-to-event" %in% previouslyLoaded()){ characterizationTimeToEventServer( - id = 'timeToEventTab', + id = 'timeToEventTab', connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings + resultDatabaseSettings = resultDatabaseSettings, + targetId = subTargetId, + outcomeId = outcomeId ) - previouslyLoaded(c(previouslyLoaded(), "Time To Event")) + previouslyLoaded(c(previouslyLoaded(), "Time-to-event")) } } - - # ============================= - # Dechallenge Rechallenge - # ============================= - if(input$mainPanel == "Dechallenge Rechallenge"){ + + # ============================= + # Dechallenge Rechallenge + # ============================= + if(currentTab() == 'Dechallenge Rechallenge'){ if(!"Dechallenge Rechallenge" %in% previouslyLoaded()){ characterizationDechallengeRechallengeServer( - id = 'dechallengeRechallengeTab', + id = 'dechallengeRechallengeTab', connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings + resultDatabaseSettings = resultDatabaseSettings, + targetId = subTargetId, + outcomeId = outcomeId ) previouslyLoaded(c(previouslyLoaded(), "Dechallenge Rechallenge")) } } - }) # end observed input tab + + # ============================= + # Incidence + # ============================= + if(currentTab() == "Cohort Incidence"){ + if(!"Incidence Results" %in% previouslyLoaded()){ + characterizationIncidenceServer( + id = 'cohortIncidenceTab', + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + #options = options, + parents = parents, + parentIndex = parentIndex, # reactive + outcomes = outcomes, # reactive + targetIds = subTargetId# reactive + ) + previouslyLoaded(c(previouslyLoaded(), "Incidence Results")) + } + } + }) + + } ) @@ -209,13 +544,21 @@ getCharacterizationTypes <- function( connection = conn, databaseSchema = resultDatabaseSettings$schema ) - + + #"Database Comparison" - TODO check multiple databases? + # check Targets if(sum(paste0( resultDatabaseSettings$cTablePrefix, c('covariates', 'covariate_ref', 'cohort_details', 'settings') ) %in% tbls) == 4){ - results <- c(results, "Target Viewer", "Outcome Stratified" ) + results <- rbind( + results, + c("Database Comparison",'Cohort Summary', 'cohortSummaryPanel'), + c("Cohort Comparison",'Cohort Summary', 'cohortSummaryPanel'), + c("Risk Factor",'Exposed Cases Summary', 'exposedCasesPanel'), + c("Case Series",'Exposed Cases Summary', 'exposedCasesPanel') + ) } # check dechallenge_rechallenge @@ -223,7 +566,10 @@ getCharacterizationTypes <- function( resultDatabaseSettings$cTablePrefix, 'dechallenge_rechallenge' ) %in% tbls){ - results <- c(results, "Dechallenge Rechallenge") + results <- rbind( + results, + c("Dechallenge Rechallenge",'Exposed Cases Summary', 'exposedCasesPanel') + ) } # check time_to_event @@ -231,7 +577,10 @@ getCharacterizationTypes <- function( resultDatabaseSettings$cTablePrefix, 'time_to_event' ) %in% tbls){ - results <- c(results, "Time To Event") + results <- rbind( + results, + c("Time-to-event",'Exposed Cases Summary', 'exposedCasesPanel') + ) } # check incidence @@ -239,8 +588,136 @@ getCharacterizationTypes <- function( resultDatabaseSettings$incidenceTablePrefix, 'incidence_summary' ) %in% tbls){ - results <- c(results, "Incidence Rate") + results <- rbind( + results, + c("Incidence Results",'Cohort Incidence', 'cohortIncidencePanel') + ) } + + + return(list( + mainPanel = unique(results[,2]), + subPanel = unique(results[,1]) + )) +} + +# TODO add tte and dechal as include options +characterizationGetOptions <- function( + connectionHandler, + resultDatabaseSettings, + includeAggregate, + includeIncidence + ){ + + # get cohorts + cg <- connectionHandler$queryDb( + sql = 'select * from @schema.@cg_table_prefixcohort_definition + ORDER BY cohort_name;', + schema = resultDatabaseSettings$schema, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix + ) + +# TODO: CohortIncidence does not caputre specific T-O pairs, will need to implement +# if this function requires it. + TnOsSql = " +select distinct temp.*, c.cohort_name +from ( +{@include_aggregate} ? { +select distinct +target_cohort_id, +outcome_cohort_id +from @schema.@c_table_prefixcohort_details +where cohort_type = 'Cases' + +{@include_incidence} ? { +union +} +} + +{@include_incidence} ? { +select target_cohort_definition_id as target_cohort_id, outcome_cohort_definition_id as outcome_cohort_id + from @schema.@ci_table_prefixtarget_def, @schema.@ci_table_prefixoutcome_def +} + +) temp +inner join @schema.@cg_table_prefixcohort_definition c on temp.outcome_cohort_id = c.cohort_definition_id +;" + TnOs <- connectionHandler$queryDb( + sql = TnOsSql, + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix, + ci_table_prefix = resultDatabaseSettings$incidenceTablePrefix, + include_incidence = includeIncidence, + include_aggregate = includeAggregate + ) + # fix backwards compatability + if(!'isSubset' %in% colnames(cg)){ + cg$isSubset <- NA + } + if(!'subsetParent' %in% colnames(cg)){ + cg$subsetParent <- cg$cohortDefinitionId + } + if(!'subsetDefinitionId' %in% colnames(cg)){ + cg$subsetDefinitionId <- cg$cohortDefinitionId + } + cg$subsetParent[is.na(cg$isSubset)] <- cg$cohortDefinitionId + cg$subsetDefinitionId[is.na(cg$isSubset)] <- cg$cohortDefinitionId + cg$isSubset[is.na(cg$isSubset)] <- 0 + + parents <- unique(cg$cohortDefinitionId[cg$isSubset == 0]) + results <- lapply(parents, function(id){ + list( + cohortName = cg$cohortName[cg$cohortDefinitionId == id], + cohortId = id, + children = lapply(cg$cohortDefinitionId[cg$subsetParent == id], function(sid){ + list( + subsetName = cg$cohortName[cg$cohortDefinitionId == sid], + subsetId = sid, + outcomeIds = unique(TnOs$outcomeCohortId[TnOs$targetCohortId == sid]), + outcomeNames = unique(TnOs$cohortName[TnOs$targetCohortId == sid]) + # add outcomes from case exposures + ) + } + ) + ) + }) + return(results) + +} + +characterizationGetParents <- function(options){ + parentTs <- unlist(lapply(options, function(x) x$cohortId)) + names(parentTs) <- unlist(lapply(options, function(x) x$cohortName)) + + return(parentTs) +} + +characterizationGetChildren <- function(options, index){ + children <- unlist(lapply(options[[index]]$children, function(x) x$subsetId)) + names(children) <- unlist(lapply(options[[index]]$children, function(x) x$subsetName)) + + return(children) } + +characterizationGetOutcomes <- function(options, index){ + result <- unique( + do.call( + 'rbind', + lapply( + X = options[[index]]$children, + FUN = function(x) data.frame(ids = x$outcomeIds, names = x$outcomeNames) + ) + ) + ) + + outcomes <- result$ids + names(outcomes) <- result$names + + # sort the outcomes alphabetically + outcomes <- outcomes[order(names(outcomes))] + return(outcomes) +} + diff --git a/R/characterization-riskFactors.R b/R/characterization-riskFactors.R new file mode 100644 index 00000000..7623c0e9 --- /dev/null +++ b/R/characterization-riskFactors.R @@ -0,0 +1,1004 @@ +# @file characterization-aggregateFeatures.R +# +# Copyright 2024 Observational Health Data Sciences and Informatics +# +# This file is part of OhdsiShinyModules +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + + +characterizationRiskFactorViewer <- function(id) { + ns <- shiny::NS(id) + + shiny::div( + + # module that does input selection for a single row DF + shiny::uiOutput(ns("inputs")), + + shiny::conditionalPanel( + condition = 'input.generate != 0', + ns = ns, + + inputSelectionDfViewer(id = ns('inputSelected'), title = 'Selected'), + + shinydashboard::box( + title = 'Counts', + width = "100%", + collapsible = T, + resultTableViewer(ns('countTable')) + ), + + shinydashboard::tabBox( + width = "100%", + # Title can include an icon + title = shiny::tagList(shiny::icon("gear"), "Risk Factors"), + + shiny::tabPanel("Binary Feature Table", + resultTableViewer(ns('binaryTable')) + ), + shiny::tabPanel("Continuous Feature Table", + resultTableViewer(ns('continuousTable')) + ) + ) + ) + ) + +} + + + +characterizationRiskFactorServer <- function( + id, + connectionHandler, + resultDatabaseSettings, + targetId, #reactive + outcomeId #reactive +) { + shiny::moduleServer( + id, + function(input, output, session) { + + # get databases + options <- shiny::reactive({ + characterizationGetCaseSeriesOptions( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetId = targetId(), + outcomeId = outcomeId() + ) + }) + + output$inputs <- shiny::renderUI({ # need to make reactive? + + shiny::div( + shiny::selectInput( + inputId = session$ns('databaseId'), + label = 'Database: ', + choices = options()$databaseIds, + selected = options()$databaseIds[1], + multiple = F + ), + + shiny::selectInput( + inputId = session$ns('tarInd'), + label = 'Time-at-risk: ', + choices = options()$tarInds, + selected = options()$tarInds[1], + multiple = F + ), + + shiny::actionButton( + inputId = session$ns('generate'), + label = 'Generate' + ) + ) + + }) + + # save the selections + selected <- shiny::reactiveVal(value = NULL) + + shiny::observeEvent(input$generate, { + + selected( + data.frame( + database = names(options()$databaseIds)[which(input$databaseId == options()$databaseIds)], + time_at_risk = names(options()$tarInds)[which(input$tarInd == options()$tarInds)] + ) + ) + + inputSelectionDfServer( + id = 'inputSelected', + dataFrameRow = selected, + ncol = 1 + ) + + counts <- characterizationGetRiskFactorCounts( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetId = targetId(), + outcomeId = outcomeId(), + databaseId = input$databaseId, + tar = options()$tarList[[which(options()$tarInds == input$tarInd)]] + ) + + countTableOutput <- resultTableServer( + id = "countTable", + df = counts, + details = data.frame( + target = options()$targetName, + outcome = options()$outcomeName, + Database = names(options()$databaseIds)[which(input$databaseId == options()$databaseIds)], + TimeAtRisk = options()$tarList[[which(options()$tarInds == input$tarInd)]], + Analysis = 'Counts - Risk Factor' + ), + downloadedFileName = 'risk_factor_counts', + colDefsInput = characteriationCountsColDefs( + elementId = session$ns('count-table-filter') + ), + addActions = NULL, + elementId = session$ns('count-table-filter') + ) + + allData <- characterizationGetRiskFactorData( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetId = targetId(), + outcomeId = outcomeId(), + databaseId = input$databaseId, + tar = options()$tarList[[which(options()$tarInds == input$tarInd)]] + ) + + binTableOutputs <- resultTableServer( + id = "binaryTable", + df = allData$binary, + details = data.frame( + target = options()$targetName, + outcome = options()$outcomeName, + Database = names(options()$databaseIds)[which(input$databaseId == options()$databaseIds)], + TimeAtRisk = options()$tarList[[which(options()$tarInds == input$tarInd)]], + Analysis = 'Exposed Cases Summary - Risk Factor' + ), + downloadedFileName = 'risk_factor_binary', + colDefsInput = characteriationRiskFactorColDefs( + elementId = session$ns('binary-table-filter') + ), # function below + addActions = NULL, + elementId = session$ns('binary-table-filter') + ) + + conTableOutputs <- resultTableServer( + id = "continuousTable", + df = allData$continuous, + colDefsInput = characteriationRiskFactorContColDefs( + elementId = session$ns('continuous-table-filter') + ), # function below + addActions = NULL, + elementId = session$ns('continuous-table-filter') + ) + + }) + + return(invisible(NULL)) + } + ) +} + + +characterizationGetRiskFactorCounts <- function( + connectionHandler, + resultDatabaseSettings, + targetId, + outcomeId, + databaseId, + tar +){ + + sql <- "SELECT + cohort_type, + min_prior_observation, + outcome_washout_days, + row_count, + person_count + + from + @schema.@c_table_prefixcohort_counts + where database_id = '@database_id' + and target_cohort_id = @target_id + and outcome_cohort_id in (@outcome_id, 0) + and (risk_window_start = @risk_window_start OR risk_window_start is NULL) + and (risk_window_end = @risk_window_end OR risk_window_end is NULL) + and (start_anchor = '@start_anchor' OR start_anchor is NULL) + and (end_anchor = '@end_anchor' OR end_anchor is NULL) + and cohort_type in ('Cases','Exclude','Target') + ;" + + counts <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + database_id = databaseId, + target_id = targetId, + outcome_id = outcomeId, + risk_window_start = tar$riskWindowStart, + start_anchor = tar$startAnchor, + risk_window_end = tar$riskWindowEnd, + end_anchor = tar$endAnchor + ) + + return(counts) + +} + +characterizationGetRiskFactorData <- function( + connectionHandler, + resultDatabaseSettings, + targetId, + outcomeId, + databaseId, + tar +){ + + shiny::withProgress(message = 'Getting risk factor data', value = 0, { + shiny::incProgress(1/4, detail = paste("Extracting ids")) + + sql <- "SELECT distinct setting_id + from + @schema.@c_table_prefixsettings + where database_id = '@database_id' + and risk_window_start = @risk_window_start + and risk_window_end = @risk_window_end + and start_anchor = '@start_anchor' + and end_anchor = '@end_anchor' + + union + + SELECT distinct setting_id + from + @schema.@c_table_prefixsettings + where database_id = '@database_id' + and risk_window_start is NULL + ;" + + ids <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + database_id = databaseId, + risk_window_start = tar$riskWindowStart, + start_anchor = tar$startAnchor, + risk_window_end = tar$riskWindowEnd, + end_anchor = tar$endAnchor + ) + + shiny::incProgress(2/4, detail = paste("Extracting binary")) + + sql <- "SELECT distinct cov.cohort_type, cr.covariate_name, + s.min_prior_observation, s.outcome_washout_days, + cov.covariate_id, cov.sum_value, cov.average_value + from + @schema.@c_table_prefixcovariates cov + inner join @schema.@c_table_prefixcovariate_ref cr + on cov.setting_id = cr.setting_id and + cov.database_id = cr.database_id and + cov.covariate_id = cr.covariate_id + + inner join @schema.@c_table_prefixsettings s + on cov.setting_id = s.setting_id + and cov.database_id = s.database_id + + where + cov.target_cohort_id = @target_id + and cov.outcome_cohort_id in (0,@outcome_id) + and cov.cohort_type in ('Target','Cases', 'Exclude') + and cov.database_id = '@database_id' + and cov.setting_id in (@setting_ids) + and cr.analysis_id not in (109, 110, 217, 218, 305, 417, 418, 505, 605, 713, 805, 926, 927) + ;" + + binary <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + target_id = targetId, + outcome_id = outcomeId, + database_id = databaseId, + setting_ids = paste0(paste0("'",ids$settingId, "'"), collapse=',') + ) + message(paste0('Extracted ',nrow(binary),' binary RF rows')) + + # now process into table + binary <- riskFactorTable( + data = binary + ) + + shiny::incProgress(3/4, detail = paste("Extracting continuous")) + + sql <- "SELECT distinct cov.cohort_type, cr.covariate_name, + s.min_prior_observation, s.outcome_washout_days,cov.covariate_id, + cov.count_value, cov.min_value, cov.max_value, cov.average_value, + cov.standard_deviation, cov.median_value, cov.p_10_value, + cov.p_25_value, cov.p_75_value, cov.p_90_value + from + @schema.@c_table_prefixcovariates_continuous cov + inner join @schema.@c_table_prefixcovariate_ref cr + on cov.setting_id = cr.setting_id and + cov.database_id = cr.database_id and + cov.covariate_id = cr.covariate_id + + inner join @schema.@c_table_prefixsettings s + on cov.setting_id = s.setting_id + and cov.database_id = s.database_id + + where cov.target_cohort_id = @target_id + and cov.outcome_cohort_id in (0,@outcome_id) + and cov.cohort_type in ('Target','Cases', 'Exclude') + and cov.database_id = '@database_id' + and cov.setting_id in (@setting_ids) + and cr.analysis_id not in (109, 110, 217, 218, 305, 417, 418, 505, 605, 713, 805, 926, 927) + ;" + + # TODO - how to remove prior outcomes?? + continuous <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + target_id = targetId, + outcome_id = outcomeId, + database_id = databaseId, + setting_ids = paste0(paste0("'",ids$settingId, "'"), collapse=',') + ) + + message(paste0('Extracted ',nrow(binary),' continuous RF rows')) + + continuous <- riskFactorContinuousTable( + data = continuous + ) + + shiny::incProgress(4/4, detail = paste("Done")) + + }) + + return( + list( + binary = binary, + continuous = continuous + ) + ) +} + + +# now process into table +riskFactorTable <- function( + data +){ + + if(is.null(data)){ + return(data) + } + + data <- unique(data) + if(nrow(data) == 0){ + return(data) + } + + outcomeWashoutDays <- unique(data$outcomeWashoutDays) + outcomeWashoutDays <- outcomeWashoutDays[!is.na(outcomeWashoutDays)] + if(length(outcomeWashoutDays) == 0){ + shiny::showNotification('No cases') + data <- data %>% + dplyr::filter(.data$cohortType == 'Target') %>% + dplyr::select(-"cohortType", -"outcomeWashoutDays") %>% + dplyr::mutate( + nonCaseSumValue = .data$sumValue, + nonCaseAverageValue = .data$averageValue + ) %>% + dplyr::select( + "covariateId","covariateName", + 'minPriorObservation', + "nonCaseSumValue","nonCaseAverageValue" + ) + return(data) + } + + targetData <- data %>% + dplyr::filter(.data$cohortType == 'Target') %>% + dplyr::mutate( + sumValue_Target = .data$sumValue, + averageValue_Target = .data$averageValue + ) %>% + dplyr::select( + -"cohortType", + -"outcomeWashoutDays", + -"sumValue", + -"averageValue" + ) + + targetN <- targetData %>% + dplyr::mutate(N_Target = .data$sumValue_Target/.data$averageValue_Target) %>% + dplyr::select('minPriorObservation', 'N_Target') %>% + dplyr::group_by(.data$minPriorObservation) %>% + dplyr::summarise(N_Target = round(max(.data$N_Target, na.rm = T))) + + completeData <- c() + for(outcomeWashoutDay in outcomeWashoutDays){ + + # add dummy Cases and Exclude to data so columns always exist + data <- rbind(data, data.frame( + cohortType = c('Cases','Exclude'), + covariateName = rep('NA', 2), + minPriorObservation = rep(unique(data$minPriorObservation)[1], 2), + outcomeWashoutDays = rep(outcomeWashoutDay, 2), + covariateId = rep(-1, 2), + sumValue = rep(0,2), + averageValue = rep(0,2) + )[colnames(data)]) + + #filter data to outcomeWashoutDays + otherData <- data %>% + dplyr::filter( + .data$cohortType != 'Target' & + .data$outcomeWashoutDays == !!outcomeWashoutDay + ) %>% + tidyr::pivot_wider( + id_cols = c( + "minPriorObservation", + "covariateId", + "covariateName" + ), + names_from = "cohortType", + values_from = c("sumValue","averageValue") + ) + + otherN <- otherData %>% + dplyr::mutate( + N_Cases = .data$sumValue_Cases/.data$averageValue_Cases, + N_Exclude = .data$sumValue_Exclude/.data$averageValue_Exclude + ) %>% + dplyr::group_by(.data$minPriorObservation) %>% + dplyr::summarise( + N_Cases = max(.data$N_Cases, na.rm = T), + N_Exclude = max(.data$N_Exclude, na.rm = T) + ) + + if(length(is.infinite(otherN$N_Cases))>0){ + otherN$N_Cases[is.infinite(otherN$N_Cases)] <- 0 + } + if(length(is.infinite(otherN$N_Exclude))>0){ + otherN$N_Exclude[is.infinite(otherN$N_Exclude)] <- 0 + } + + # get all counts + counts <- targetN %>% + dplyr::left_join(otherN, by = c('minPriorObservation')) + + # get final data for minPriorObs + finalData <- targetData %>% + dplyr::left_join( + otherData, + by = c( + "minPriorObservation", + "covariateId", + "covariateName" + ) + ) %>% + dplyr::inner_join( + counts , + by = c( + "minPriorObservation" + ) + ) + if(length(is.na(finalData$sumValue_Cases))>0){ + finalData$sumValue_Cases[is.na(finalData$sumValue_Cases)] <- 0 + } + if(length(is.na(finalData$sumValue_Target))>0){ + finalData$sumValue_Target[is.na(finalData$sumValue_Target)] <- 0 + } + if(length(is.na(finalData$sumValue_Exclude))>0){ + finalData$sumValue_Exclude[is.na(finalData$sumValue_Exclude)] <- 0 + } + if(length(is.na(finalData$N_Target))>0){ + finalData$N_Target[is.na(finalData$N_Target)] <- 0 + } + if(length(is.na(finalData$N_Cases))>0){ + finalData$N_Cases[is.na(finalData$N_Cases)] <- 0 + } + if(length(is.na(finalData$N_Exclude))>0){ + finalData$N_Exclude[is.na(finalData$N_Exclude)] <- 0 + } + if(length(is.na(finalData$averageValue_Cases))>0){ + finalData$averageValue_Cases[is.na(finalData$averageValue_Cases)] <- 0 + } + + # removing censored counts as dont want to add due to negative + if(length(finalData$N_Exclude < 0) > 0 ){ + finalData$N_Exclude[finalData$N_Exclude < 0] <- 0 + } + finalData$N_Cases_exclude <- finalData$N_Cases + if(length(finalData$N_Cases_exclude < 0) > 0 ){ + finalData$N_Cases_exclude[finalData$N_Cases_exclude < 0] <- 0 + } + if(length(finalData$sumValue_Exclude < 0) > 0 ){ + finalData$sumValue_Exclude[finalData$sumValue_Exclude < 0] <- 0 + } + finalData$sumValue_Cases_exclude <- finalData$sumValue_Cases + if(length(finalData$sumValue_Cases_exclude < 0) > 0 ){ + finalData$sumValue_Cases_exclude[finalData$sumValue_Cases_exclude < 0] <- 0 + } + + finalData <- finalData %>% + dplyr::mutate( + nonCaseN = round(.data$N_Target-.data$N_Exclude-.data$N_Cases_exclude), + caseN = .data$N_Cases, + N = .data$N_Target, + nonCaseSumValue = .data$sumValue_Target-.data$sumValue_Exclude-.data$sumValue_Cases_exclude, + caseSumValue = .data$sumValue_Cases, + nonCaseAverageValue = (.data$sumValue_Target-.data$sumValue_Exclude-.data$sumValue_Cases_exclude)/(.data$N_Target-.data$N_Exclude-.data$N_Cases_exclude), + caseAverageValue = .data$averageValue_Cases + ) %>% + dplyr::select( + "covariateId", "covariateName", "minPriorObservation", + "caseSumValue","caseAverageValue", + "nonCaseSumValue","nonCaseAverageValue", + "nonCaseN", "caseN", "N" + ) %>% + dplyr::mutate( + meanDiff = .data$caseAverageValue - .data$nonCaseAverageValue, + std1 = ifelse(.data$caseN == 0, 0 ,sqrt(((1-.data$caseAverageValue)^2*.data$caseSumValue + (-.data$caseAverageValue)^2*(.data$caseN - .data$caseSumValue))/.data$caseN)), + std2 = ifelse(.data$nonCaseN == 0, 0, sqrt(((1-.data$nonCaseAverageValue)^2*.data$nonCaseSumValue + (-.data$nonCaseAverageValue)^2*(.data$nonCaseN - .data$nonCaseSumValue))/.data$nonCaseN)) + ) %>% + dplyr::mutate( + SMD = .data$meanDiff/sqrt((.data$std1^2 + .data$std2^2)/2), + absSMD = abs(.data$meanDiff/sqrt((.data$std1^2 + .data$std2^2)/2)) + ) %>% + dplyr::select(-"meanDiff",-"std1", -"std2", -"N",-"caseN", -"nonCaseN") + + + # add outcomewashout back here + finalData <- finalData %>% + dplyr::mutate( + outcomeWashoutDays = !!outcomeWashoutDay + ) %>% + dplyr::relocate("outcomeWashoutDays", + .after = "minPriorObservation") + + completeData <- rbind(finalData, completeData) + + } # end outcomeWashoutDays loop + + if(nrow(completeData) == 0){ + completeData <- data %>% + dplyr::filter(.data$cohortType == 'Target') %>% + dplyr::select(-"cohortType", -"outcomeWashoutDays") %>% + dplyr::mutate( + nonCaseSumValue = .data$sumValue, + nonCaseAverageValue = .data$averageValue + ) %>% + dplyr::select( + "covariateId","covariateName", + 'minPriorObservation', + "nonCaseSumValue","nonCaseAverageValue" + ) + } + + return(unique(completeData)) +} + + +riskFactorContinuousTable <- function( + data +){ + + + data <- unique(data) + + caseData <- data %>% + dplyr::filter(.data$cohortType == 'Cases') %>% + dplyr::select(-"cohortType") + + allData <- data %>% + dplyr::filter(.data$cohortType == 'Target') %>% + dplyr::select(-"cohortType", -"outcomeWashoutDays") + + if(nrow(caseData) > 0){ + + caseData <- caseData %>% + dplyr::mutate( + caseCountValue = .data$countValue, + caseAverageValue = .data$averageValue, + caseStandardDeviation = .data$standardDeviation, + caseMedianValue = .data$medianValue, + caseMinValue = .data$minValue, + caseMaxValue = .data$maxValue, + caseP10Value = .data$p10Value, + caseP25Value = .data$p25Value, + caseP75Value = .data$p75Value, + caseP90Value = .data$p90Value + ) %>% + dplyr::select("covariateId","covariateName", + 'minPriorObservation', 'outcomeWashoutDays', + "caseCountValue","caseAverageValue", + "caseStandardDeviation", "caseMedianValue", "caseP10Value", "caseP25Value", + "caseP75Value", "caseP90Value", "caseMaxValue", "caseMinValue") + + # join with cases + allData <- allData %>% + dplyr::full_join(caseData, by = c('covariateId', 'covariateName', 'minPriorObservation')) %>% + dplyr::mutate( + targetCountValue = .data$countValue, + targetAverageValue = .data$averageValue, + targetStandardDeviation = .data$standardDeviation, + targetMedianValue = .data$medianValue, + targetMinValue = .data$minValue, + targetMaxValue = .data$maxValue, + targetP10Value = .data$p10Value, + targetP25Value = .data$p25Value, + targetP75Value = .data$p75Value, + targetP90Value = .data$p90Value + ) %>% + dplyr::select("covariateId","covariateName", + 'minPriorObservation', 'outcomeWashoutDays', + "caseCountValue","caseAverageValue", + "caseStandardDeviation", "caseMedianValue", "caseP10Value", "caseP25Value", + "caseP75Value", "caseP90Value", "caseMaxValue", "caseMinValue", + + "targetCountValue","targetAverageValue", + "targetStandardDeviation", "targetMedianValue", "targetP10Value", "targetP25Value", + "targetP75Value", "targetP90Value","targetMaxValue", "targetMinValue",) + + # add abs smd + allData <- allData %>% + dplyr::mutate( + SMD = (.data$caseAverageValue - .data$targetAverageValue)/sqrt((.data$caseStandardDeviation^2 + .data$targetStandardDeviation^2)/2), + absSMD = abs((.data$caseAverageValue - .data$targetAverageValue)/sqrt((.data$caseStandardDeviation^2 + .data$targetStandardDeviation^2)/2)), + targetBoxPlot = 0, + caseBoxPlot = 0 + ) + + + } + + + return(unique(allData)) + +} + +characteriationCountsColDefs <- function( + elementId +){ + result <- list( + cohortType = reactable::colDef( + header = withTooltip("Cohort Type", + "The target popualtion, exclusions from target or cases"), + filterable = T + ), + + rowCount = reactable::colDef( + header = withTooltip("# rows", + "Number of exposures in the cohort (people can be in more than once)"), + cell = function(value) { + if(is.null(value)){return('< min threshold')} + if(is.na(value)){return('< min threshold')} + if (value >= 0) value else paste0('< ', abs(value)) + } + ), + personCount = reactable::colDef( + header = withTooltip("# persons", + "Number of distinct people in the cohort"), + cell = function(value) { + if(is.null(value)){return('< min threshold')} + if(is.na(value)){return('< min threshold')} + if (value >= 0) value else paste0('< ', abs(value)) + } + ) + ) + return(result) +} + +characteriationRiskFactorColDefs <- function( + elementId + ){ + result <- list( + covariateId = reactable::colDef( + show = F + ), + covariateName = reactable::colDef( + header = withTooltip("Covariate Name", + "Name of the covariate"), + filterable = T, + minWidth = 300 + ), + minPriorObservation = reactable::colDef( + header = withTooltip("Min Prior Observation", + "Minimum prior observation time (days)"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + outcomeWashoutDays = reactable::colDef( + header = withTooltip("Outcome Washout Days", + "Number of days for the outcome washout"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + nonCaseSumValue = reactable::colDef( + header = withTooltip("# Non-cases with Feature Before Exposure", + "Number of non-cases for the outcome with the feature before exposure"), + filterable = T, + format = reactable::colFormat( + percent = F, + separators = TRUE + ), + cell = function(value) { + if(is.null(value)){return('< min threshold')} + if(is.na(value)){return('< min threshold')} + if (value >= 0) value else paste0('< ', abs(value)) + } + ), + caseSumValue = reactable::colDef( + header = withTooltip("# Cases with Feature Before Exposure", + "Number of cases for the outcome with the feature before exposure"), + filterable = T, + format = reactable::colFormat( + separators = TRUE, + percent = F + ), + cell = function(value) { + if(is.null(value)){return('< min threshold')} + if(is.na(value)){return('< min threshold')} + if (value >= 0) value else paste0('< ', abs(value)) + } + ), + nonCaseAverageValue = reactable::colDef( + header = withTooltip("% Non-cases with Feature Before Exposure", + "Percent of non-cases for the outcome with the feature before exposure"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = T) + ), + caseAverageValue = reactable::colDef( + header = withTooltip("% Cases with Feature Before Exposure", + "Percent of Cases for the outcome with the feature before exposure"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = T) + ), + + SMD = reactable::colDef( + header = withTooltip("SMD", + "Standardized mean difference"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + + absSMD = reactable::colDef( + header = withTooltip("absSMD", + "Absolute value of standardized mean difference"), + format = reactable::colFormat(digits = 2, percent = F), + filterable = TRUE, + filterMethod = reactable::JS("function(rows, columnId, filterValue) { + return rows.filter(function(row) { + return row.values[columnId] >= filterValue + }) + }"), + filterInput = function(values, name) { + oninput <- sprintf("Reactable.setFilter('%s', '%s', this.value)", elementId, name) + shiny::tags$input( + type = "range", + min = floor(min(values, na.rm = T)), + max = ceiling(max(values, na.rm = T)), + value = floor(min(values, na.rm = T)), + oninput = oninput, + onchange = oninput, # For IE11 support + "aria-label" = sprintf("Filter by minimum %s", name) + ) + } + ) + ) + return(result) +} + + + +characteriationRiskFactorContColDefs <- function( + elementId + ){ + result <- list( + covariateName = reactable::colDef( + header = withTooltip("Covariate Name", + "Name of the covariate"), + filterable = T, + minWidth = 300 + ), + covariateId = reactable::colDef( + show = F + ), + minPriorObservation = reactable::colDef( + header = withTooltip("Min Prior Observation", + "Minimum prior observation time (days)"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + outcomeWashoutDays = reactable::colDef( + header = withTooltip("Outcome Washout Days", + "Number of days for the outcome washout"), + filterable = T, + filterInput = function(values, name) { + shiny::tags$select( + # Set to undefined to clear the filter + onchange = sprintf("Reactable.setFilter('%s', '%s', event.target.value || undefined)", elementId, name), + # "All" has an empty value to clear the filter, and is the default option + shiny::tags$option(value = "", "All"), + lapply(unique(values), shiny::tags$option), + "aria-label" = sprintf("Filter %s", name), + style = "width: 100%; height: 28px;" + ) + } + ), + countValue = reactable::colDef( + header = withTooltip("# with Feature", + "Number with feature"), + filterable = T + , + format = reactable::colFormat( + percent = F, + separators = TRUE + ), + cell = function(value) { + if(is.null(value)){return('< min threshold')} + if(is.na(value)){return('< min threshold')} + if (value >=0) value else paste0('< ', abs(value)) + } + ), + averageValue = reactable::colDef( + header = withTooltip("Mean Feature Value", + "Mean value of the feature in the population"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + standardDeviation = reactable::colDef( + header = withTooltip("SD Feature Value", + "Standard deviation of the feature value in the population"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + medianValue = reactable::colDef( + header = withTooltip("Median Feature Value", + "Median of the feature value"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + p10Value = reactable::colDef( + header = withTooltip("10th %ile Feature Value", + "10th percentile of the feature value"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + p25Value = reactable::colDef( + header = withTooltip("25th %tile Feature Value", + "25th percentile of the feature value"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + p75Value = reactable::colDef( + header = withTooltip("75th %tile Feature Value", + "75th percentile of the feature value"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + p90Value = reactable::colDef( + header = withTooltip("90th %tile Feature Value", + "90th percentile of the feature value"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + maxValue = reactable::colDef( + header = withTooltip("Max Feature Value", + "Maximum of the feature value"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + minValue = reactable::colDef( + header = withTooltip("Min Feature Value", + "Minimum of the feature value"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + boxPlot = reactable::colDef( + show = F + ), + #targetBoxPlot = reactable::colDef(cell = function(value, index) { + # ggplot2::ggplot() + + # ggplot2::geom_boxplot( + # ggplot2::aes( + # x = 1, + # ymin = data$targetMinValue[index], + # lower = data$targetP10Value[index], + # middle = data$targetMedianValue[index], + # upper = data$targetP90Value[index], + # ymax = data$targetMaxValue[index] + # ), + # stat = "identity" + # ) + #}), + #caseBoxPlot = reactable::colDef(cell = function(value, index) { + # sparkline(vcs_boxp_data$em_red_per_th[[index]], type = "box") + # }), + #caseBoxPlot = reactable::colDef(cell = function(value, index) { + # sparkline::sparkline(vcs_boxp_data$em_red_per_th[[index]], type = "box") + # }), + + # low_outlier, low_whisker, q1, median, q3, high_whisker, high_outlier + #sparkline::spk_chr(c(data$targetMinValue[index], data$targetP10Value[index], data$targetP25Value[index], data$targetMedianValue[index], 3, 6, 6), type="box", raw = TRUE, width = 200) + + SMD = reactable::colDef( + header = withTooltip("SMD", + "Standardized mean difference"), + filterable = T, + format = reactable::colFormat(digits = 2, percent = F) + ), + absSMD = reactable::colDef( + header = withTooltip("absSMD", + "Absolute value of the standardized mean difference"), + format = reactable::colFormat(digits = 2, percent = F), + filterable = TRUE, + filterMethod = reactable::JS("function(rows, columnId, filterValue) { + return rows.filter(function(row) { + return row.values[columnId] >= filterValue + }) + }"), + filterInput = function(values, name) { + oninput <- sprintf("Reactable.setFilter('%s', '%s', this.value)", elementId, name) + shiny::tags$input( + type = "range", + min = floor(min(values, na.rm = T)), + max = ceiling(max(values, na.rm = T)), + value = floor(min(values, na.rm = T)), + oninput = oninput, + onchange = oninput, # For IE11 support + "aria-label" = sprintf("Filter by minimum %s", name) + ) + } + ) + ) + return(result) +} + diff --git a/R/characterization-timeToEvent.R b/R/characterization-timeToEvent.R index 8b0f6041..abfb322b 100644 --- a/R/characterization-timeToEvent.R +++ b/R/characterization-timeToEvent.R @@ -17,165 +17,207 @@ # limitations under the License. -#' The module viewer for exploring time to event results -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' -#' @return -#' The user interface to the characterization time to event module -#' -#' @export characterizationTimeToEventViewer <- function(id) { ns <- shiny::NS(id) shiny::div( - infoHelperViewer( - id = "helper", - helpLocation= system.file("characterization-www", "help-timeToEvent.html", package = utils::packageName()) - ), - - # input component module - inputSelectionViewer(id = ns('input-selection')), - - shiny::conditionalPanel( - condition = 'input.generate != 0', - ns = shiny::NS(ns("input-selection")), + shiny::tabsetPanel( + type = 'pills', + id = ns('tteMainPanel'), + shiny::tabPanel( + title = "Time-to-event Plots", + shinydashboard::box( width = "100%", - title = shiny::tagList(shiny::icon("gear"), "Results"), + title = "", - shiny::fluidRow( - shiny::column( - width = 2, - shiny::uiOutput(ns('timeToEventPlotInputs')) - ), - shiny::column( - width = 10, - shinycssloaders::withSpinner( - shiny::plotOutput(ns('timeToEvent')) - ) + shiny::uiOutput(ns('timeToEventPlotInputs')), + shinycssloaders::withSpinner( + shiny::plotOutput(ns('timeToEvent')) ) ) + ), + + shiny::tabPanel( + title = "Time-to-event Table", + + shinydashboard::box( + status = 'info', + width = '100%', + solidHeader = TRUE, + resultTableViewer(ns('tableResults')) + ) ) ) - - ) } -#' The module server for exploring time to event results -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' @param connectionHandler the connection to the prediction result database -#' @param resultDatabaseSettings a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix -#' -#' @return -#' The server to the prediction time to event module -#' -#' @export characterizationTimeToEventServer <- function( id, connectionHandler, - resultDatabaseSettings + resultDatabaseSettings, + targetId, + outcomeId ) { shiny::moduleServer( id, function(input, output, session) { - # get the possible target ids - bothIds <- timeToEventGetIds( - connectionHandler, - resultDatabaseSettings - ) - - - # input selection component - inputSelected <- inputSelectionServer( - id = "input-selection", - inputSettingList = list( - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'targetId', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Target: ', - choices = bothIds$targetIds, - #choicesOpt = list(style = rep_len("color: black;", 999)), - selected = bothIds$targetIds[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'outcomeId', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Outcome: ', - choices = bothIds$outcomeIds, - #choicesOpt = list(style = rep_len("color: black;", 999)), - selected = bothIds$outcomeIds[1], - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ) + options <- shiny::reactive({ + characterizationGetCaseSeriesOptions( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetId = targetId(), + outcomeId = outcomeId() ) - ) + }) allData <- shiny::reactive({ getTimeToEventData( - targetId = inputSelected()$targetId, - outcomeId = inputSelected()$outcomeId, + targetId = targetId(), + outcomeId = outcomeId(), connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings - ) + ) %>% + dplyr::mutate(targetName = options()$targetName, + outcomeName = options()$outcomeName) %>% + dplyr::relocate("databaseName", .before = "databaseId") %>% + dplyr::relocate("targetName", .after = "databaseName") %>% + dplyr::relocate("outcomeName", .after = "targetName") }) + + + characterizationTimeToEventColDefs <- function(){ + result <- list( + databaseName = reactable::colDef( + header = withTooltip("Database", + "Name of the database"), + filterable = T + ), + databaseId = reactable::colDef( + header = withTooltip("Database ID", + "Unique ID of the database"), + filterable = T, + show = F + ), + targetCohortDefinitionId = reactable::colDef( + header = withTooltip("Target ID", + "Unique ID of the target cohort"), + filterable = T, + show = F + ), + targetName = reactable::colDef( + header = withTooltip("Target Name", + "Name of the target cohort"), + filterable = T + ), + outcomeCohortDefinitionId = reactable::colDef( + header = withTooltip("Outcome ID", + "Unique ID of the outcome cohort"), + filterable = T, + show = F + ), + outcomeName = reactable::colDef( + header = withTooltip("Outcome Name", + "Name of the outcome cohort"), + filterable = T + ), + outcomeType = reactable::colDef( + header = withTooltip("Outcome Type", + "Type of the outcome, either first or subsequent occurrence"), + filterable = T + ), + targetOutcomeType = reactable::colDef( + header = withTooltip("Target-Outcome Type", + "The timing of the event relative to the target era"), + filterable = T + ), + timeToEvent = reactable::colDef( + header = withTooltip("Time (in days) To Event", + "The time in days relative to target index until the event occurred"), + filterable = T + ), + numEvents = reactable::colDef( + header = withTooltip("# of Events", + "The number of events that occurred"), + filterable = T, + cell = function(value) { + # Add < if cencored + if (value < 0 ) paste("<", abs(value)) else value + } + ), + timeScale = reactable::colDef( + header = withTooltip("Time Scale", + "The time scale in which the events occurred"), + filterable = T + ) + ) + return(result) + } + + tableOutputs <- resultTableServer( + id = "tableResults", + df = allData, + details = data.frame( + target = options()$targetName, + outcome = options()$outcomeName, + Analysis = 'Exposed Cases Summary - Time-to-event' + ), + downloadedFileName = 'time_to_event', + colDefsInput = characterizationTimeToEventColDefs() + ) output$timeToEventPlotInputs <- shiny::renderUI({ shiny::fluidPage( shiny::fluidRow( - shiny::checkboxGroupInput( - inputId = session$ns("databases"), + shiny::selectInput( + inputId = session$ns("databases"), label = "Databases:", - choiceNames = unique(allData()$databaseName), - choiceValues = unique(allData()$databaseName), + multiple = T, + choices = unique(allData()$databaseName), selected = unique(allData()$databaseName) - ), - shiny::checkboxGroupInput( - inputId = session$ns("times"), - label = "Timespan:", - choiceNames = unique(allData()$timeScale), - choiceValues = unique(allData()$timeScale), - selected = unique(allData()$timeScale) + ), + + shiny::fluidRow( + shiny::column( + width = 3, + shiny::selectInput( + inputId = session$ns("times"), + label = "Timespan:", + multiple = T, + choices = unique(allData()$timeScale), + selected = unique(allData()$timeScale) + ) + ), + + shiny::column( + width = 3, + shiny::selectInput( + inputId = session$ns("outcomeTypes"), + label = "Outcome occurrence type:", + multiple = T, + choices = unique(allData()$outcomeType), + selected = unique(allData()$outcomeType) + ) + ), + + shiny::column( + width = 6, + shiny::selectInput( + inputId = session$ns("targetOutcomeTypes"), + label = "Timing of outcome:", + multiple = T, + choices = unique(allData()$targetOutcomeType), + selected = unique(allData()$targetOutcomeType) + ) + ) ) + ) ) } @@ -185,7 +227,9 @@ characterizationTimeToEventServer <- function( plotTimeToEvent( timeToEventData = allData, # reactive databases = input$databases, - times = input$times + times = input$times, + outcomeTypes = input$outcomeTypes, + targetOutcomeTypes = input$targetOutcomeTypes ) ) @@ -196,61 +240,6 @@ characterizationTimeToEventServer <- function( ) } -timeToEventGetIds <- function( - connectionHandler, - resultDatabaseSettings -){ - - shiny::withProgress(message = 'Getting time to event T and O ids', value = 0, { - - sql <- "SELECT DISTINCT - t.COHORT_NAME as target, TARGET_COHORT_DEFINITION_ID, - o.COHORT_NAME as outcome, OUTCOME_COHORT_DEFINITION_ID - FROM @schema.@c_table_prefixTIME_TO_EVENT tte - inner join @schema.@cg_table_prefixCOHORT_DEFINITION t - on tte.TARGET_COHORT_DEFINITION_ID = t.COHORT_DEFINITION_ID - inner join @schema.@cg_table_prefixCOHORT_DEFINITION o - on tte.OUTCOME_COHORT_DEFINITION_ID = o.COHORT_DEFINITION_ID - ;" - - - shiny::incProgress(1/4, detail = paste("Fetching ids")) - - bothIds <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - c_table_prefix = resultDatabaseSettings$cTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix - ) - - shiny::incProgress(3/4, detail = paste("Processing ids")) - - targetUnique <- bothIds %>% - dplyr::select(c("targetCohortDefinitionId", "target")) %>% - dplyr::distinct() - - targetIds <- targetUnique$targetCohortDefinitionId - names(targetIds) <- targetUnique$target - - outcomeUnique <- bothIds %>% - dplyr::select(c("outcomeCohortDefinitionId", "outcome")) %>% - dplyr::distinct() - - outcomeIds <- outcomeUnique$outcomeCohortDefinitionId - names(outcomeIds) <- outcomeUnique$outcome - - shiny::incProgress(4/4, detail = paste("Finished")) - - }) - - return( - list( - targetIds = targetIds, - outcomeIds = outcomeIds - ) - ) -} - # pulls all data for a target and outcome getTimeToEventData <- function( targetId, @@ -286,13 +275,17 @@ getTimeToEventData <- function( }) + #write.csv(data,'/Users/jreps/Documents/tte_data.csv') + return(data) } plotTimeToEvent <- function( timeToEventData, databases, - times + times, + outcomeTypes, + targetOutcomeTypes ){ if(is.null(timeToEventData())){ @@ -302,14 +295,31 @@ plotTimeToEvent <- function( timeToEventData <- timeToEventData() %>% dplyr::filter(.data$databaseName %in% databases) - if(is.null(timeToEventData)){ + if(nrow(timeToEventData) == 0){ + shiny::showNotification('No results for selected databases') return(NULL) } timeToEventData <- timeToEventData %>% dplyr::filter(.data$timeScale %in% times) - if(is.null(timeToEventData)){ + if(nrow(timeToEventData) == 0){ + shiny::showNotification('No results for selected databases and times') + return(NULL) + } + + # remove censored data + timeToEventData <- timeToEventData %>% + dplyr::filter( + .data$outcomeType %in% outcomeTypes & + .data$targetOutcomeType %in% targetOutcomeTypes & + .data$numEvents > 0 + ) + + # TODO plot censored as black? + + if(nrow(timeToEventData) == 0){ + shiny::showNotification('No results for selection') return(NULL) } @@ -320,7 +330,10 @@ plotTimeToEvent <- function( shiny::incProgress(1/2, detail = paste("Generating plot")) plot <- ggplot2::ggplot( - data = timeToEventData %>% dplyr::mutate(fillGroup = paste0(.data$outcomeType, '-', .data$targetOutcomeType)), + data = timeToEventData %>% + dplyr::mutate( + fillGroup = paste0(.data$outcomeType, '-', .data$targetOutcomeType) + ), ggplot2::aes( x = .data$timeToEvent, y = .data$numEvents, @@ -329,21 +342,14 @@ plotTimeToEvent <- function( ) ) + ggplot2::geom_bar( - #position="stacked", stat = "identity" ) + - #ggplot2::geom_text( - # ggplot2::aes( - # label = .data$numEvents - # ), - # vjust = 1.6, - # color = "white", - # size = 3.5 - # ) + ggplot2::facet_wrap(ncol = nDatabases , .data$timeScale ~ .data$databaseName , scales = 'free' ) + - ggplot2::theme_minimal() + ggplot2::theme_minimal() + + ggplot2::guides(fill=ggplot2::guide_legend(title="Outcome Type")) + + ggplot2::labs(y= "# of Events", x = "Time (days) to Event") shiny::incProgress(2/2, detail = paste("Finished")) diff --git a/R/cohort-diagnostics-characterization.R b/R/cohort-diagnostics-characterization.R index 02a88887..880eec4d 100644 --- a/R/cohort-diagnostics-characterization.R +++ b/R/cohort-diagnostics-characterization.R @@ -19,6 +19,7 @@ #' Use for customizing UI #' #' @param id Namespace Id - use namespaced id ns("characterization") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export cohortDiagCharacterizationView <- function(id) { ns <- shiny::NS(id) @@ -419,7 +420,7 @@ prepareTable1 <- function(covariates, "characteristic", "valueCount" ) %>% - dplyr::rename("count" = "valueCount") %>% + dplyr::rename(count = "valueCount") %>% dplyr::inner_join(cohort %>% dplyr::select( "cohortId", @@ -739,7 +740,7 @@ cohortDiagCharacterizationModule <- function( "covariateName", "mean" ) %>% - dplyr::rename("sumValue" = "mean") + dplyr::rename(sumValue = "mean") table <- data %>% diff --git a/R/cohort-diagnostics-cohort-overlap.R b/R/cohort-diagnostics-cohort-overlap.R index 016c5556..0d376642 100644 --- a/R/cohort-diagnostics-cohort-overlap.R +++ b/R/cohort-diagnostics-cohort-overlap.R @@ -172,6 +172,7 @@ plotCohortOverlap <- function(data, #' Use for customizing UI #' #' @param id Namespace Id - use namespaced id ns("cohortOverlap") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export cohortOverlapView <- function(id) { ns <- shiny::NS(id) @@ -358,7 +359,7 @@ getResultsCohortOverlap <- function(dataSource, dplyr::inner_join( cohortCounts %>% dplyr::select(-"cohortEntries") %>% - dplyr::rename("targetCohortSubjects" = "cohortSubjects"), + dplyr::rename(targetCohortSubjects = "cohortSubjects"), by = c("databaseId", "cohortId") ) %>% dplyr::mutate(tOnlySubjects = .data$targetCohortSubjects - .data$subjects) %>% @@ -366,8 +367,8 @@ getResultsCohortOverlap <- function(dataSource, cohortCounts %>% dplyr::select(-"cohortEntries") %>% dplyr::rename( - "comparatorCohortSubjects" = "cohortSubjects", - "comparatorCohortId" = "cohortId" + comparatorCohortSubjects = "cohortSubjects", + comparatorCohortId = "cohortId" ), by = c("databaseId", "comparatorCohortId") ) %>% diff --git a/R/cohort-diagnostics-compareCharacterization.R b/R/cohort-diagnostics-compareCharacterization.R index 58d5e177..6bcd45d3 100644 --- a/R/cohort-diagnostics-compareCharacterization.R +++ b/R/cohort-diagnostics-compareCharacterization.R @@ -246,6 +246,7 @@ plotTemporalCompareStandardizedDifference <- function(balance, #' #' @param id Namespace Id - use namespaced id ns("compareCohortCharacterization") inside diagnosticsExplorer module #' @param title Optional string title field +#' @family {CohortDiagnostics} #' @export compareCohortCharacterizationView <- function(id, title = "Compare cohort characterization") { ns <- shiny::NS(id) diff --git a/R/cohort-diagnostics-conceptsInDataSource.R b/R/cohort-diagnostics-conceptsInDataSource.R index 825e58aa..33819cbe 100644 --- a/R/cohort-diagnostics-conceptsInDataSource.R +++ b/R/cohort-diagnostics-conceptsInDataSource.R @@ -19,6 +19,7 @@ #' Use for customizing UI #' #' @param id Namespace Id - use namespaced id ns("conceptsInDataSource") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export conceptsInDataSourceView <- function(id) { ns <- shiny::NS(id) diff --git a/R/cohort-diagnostics-counts.R b/R/cohort-diagnostics-counts.R index 262c41ea..e104d113 100644 --- a/R/cohort-diagnostics-counts.R +++ b/R/cohort-diagnostics-counts.R @@ -18,6 +18,7 @@ #' @description #' Shiny view for cohort counts module #' @param id Namespace id +#' @family {CohortDiagnostics} #' @export cohortCountsView <- function(id) { ns <- shiny::NS(id) @@ -191,6 +192,7 @@ getInclusionRulesTable <- function( #' @param selectedCohorts shiny::reactive - should return cohorts selected or NULL #' @param selectedDatabaseIds shiny::reactive - should return cohorts selected or NULL #' @param cohortIds shiny::reactive - should return cohorts selected integers or NULL +#' @family {CohortDiagnostics} cohortCountsModule <- function(id, dataSource, cohortTable = dataSource$cohortTable, diff --git a/R/cohort-diagnostics-databaseInformation.R b/R/cohort-diagnostics-databaseInformation.R index 74f4c671..6e1ac0d3 100644 --- a/R/cohort-diagnostics-databaseInformation.R +++ b/R/cohort-diagnostics-databaseInformation.R @@ -20,6 +20,7 @@ #' Use for customizing UI #' #' @param id Namespace Id - use namespaced id ns("databaseInformation") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export databaseInformationView <- function(id) { ns <- shiny::NS(id) diff --git a/R/cohort-diagnostics-definition.R b/R/cohort-diagnostics-definition.R index 27733ec4..ec887394 100644 --- a/R/cohort-diagnostics-definition.R +++ b/R/cohort-diagnostics-definition.R @@ -26,7 +26,7 @@ #' @param cohortName Name for the cohort definition #' #' @param includeConceptSets Do you want to inclued concept set in the documentation -#' +#' @family {CohortDiagnostics} #' @return list object #' getCirceRenderedExpression <- function(cohortDefinition, @@ -302,6 +302,7 @@ exportCohortDefinitionsZip <- function(cohortDefinitions, #' @description #' Outputs cohort definitions #' @param id Namespace id for module +#' @family {CohortDiagnostics} #' @export cohortDefinitionsView <- function(id) { ns <- shiny::NS(id) @@ -522,6 +523,7 @@ getCountForConceptIdInCohort <- #' @param databaseTable data.frame of databasese, databaseId, name #' @param cohortTable data.frame of cohorts, cohortId, cohortName #' @param cohortCountTable data.frame of cohortCounts, cohortId, subjects records +#' @family {CohortDiagnostics} cohortDefinitionsModule <- function( id, dataSource, diff --git a/R/cohort-diagnostics-incidenceRates.R b/R/cohort-diagnostics-incidenceRates.R index 1e331c95..d588ab20 100644 --- a/R/cohort-diagnostics-incidenceRates.R +++ b/R/cohort-diagnostics-incidenceRates.R @@ -500,6 +500,7 @@ plotIncidenceRate <- function(data, #' Use for customizing UI #' #' @param id Namespace Id - use namespaced id ns("incidenceRates") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export incidenceRatesView <- function(id) { ns <- shiny::NS(id) diff --git a/R/cohort-diagnostics-inclusionRules.R b/R/cohort-diagnostics-inclusionRules.R index 8122a1fa..2b71c6fc 100644 --- a/R/cohort-diagnostics-inclusionRules.R +++ b/R/cohort-diagnostics-inclusionRules.R @@ -19,6 +19,7 @@ #' Use for customizing UI #' #' @param id Namespace Id - use namespaced id ns("inclusionRules") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export inclusionRulesView <- function(id) { ns <- shiny::NS(id) diff --git a/R/cohort-diagnostics-indexEventBreakdown.R b/R/cohort-diagnostics-indexEventBreakdown.R index 07476663..25a3ea2f 100644 --- a/R/cohort-diagnostics-indexEventBreakdown.R +++ b/R/cohort-diagnostics-indexEventBreakdown.R @@ -19,6 +19,7 @@ #' Use for customizing UI #' #' @param id Namespace Id - use namespaced id ns("indexEvents") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export indexEventBreakdownView <- function(id) { ns <- shiny::NS(id) diff --git a/R/cohort-diagnostics-main-ui.R b/R/cohort-diagnostics-main-ui.R index 8552922f..b248e3f8 100644 --- a/R/cohort-diagnostics-main-ui.R +++ b/R/cohort-diagnostics-main-ui.R @@ -146,6 +146,7 @@ cdUiControls <- function(ns) { #' @return #' string location of the description helper file #' @family {CohortDiagnostics} +#' #' @export cohortDiagnosticsHelperFile <- function() { fileLoc <- system.file('cohort-diagnostics-www', "cohort-diagnostics.html", package = utils::packageName()) @@ -161,6 +162,7 @@ cohortDiagnosticsHelperFile <- function() { #' #' @return #' The user interface to the cohort diagnostics viewer module +#' @family {CohortDiagnostics} #' #' @export cohortDiagnosticsView <- function(id = "DiagnosticsExplorer") { diff --git a/R/cohort-diagnostics-main.R b/R/cohort-diagnostics-main.R index 5541d7de..ef59f164 100644 --- a/R/cohort-diagnostics-main.R +++ b/R/cohort-diagnostics-main.R @@ -23,7 +23,7 @@ } else if (connectionHandler$dbms() != "sqlite") { tables <- DatabaseConnector::getTableNames(connectionHandler$getConnection(), - databaseSchema = schema) |> + databaseSchema = schema) |> tolower() } else { tables <- DatabaseConnector::getTableNames(connectionHandler$getConnection()) |> @@ -39,18 +39,18 @@ loadResultsTable <- function(dataSource, tableName, required = FALSE, cdTablePre selectTableName <- paste0(cdTablePrefix, tableName) resultsTablesOnServer <- tolower(.availableTables(dataSource$connectionHandler, dataSource$schema)) - + if (required || selectTableName %in% resultsTablesOnServer) { if (tableIsEmpty(dataSource, selectTableName)) { return(data.frame()) } - + tryCatch( - { - table <- dataSource$connectionHandler$queryDb("SELECT * FROM @schema.@table", - schema = dataSource$schema, - table = selectTableName) - }, + { + table <- dataSource$connectionHandler$queryDb("SELECT * FROM @schema.@table", + schema = dataSource$schema, + table = selectTableName) + }, error = function(err) { stop( "Error reading from ", @@ -60,10 +60,10 @@ loadResultsTable <- function(dataSource, tableName, required = FALSE, cdTablePre ) } ) - + return(table) } - + return(data.frame()) } @@ -77,16 +77,16 @@ tableIsEmpty <- function(dataSource, tableName) { schema = dataSource$schema, table = tableName ) - + }, error = function(...) { message("Table not found: ", tableName) }) - + return(nrow(row) == 0) } postgresEnabledReports <- function(connectionHandler, schema, tbls) { - + sql <- " select c.relname as table_name from pg_class c @@ -96,15 +96,16 @@ postgresEnabledReports <- function(connectionHandler, schema, tbls) { and c.reltuples != 0 and n.nspname = '@schema' " - + return(connectionHandler$queryDb(sql, schema = schema) %>% dplyr::pull("tableName")) } #' Get enable cd reports from available data #' @param dataSource Cohort diagnostics data source +#' @family {CohortDiagnostics} #' @export getEnabledCdReports <- function(dataSource) { - + if (dataSource$connectionHandler$dbms() == "postgresql") { tbls <- dataSource$dataModelSpecifications$tableName %>% unique() possible <- paste0(dataSource$cdTablePrefix, tbls) @@ -114,10 +115,10 @@ getEnabledCdReports <- function(dataSource) { SqlRender::snakeCaseToCamelCase() return(enabledReports) } - + enabledReports <- c() resultsTables <- .availableTables(dataSource$connectionHandler, schema = dataSource$schema) - + for (table in dataSource$dataModelSpecifications$tableName %>% unique()) { if (dataSource$prefixTable(table) %in% resultsTables) { if (!tableIsEmpty(dataSource, dataSource$prefixTable(table))) { @@ -126,7 +127,7 @@ getEnabledCdReports <- function(dataSource) { } } enabledReports <- c(enabledReports, "cohort", "database") - + return(enabledReports) } @@ -142,7 +143,7 @@ getEnabledCdReports <- function(dataSource) { #' @param displayProgress display a progress messaage (can only be used inside a shiny reactive context) #' @param dataMigrationsRef The path to a file listing all migrations for the data model that should have been applied #' @return An object of class `CdDataSource`. -#' +#' @family {CohortDiagnostics} #' @export createCdDatabaseDataSource <- function( connectionHandler, @@ -155,7 +156,7 @@ createCdDatabaseDataSource <- function( package = utils::packageName()), displayProgress = FALSE ) { - + checkmate::assertR6(connectionHandler, "ConnectionHandler") checkmate::assertString(resultDatabaseSettings$schema) checkmate::assertString(resultDatabaseSettings$vocabularyDatabaseSchema, null.ok = TRUE) @@ -165,7 +166,7 @@ createCdDatabaseDataSource <- function( checkmate::assertString(resultDatabaseSettings$databaseTablePrefix, null.ok = TRUE) checkmate::assertFileExists(dataModelSpecificationsPath) checkmate::assertFileExists(dataMigrationsRef) - + if (is.null(resultDatabaseSettings$vocabularyDatabaseSchema)) { resultDatabaseSettings$vocabularyDatabaseSchema <- resultDatabaseSettings$schema } @@ -184,7 +185,7 @@ createCdDatabaseDataSource <- function( if (is.null(resultDatabaseSettings$databaseTablePrefix)) { resultDatabaseSettings$databaseTablePrefix <- resultDatabaseSettings$cdTablePrefix } - + if (displayProgress) { shiny::setProgress(value = 0.05, message = "Getting settings") } @@ -196,13 +197,13 @@ createCdDatabaseDataSource <- function( schema = resultDatabaseSettings$schema, cd_table_prefix = resultDatabaseSettings$cdTablePrefix) }, error = function(...) { - warning("CohortDiagnotics schema does not contain migrations table. Schema was likely created incorrectly") + warning("CohortDiagnostics schema does not contain migrations table. Schema was likely created incorrectly") if (displayProgress) { shiny::showNotification(paste("CohortDiagnostics data model does not have migrations table. Schema was likely created incorrectly"), type = "error") } }) - + dataMigrationsExpected <- utils::read.csv(dataMigrationsRef) for (m in dataMigrationsExpected$migrationFile) { if (!m %in% migrations$migrationFile) { @@ -212,10 +213,10 @@ createCdDatabaseDataSource <- function( } } } - + modelSpec <- utils::read.csv(dataModelSpecificationsPath) colnames(modelSpec) <- SqlRender::snakeCaseToCamelCase(colnames(modelSpec)) - + dataSource <- list( connectionHandler = connectionHandler, schema = resultDatabaseSettings$schema, @@ -228,7 +229,7 @@ createCdDatabaseDataSource <- function( # don't prexfix table if we us a dedicated vocabulary schema if (resultDatabaseSettings$vocabularyDatabaseSchema == resultDatabaseSettings$schema) return(paste0(resultDatabaseSettings$cdTablePrefix, tableName)) - + return(tableName) }, cgTable = resultDatabaseSettings$cgTable, @@ -238,53 +239,53 @@ createCdDatabaseDataSource <- function( databaseTablePrefix = "cd_", dataModelSpecifications = modelSpec ) - + if (displayProgress) shiny::setProgress(value = 0.05, message = "Getting enabled reports") - + dataSource$enabledReports <- getEnabledCdReports(dataSource) - + if (displayProgress) shiny::setProgress(value = 0.1, message = "Getting database information") dataSource$dbTable <- getDatabaseTable(dataSource) - + if (displayProgress) shiny::setProgress(value = 0.2, message = "Getting cohorts") - - + + dataSource$cohortTableName <- paste0(dataSource$cdTablePrefix, "cohort") - + dataSource$cohortTable <- getCohortTable(dataSource) - + if (displayProgress) shiny::setProgress(value = 0.6, message = "Getting concept sets") - + dataSource$conceptSets <- loadResultsTable(dataSource, "concept_sets", cdTablePrefix = dataSource$cdTablePrefix) - + if (displayProgress) shiny::setProgress(value = 0.7, message = "Getting counts") - + dataSource$cohortCountTable <- loadResultsTable(dataSource, "cohort_count", required = TRUE, cdTablePrefix = dataSource$cdTablePrefix) - + dataSource$enabledReports <- dataSource$enabledReports - + if (displayProgress) shiny::setProgress(value = 0.7, message = "Getting Temporal References") - + dataSource$temporalAnalysisRef <- loadResultsTable(dataSource, "temporal_analysis_ref", cdTablePrefix = dataSource$cdTablePrefix) - + dataSource$temporalChoices <- getResultsTemporalTimeRef(dataSource = dataSource) - + if (hasData(dataSource$temporalChoices)) { dataSource$temporalCharacterizationTimeIdChoices <- dataSource$temporalChoices %>% dplyr::arrange(.data$sequence) - + dataSource$characterizationTimeIdChoices <- dataSource$temporalChoices %>% dplyr::filter(.data$isTemporal == 0) %>% dplyr::filter(.data$primaryTimeId == 1) %>% dplyr::arrange(.data$sequence) } - + if (!is.null(dataSource$temporalAnalysisRef)) { dataSource$temporalAnalysisRef <- dplyr::bind_rows( dataSource$temporalAnalysisRef, @@ -296,20 +297,20 @@ createCdDatabaseDataSource <- function( missingMeansZero = "Y" ) ) - + dataSource$domainIdOptions <- dataSource$temporalAnalysisRef %>% dplyr::select("domainId") %>% dplyr::pull("domainId") %>% unique() %>% sort() - + dataSource$analysisNameOptions <- dataSource$temporalAnalysisRef %>% dplyr::select("analysisName") %>% dplyr::pull("analysisName") %>% unique() %>% sort() } - + class(dataSource) <- "CdDataSource" return(dataSource) } @@ -317,13 +318,13 @@ createCdDatabaseDataSource <- function( getDatabaseTable <- function(dataSource) { databaseTable <- loadResultsTable(dataSource, dataSource$prefixTable(dataSource$databaseTable), required = TRUE) if (nrow(databaseTable) > 0 & - "vocabularyVersion" %in% colnames(databaseTable)) { + "vocabularyVersion" %in% colnames(databaseTable)) { databaseTable <- databaseTable %>% dplyr::mutate( databaseIdWithVocabularyVersion = paste0(.data$databaseId, " (", .data$vocabularyVersion, ")") ) } - + databaseTable } @@ -334,12 +335,12 @@ getCohortTable <- function(dataSource) { schema = dataSource$schema, cd_table_prefix = dataSource$cdTablePrefix ) - + cohortTable <- cohortTable %>% dplyr::arrange(.data$cohortId) %>% dplyr::mutate(shortName = paste0("C", .data$cohortId)) %>% dplyr::mutate(compoundName = paste0(.data$shortName, ": ", .data$cohortName)) - + cohortTable } @@ -352,11 +353,11 @@ getResultsTemporalTimeRef <- function(dataSource) { schema = dataSource$schema, table_name = dataSource$prefixTable("temporal_time_ref") ) - + if (nrow(temporalTimeRef) == 0) { return(NULL) } - + temporalChoices <- temporalTimeRef %>% dplyr::mutate(temporalChoices = paste0("T (", .data$startDay, "d to ", .data$endDay, "d)")) %>% dplyr::arrange(.data$startDay, .data$endDay) %>% @@ -390,7 +391,7 @@ getResultsTemporalTimeRef <- function(dataSource) { false = 1 )) %>% dplyr::arrange(.data$startDay, .data$timeId, .data$endDay) - + temporalChoices <- dplyr::bind_rows( temporalChoices %>% dplyr::slice(0), dplyr::tibble( @@ -402,7 +403,7 @@ getResultsTemporalTimeRef <- function(dataSource) { temporalChoices ) %>% dplyr::mutate(sequence = dplyr::row_number()) - + return(temporalChoices) } @@ -420,7 +421,7 @@ cohortDiagnosticsServer <- function(id, resultDatabaseSettings, dataSource = NULL) { ns <- shiny::NS(id) - + checkmate::assertClass(dataSource, "CdDataSource", null.ok = TRUE) if (is.null(dataSource)) { checkmate::assertR6(connectionHandler, "ConnectionHandler", null.ok = FALSE) @@ -431,7 +432,7 @@ cohortDiagnosticsServer <- function(id, displayProgress = TRUE ) } - + shiny::moduleServer(id, function(input, output, session) { databaseTable <- dataSource$dbTable cohortTable <- dataSource$cohortTable @@ -440,46 +441,46 @@ cohortDiagnosticsServer <- function(id, enabledReports <- dataSource$enabledReports temporalChoices <- dataSource$temporalChoices temporalCharacterizationTimeIdChoices <- dataSource$temporalCharacterizationTimeIdChoices - + shiny::observe({ - + selection <- c( "Cohort Definitions" = "cohortDefinitions", "Database Information" = "databaseInformation" ) if ("cohortCount" %in% dataSource$enabledReports) selection["Cohort Counts"] <- "cohortCounts" - + if ("indexEvents" %in% dataSource$enabledReports) selection["Index Events"] <- "indexEvents" - + if ("temporalCovariateValue" %in% dataSource$enabledReports) { selection["Cohort Characterization"] <- "characterization" selection["Compare Cohort Characterization"] <- "compareCohortCharacterization" selection["Time Distributions"] <- "timeDistribution" } - + if ("relationship" %in% dataSource$enabledReports) selection["Cohort Overlap"] <- "cohortOverlap" - + if ("cohortInclusion" %in% dataSource$enabledReports) selection["Inclusion Rule Statistics"] <- "inclusionRules" - + if ("incidenceRate" %in% dataSource$enabledReports) selection["Incidence"] <- "incidenceRates" - + if ("visitContext" %in% dataSource$enabledReports) selection["Visit Context"] <- "visitContext" - + if ("includedSourceConcept" %in% dataSource$enabledReports) selection["Concepts In Data Source"] <- "conceptsInDataSource" - + if ("orphanConcepts" %in% dataSource$enabledReports) selection["Orphan Concepts"] <- "orphanConcepts" - + if ("indexEventBreakdown" %in% dataSource$enabledReports) selection["Index Event Breakdown"] <- "indexEvents" - + shiny::updateSelectInput( inputId = "tabs", label = "Select Report", @@ -487,7 +488,7 @@ cohortDiagnosticsServer <- function(id, selected = c("cohortDefinitions") ) }) - + # Reacive: targetCohortId targetCohortId <- shiny::reactive({ return(cohortTable$cohortId[cohortTable$compoundName == input$targetCohort]) @@ -499,11 +500,11 @@ cohortDiagnosticsServer <- function(id, dplyr::select("cohortId") %>% dplyr::pull() }) - + selectedConceptSets <- shiny::reactive({ input$conceptSetsSelected }) - + # conceptSetIds ---- conceptSetIds <- shiny::reactive(x = { conceptSetsFiltered <- conceptSets %>% @@ -514,10 +515,10 @@ cohortDiagnosticsServer <- function(id, unique() return(conceptSetsFiltered) }) - + databaseChoices <- databaseTable$databaseId names(databaseChoices) <- databaseTable$databaseName - + ## ReactiveValue: selectedDatabaseIds ---- selectedDatabaseIds <- shiny::reactive({ if (!is.null(input$tabs)) { @@ -533,8 +534,8 @@ cohortDiagnosticsServer <- function(id, } } }) - - + + shiny::observe({ shinyWidgets::updatePickerInput(session = session, inputId = "database", @@ -547,7 +548,7 @@ cohortDiagnosticsServer <- function(id, selected = databaseChoices[[1]], ) }) - + ## ReactiveValue: selectedTemporalTimeIds ---- selectedTemporalTimeIds <- shiny::reactiveVal(NULL) shiny::observeEvent(eventExpr = { @@ -558,7 +559,7 @@ cohortDiagnosticsServer <- function(id, ) }, handlerExpr = { if (isFALSE(input$timeIdChoices_open) || - !is.null(input$tabs) & !is.null(temporalCharacterizationTimeIdChoices)) { + !is.null(input$tabs) & !is.null(temporalCharacterizationTimeIdChoices)) { selectedTemporalTimeIds( temporalCharacterizationTimeIdChoices %>% dplyr::filter(.data$temporalChoices %in% input$timeIdChoices) %>% @@ -568,12 +569,12 @@ cohortDiagnosticsServer <- function(id, ) } }) - + cohortSubset <- shiny::reactive({ return(cohortTable %>% dplyr::arrange(.data$cohortId)) }) - + shiny::observe({ subset <- cohortSubset()$compoundName shinyWidgets::updatePickerInput( @@ -583,7 +584,7 @@ cohortDiagnosticsServer <- function(id, choices = subset ) }) - + shiny::observe({ subset <- cohortSubset()$compoundName shinyWidgets::updatePickerInput( @@ -594,7 +595,7 @@ cohortDiagnosticsServer <- function(id, selected = c(subset[1], subset[2]) ) }) - + # Characterization (Shared across) ------------------------------------------------- ## Reactive objects ---- ### getConceptSetNameForFilter ---- @@ -606,9 +607,9 @@ cohortDiagnosticsServer <- function(id, dplyr::filter(.data$cohortId == targetCohortId()) %>% dplyr::mutate(name = .data$conceptSetName) %>% dplyr::select("name") - + }) - + shiny::observe({ subset <- getConceptSetNameForFilter()$name %>% sort() %>% @@ -620,7 +621,7 @@ cohortDiagnosticsServer <- function(id, choices = subset ) }) - + selectedCohorts <- shiny::reactive({ cohorts <- cohortSubset() %>% dplyr::filter(.data$cohortId %in% cohortIds()) %>% @@ -630,17 +631,17 @@ cohortDiagnosticsServer <- function(id, shiny::tags$tr(lapply(x, shiny::tags$td)) })) }) - + selectedCohort <- shiny::reactive({ return(input$targetCohort) }) - + if ("cohort" %in% enabledReports) { cohortDefinitionsModule(id = "cohortDefinitions", dataSource = dataSource, cohortDefinitions = cohortSubset) } - + if ("includedSourceConcept" %in% enabledReports) { conceptsInDataSourceModule(id = "conceptsInDataSource", dataSource = dataSource, @@ -650,7 +651,7 @@ cohortDiagnosticsServer <- function(id, selectedConceptSets = selectedConceptSets, databaseTable = databaseTable) } - + if ("orphanConcept" %in% enabledReports) { orphanConceptsModule("orphanConcepts", dataSource = dataSource, @@ -661,7 +662,7 @@ cohortDiagnosticsServer <- function(id, selectedConceptSets = selectedConceptSets, conceptSetIds = conceptSetIds) } - + if ("cohortCount" %in% enabledReports) { cohortCountsModule(id = "cohortCounts", dataSource = dataSource, @@ -671,7 +672,7 @@ cohortDiagnosticsServer <- function(id, selectedDatabaseIds = selectedDatabaseIds, cohortIds = cohortIds) } - + if ("indexEventBreakdown" %in% enabledReports) { indexEventBreakdownModule(id = "indexEvents", dataSource = dataSource, @@ -680,7 +681,7 @@ cohortDiagnosticsServer <- function(id, cohortCountTable = cohortCountTable, selectedDatabaseIds = selectedDatabaseIds) } - + if ("visitContext" %in% enabledReports) { visitContextModule(id = "visitContext", dataSource = dataSource, @@ -690,7 +691,7 @@ cohortDiagnosticsServer <- function(id, cohortCountTable = cohortCountTable, databaseTable = databaseTable) } - + if ("relationship" %in% enabledReports) { cohortOverlapModule(id = "cohortOverlap", dataSource = dataSource, @@ -700,21 +701,21 @@ cohortDiagnosticsServer <- function(id, cohortIds = cohortIds, cohortTable = cohortTable) } - + if ("temporalCovariateValue" %in% enabledReports) { timeDistributionsModule(id = "timeDistributions", dataSource = dataSource, selectedCohorts = selectedCohorts, cohortIds = cohortIds, selectedDatabaseIds = selectedDatabaseIds) - + cohortDiagCharacterizationModule(id = "characterization", dataSource = dataSource) - + compareCohortCharacterizationModule(id = "compareCohortCharacterization", dataSource = dataSource) } - + if ("incidenceRate" %in% enabledReports) { incidenceRatesModule(id = "incidenceRates", dataSource = dataSource, @@ -724,7 +725,7 @@ cohortDiagnosticsServer <- function(id, databaseTable = databaseTable, cohortTable = cohortTable) } - + if ("cohortInclusion" %in% enabledReports) { inclusionRulesModule(id = "inclusionRules", dataSource = dataSource, @@ -732,15 +733,15 @@ cohortDiagnosticsServer <- function(id, selectedCohort = selectedCohort, targetCohortId = targetCohortId, selectedDatabaseIds = selectedDatabaseIds) - + } databaseInformationModule(id = "databaseInformation", dataSource = dataSource, selectedDatabaseIds = selectedDatabaseIds, databaseTable = databaseTable) - + } - + ) - -} + +} \ No newline at end of file diff --git a/R/cohort-diagnostics-orphanConcepts.R b/R/cohort-diagnostics-orphanConcepts.R index a50bf0db..51d6564d 100644 --- a/R/cohort-diagnostics-orphanConcepts.R +++ b/R/cohort-diagnostics-orphanConcepts.R @@ -19,6 +19,7 @@ #' Use for customizing UI #' @family {CohortDiagnostics} #' @param id Namespace Id - use namespaced id ns("orphanConcepts") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export orpahanConceptsView <- function(id) { ns <- shiny::NS(id) @@ -122,8 +123,8 @@ orphanConceptsModule <- function(id, }) databaseSubGrp <- ", - MAX(CASE WHEN oc.database_id = '@db_id_i' THEN oc.concept_count END) AS concept_count_@db_id_i, - MAX(CASE WHEN oc.database_id = '@db_id_i' THEN oc.concept_subjects END) AS subject_count_@db_id_i" + MAX(CASE WHEN oc.database_id = '@db_id_i' THEN oc.concept_count END) AS concept_count_@db_id_id, + MAX(CASE WHEN oc.database_id = '@db_id_i' THEN oc.concept_subjects END) AS subject_count_@db_id_id" sql <- " SELECT @@ -170,11 +171,11 @@ orphanConceptsModule <- function(id, "Vocabulary Id" = "c.vocabulary_id", "Concept Code" = "c.concept_code") for (dbid in databaseIds) { - dbCols <- SqlRender::render(databaseSubGrp, db_id_i = dbid) + dbCols <- SqlRender::render(databaseSubGrp, db_id_i = dbid, db_id_id = gsub("-", "", dbid)) dbSelectCols <- paste(dbSelectCols, dbCols) - columnIdCount <- SqlRender::snakeCaseToCamelCase(paste0("concept_count_", dbid)) - columnIdSubject <- SqlRender::snakeCaseToCamelCase(paste0("subject_count_", dbid)) + columnIdCount <- SqlRender::snakeCaseToCamelCase(paste0("concept_count_", gsub("-", "", dbid))) + columnIdSubject <- SqlRender::snakeCaseToCamelCase(paste0("subject_count_", gsub("-", "", dbid))) columnDefinitions[[columnIdCount]] <- reactable::colDef(name = "Records", cell = formatDataCellValueInDisplayTable(), @@ -195,7 +196,7 @@ orphanConceptsModule <- function(id, ) cNames <- names(sortByColumns) - sortByColumns <- c(sortByColumns, paste0("concept_count_", dbid), paste0("subject_count_", dbid)) + sortByColumns <- c(sortByColumns, paste0("concept_count_", gsub("-", "", dbid)), paste0("subject_count_", gsub("-", "", dbid))) names(sortByColumns) <- c(cNames, paste(databaseName, "Records"), paste(databaseName, "Subjects")) } diff --git a/R/cohort-diagnostics-timeDistributions.R b/R/cohort-diagnostics-timeDistributions.R index 436c7b80..764809f9 100644 --- a/R/cohort-diagnostics-timeDistributions.R +++ b/R/cohort-diagnostics-timeDistributions.R @@ -219,6 +219,7 @@ plotTimeDistribution <- function(data, shortNameRef = NULL, showMax = FALSE) { #' Use for customizing UI #' #' @param id Namespace Id - use namespaced id ns("imeDistributions") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export timeDistributionsView <- function(id) { ns <- shiny::NS(id) diff --git a/R/cohort-diagnostics-visitContext.R b/R/cohort-diagnostics-visitContext.R index 3b4b1771..dcc5c704 100644 --- a/R/cohort-diagnostics-visitContext.R +++ b/R/cohort-diagnostics-visitContext.R @@ -19,6 +19,7 @@ #' Use for customizing UI #' #' @param id Namespace Id - use namespaced id ns("vistConext") inside diagnosticsExplorer module +#' @family {CohortDiagnostics} #' @export visitContextView <- function(id) { ns <- shiny::NS(id) diff --git a/R/cohort-generator-main.R b/R/cohort-generator-main.R index 2237168a..2e2f7b88 100644 --- a/R/cohort-generator-main.R +++ b/R/cohort-generator-main.R @@ -21,7 +21,7 @@ #' #' @details #' Returns the location of the cohort-generator helper file -#' +#' @family {CohortGenerator} #' @return #' string location of the cohort-generator helper file #' @@ -34,7 +34,7 @@ cohortGeneratorHelperFile <- function(){ #' The viewer of the main cohort generator module #' #' @param id the unique reference id for the module -#' +#' @family {CohortGenerator} #' @return #' The user interface to the cohort generator results viewer #' @@ -50,14 +50,7 @@ cohortGeneratorViewer <- function(id) { width = '100%', title = shiny::span( shiny::icon("user-gear"),'Cohorts'), solidHeader = TRUE, - - shinydashboard::box( - collapsible = TRUE, - collapsed = TRUE, - title = shiny::span( shiny::icon("circle-question"), "Help & Information"), - width = "100%", - shiny::htmlTemplate(system.file("cohort-generator-www", "cohort-generator.html", package = utils::packageName())) - ), + shiny::tabsetPanel( id = ns("cohortGeneratorTabs"), @@ -126,6 +119,9 @@ cohortGeneratorViewer <- function(id) { title = shiny::span( shiny::icon("table"), 'Generation Table'), #solidHeader = TRUE, + shiny::uiOutput(ns("selectColsCohortGeneration") + ), + reactable::reactableOutput( outputId = ns("cohortGeneration") ) @@ -174,7 +170,10 @@ cohortGeneratorViewer <- function(id) { title = shiny::span( shiny::icon("table"), 'Attrition Table'), #solidHeader = TRUE, - reactable::reactableOutput(ns('attritionTable')) + # shiny::uiOutput(ns("selectColsCohortAttrition") + # ), + + resultTableViewer(ns('attritionTable')) ), shinydashboard::box( @@ -200,7 +199,7 @@ cohortGeneratorViewer <- function(id) { #' @param id the unique reference id for the module #' @param connectionHandler a connection to the database with the results #' @param resultDatabaseSettings a named list containing the cohort generator results database details (schema, table prefix) -#' +#' @family {CohortGenerator} #' @return #' the cohort generator results viewer main module server #' @@ -366,19 +365,65 @@ cohortGeneratorServer <- function( } ) - output$cohortGeneration <- reactable::renderReactable({ - data <- getCohortGeneratorCohortMeta( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) %>% - dplyr::select("cdmSourceName", - "cohortId", - "cohortName", - "generationStatus", - "startTime", - "endTime", - "generationDuration") - reactable::reactable(data, + inputColsCohortGeneration <- colnames(getCohortGeneratorCohortMeta( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings + ) %>% + dplyr::select("cdmSourceName", + "cohortId", + "cohortName", + "generationStatus", + "startTime", + "endTime", + "generationDuration") + ) + + names(inputColsCohortGeneration) <- c("Database Name", + "Cohort ID", + "Cohort Name", + "Is the Cohort Generated?", + "Generation Start Time", + "Generation End Time", + "Generation Duration (mins)") + + output$selectColsCohortGeneration <- shiny::renderUI({ + + shinyWidgets::pickerInput( + inputId = session$ns('cohortGenerationCols'), + label = 'Select Columns to Display: ', + choices = inputColsCohortGeneration, + selected = inputColsCohortGeneration, + choicesOpt = list(style = rep_len("color: black;", 999)), + multiple = T, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + size = 10, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 50 + ), + width = "50%" + ) + + }) + + dataGen <- getCohortGeneratorCohortMeta( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings + ) %>% + dplyr::select("cdmSourceName", + "cohortId", + "cohortName", + "generationStatus", + "startTime", + "endTime", + "generationDuration") + + cgTable <- shiny::reactive({ + + reactable::reactable(dataGen %>% + dplyr::select(input$cohortGenerationCols), columns = list( # Render a "show details" button in the last column of the table. # This button won't do anything by itself, but will trigger the custom @@ -437,6 +482,24 @@ cohortGeneratorServer <- function( ) }) + output$cohortGeneration <- reactable::renderReactable({ + + tryCatch({ + cgTable() + }, + + error = function(e){ + shiny::showNotification( + paste0( + "Loading..." + ) + ); + return(NULL) + } + + ) + }) + # download button - generation output$downloadCohortGeneration <- shiny::downloadHandler( filename = function() { @@ -586,97 +649,98 @@ cohortGeneratorServer <- function( data <- inputValsClean %>% - dplyr::filter(.data$cdmSourceName %in% input$selectedDatabaseId & - .data$cohortName %in% input$selectedCohortName & - .data$modeId %in% input$selectedModeId - ) + dplyr::filter(.data$cdmSourceName %in% input$selectedDatabaseId & + .data$cohortName %in% input$selectedCohortName & + .data$modeId %in% input$selectedModeId + ) reactiveData <- shiny::reactive(data) if(!is.null(data)){ - - output$attritionTable <- reactable::renderReactable( - reactable::reactable( - data = reactiveData() %>% - dplyr::select(c("cdmSourceName", "cohortName", "ruleName", - "personCount", "dropCount", - "dropPerc", "retainPerc") + + resultTableServer( + id = 'attritionTable', + df = reactiveData() %>% + dplyr::select(c("cdmSourceName", "cohortName", "ruleName", + "personCount", "dropCount", + "dropPerc", "retainPerc") + ) + + , + # rownames = FALSE, + # defaultPageSize = 5, + # showPageSizeOptions = T, + # striped = T, + colDefsInput = list( + cdmSourceName = reactable::colDef( + filterable = TRUE, + header = withTooltip( + "Database Name", + "The name of the database" + )), + cohortName = reactable::colDef( + filterable = TRUE, + header = withTooltip( + "Cohort Name", + "The name of the cohort" + )), + ruleName = reactable::colDef( + header = withTooltip( + "Inclusion Rule Name", + "The name of the inclusion rule" + )), + personCount = reactable::colDef( + format = reactable::colFormat(separators = TRUE), + header = withTooltip( + "Subject/Record Count", + "The number of subjects or records (depending on your selection) remaining after the inclusion rule was applied" + )), + dropCount = reactable::colDef( + format = reactable::colFormat(separators = TRUE), + header = withTooltip( + "Number Lost", + "The number of subjects or records (depending on your selection) removed/lost after the inclusion rule was applied" + )), + dropPerc = reactable::colDef( + format = reactable::colFormat(separators = TRUE), + header = withTooltip( + "Percentage Lost", + "The percentage of subjects or records (depending on your selection) removed/lost after the inclusion rule was applied compared to the previous rule count" + )), + retainPerc = reactable::colDef( + format = reactable::colFormat(separators = TRUE), + header = withTooltip( + "Percentage Retained", + "The percentage of subjects or records (depending on your selection) retained after the inclusion rule was applied compared to the previous rule count" + )) ) - - , - rownames = FALSE, - defaultPageSize = 5, - showPageSizeOptions = T, - striped = T, - columns = list( - cdmSourceName = reactable::colDef( - filterable = TRUE, - header = withTooltip( - "Database Name", - "The name of the database" - )), - cohortName = reactable::colDef( - filterable = TRUE, - header = withTooltip( - "Cohort Name", - "The name of the cohort" - )), - ruleName = reactable::colDef( - header = withTooltip( - "Inclusion Rule Name", - "The name of the inclusion rule" - )), - personCount = reactable::colDef( - format = reactable::colFormat(separators = TRUE), - header = withTooltip( - "Subject/Record Count", - "The number of subjects or records (depending on your selection) remaining after the inclusion rule was applied" - )), - dropCount = reactable::colDef( - format = reactable::colFormat(separators = TRUE), - header = withTooltip( - "Number Lost", - "The number of subjects or records (depending on your selection) removed/lost after the inclusion rule was applied" - )), - dropPerc = reactable::colDef( - format = reactable::colFormat(separators = TRUE), - header = withTooltip( - "Percentage Lost", - "The percentage of subjects or records (depending on your selection) removed/lost after the inclusion rule was applied compared to the previous rule count" - )), - retainPerc = reactable::colDef( - format = reactable::colFormat(separators = TRUE), - header = withTooltip( - "Number Retained", - "The number of subjects or records (depending on your selection) retained after the inclusion rule was applied compared to the previous rule count" - )) - ), - - filterable = TRUE, - sortable = TRUE, - defaultColDef = reactable::colDef( - align = "left" + #, + + # filterable = TRUE, + # sortable = TRUE, + # defaultColDef = reactable::colDef( + # align = "left" + # ) + ) + #) + + #attrition plot + output$attritionPlot <- plotly::renderPlotly( + getCohortAttritionPlot( + data ) ) - ) - - #attrition plot - output$attritionPlot <- plotly::renderPlotly( - getCohortAttritionPlot( - data + + # download button + output$downloadAttritionTable <- shiny::downloadHandler( + filename = function() { + paste('cohort-attrition-data-', Sys.Date(), '.csv', sep='') + }, + content = function(con) { + utils::write.csv(data() + , con) + } ) - ) - - # download button - output$downloadAttritionTable <- shiny::downloadHandler( - filename = function() { - paste('cohort-attrition-data-', Sys.Date(), '.csv', sep='') - }, - content = function(con) { - utils::write.csv(data() - , con) - } - ) } else{ @@ -684,20 +748,20 @@ cohortGeneratorServer <- function( } } - ) - - }, + ) - error = function(e){ - shiny::showNotification( - paste0( - "No cohort inclusion result data present." - ) - ); - return(NULL) - } - - ) + }, + + error = function(e){ + shiny::showNotification( + paste0( + "No cohort inclusion result data present." + ) + ); + return(NULL) + } + + ) # end of server diff --git a/R/cohort-method-diagnosticsSummary.R b/R/cohort-method-diagnosticsSummary.R deleted file mode 100644 index 72f0a66b..00000000 --- a/R/cohort-method-diagnosticsSummary.R +++ /dev/null @@ -1,389 +0,0 @@ -# @file cohort-method-diagnosticsSummary -# -# Copyright 2024 Observational Health Data Sciences and Informatics -# -# This file is part of OhdsiShinyModules -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -#' The module viewer for rendering the PLE diagnostics results -#' -#' @param id the unique reference id for the module -#' -#' @return -#' The user interface to the cohort method diagnostics viewer -#' -#' @export -cohortMethodDiagnosticsSummaryViewer <- function(id) { - ns <- shiny::NS(id) - - shiny::div( - - shiny::tabsetPanel( - type = 'pills', - id = ns('diagnosticsTablePanel'), - shiny::tabPanel( - title = 'Summary', - resultTableViewer(ns("diagnosticsSummaryTable")) - ), - shiny::tabPanel( - title = 'Full', - resultTableViewer(ns("diagnosticsTable")) - ) - ) - #) - ) -} - - -#' The module server for rendering the PLE diagnostics summary -#' -#' @param id the unique reference id for the module -#' @param connectionHandler the connection to the PLE results database -#' @param resultDatabaseSettings a list containing the result schema and prefixes -#' @param inputSelected The target id, comparator id, outcome id and analysis id selected by the user -#' -#' @return -#' the PLE diagnostics summary results -#' -#' @export -cohortMethodDiagnosticsSummaryServer <- function( - id, - connectionHandler, - resultDatabaseSettings, - inputSelected -) { - - shiny::moduleServer( - id, - function(input, output, session) { - - data <- shiny::reactive({ - getCmDiagnosticsData( - connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - inputSelected = inputSelected - ) - }) - - data2 <- shiny::reactive({ - diagnosticSummaryFormat(data) - }) - - customColDefs <- list( - databaseName = reactable::colDef( - header = withTooltip( - "Database", - "The database name" - ) - ), - target = reactable::colDef( - header = withTooltip( - "Target", - "The target cohort of interest" - ), - minWidth = 300 - ), - comparator = reactable::colDef( - header = withTooltip( - "Comparator", - "The comparator cohort of interest" - ), - minWidth = 300 - ), - outcome = reactable::colDef( - header = withTooltip( - "Outcome", - "The outcome of interest" - ) - ), - analysis = reactable::colDef( - header = withTooltip( - "Analysis", - "The analysis name" - ) - ), - - mdrr = reactable::colDef( - header = withTooltip( - "MDRR", - "The minimum detectible relative risk" - ), - format = reactable::colFormat(digits = 4) - ), - ease = reactable::colDef( - header = withTooltip( - "EASE", - "The expected absolute systematic error" - ), - format = reactable::colFormat(digits = 4) - ), - maxSdm = reactable::colDef( - header = withTooltip( - "Max SDM", - "The maximum absolute standardized difference of mean" - ), - format = reactable::colFormat(digits = 4) - ), - sharedMaxSdm = reactable::colDef( - header = withTooltip( - "Shared Max SDM", - "The maximum absolute standardized difference of mean of the shared balance (shared across outcomes)" - ), - format = reactable::colFormat(digits = 4) - ), - equipoise = reactable::colDef( - header = withTooltip( - "Equipoise", - "The fraction of the study population with a preference score between 0.3 and 0.7" - ), - format = reactable::colFormat(digits = 4) - ), - balanceDiagnostic = reactable::colDef( - header = withTooltip( - "Balance Diagnostic", - "Pass / warning / fail classification of the balance diagnostic (Max SDM)" - ) - ), - mdrrDiagnostic = reactable::colDef( - header = withTooltip( - "MDRR Diagnostic", - "Pass / warning / fail classification of the MDRR diagnostic" - ) - ), - sharedBalanceDiagnostic = reactable::colDef( - header = withTooltip( - "Shared Balance Diagnostic", - "Pass / warning / fail classification of the shared balance diagnostic (Shared Max SDM)" - ) - ), - easeDiagnostic = reactable::colDef( - header = withTooltip( - "Ease Diagnostic", - "Pass / warning / fail classification of the EASE diagnostic" - ) - ), - equipoiseDiagnostic = reactable::colDef( - header = withTooltip( - "Equipoise Diagnostic", - "Pass / warning / fail classification of the equipoise diagnostic" - ) - ), - - unblind = reactable::colDef( - header = withTooltip( - "Unblind", - "If the value is 1 then the diagnostics passed and results can be unblinded" - ) - ), - - summaryValue = reactable::colDef(show = F) - - ) - - resultTableServer( - id = "diagnosticsTable", - df = data, - colDefsInput = customColDefs - ) - - resultTableServer( - id = "diagnosticsSummaryTable", - df = data2, - colDefsInput = getColDefsCmDiag( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - ) - - } - ) -} - -getColDefsCmDiag <- function( - connectionHandler, - resultDatabaseSettings -){ - - fixedColumns = list( - databaseName = reactable::colDef( - header = withTooltip( - "Database", - "The database name" - ), - sticky = "left" - ), - target = reactable::colDef( - header = withTooltip( - "Target", - "The target cohort of interest" - ), - sticky = "left" - ), - comparator = reactable::colDef( - header = withTooltip( - "Comparator", - "The comparator cohort of interest" - ), - sticky = "left" - ) - ) - - outcomes <- getCmCohorts( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - type = 'outcome' - ) - analyses <- getCmAnalyses( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - colnameFormat <- merge(unique(names(outcomes)), unique(names(analyses))) - colnameFormat <- apply(colnameFormat, 1, function(x){paste(x, collapse = '_', sep = '_')}) - - styleList <- lapply( - colnameFormat, - FUN = function(x){ - reactable::colDef( - header = withTooltip( - paste0(substring(x,1,35), "...", sep=""), - x - ), - style = function(value) { - color <- 'orange' - if(is.na(value)){ - color <- 'black' - }else if(value == 'Pass'){ - color <- '#AFE1AF' - }else if(value == 'Fail'){ - color <- '#E97451' - } - list(background = color) - } - ) - } - ) - names(styleList) <- colnameFormat - result <- append(fixedColumns, styleList) - - return(result) -} - -diagnosticSummaryFormat <- function( - data, - idCols = c('databaseName','target', 'comparator'), - namesFrom = c('outcome','analysis') - ){ - - if(is.null(data())){ - return(NULL) - } - - data2 <- tidyr::pivot_wider( - data = data(), - id_cols = idCols, - names_from = namesFrom, - values_from = c('summaryValue') - ) - - return(data2) -} - - - -getCmDiagnosticsData <- function( - connectionHandler, - resultDatabaseSettings, - inputSelected -) { - - targetIds = inputSelected()$targetIds - outcomeIds = inputSelected()$outcomeIds - comparatorIds = inputSelected()$comparatorIds - analysisIds = inputSelected()$analysisIds - - if(is.null(targetIds) || is.null(outcomeIds)){ - return(NULL) - } - - sql <- " - SELECT DISTINCT - dmd.cdm_source_abbreviation database_name, - cma.description analysis, - cgcd1.cohort_name target, - cgcd2.cohort_name comparator, - cgcd3.cohort_name outcome, - cmds.max_sdm, - cmds.shared_max_sdm, - cmds.equipoise, - cmds.mdrr, - cmds.ease, - cmds.balance_diagnostic, - cmds.shared_balance_diagnostic, -- added back - cmds.equipoise_diagnostic, - cmds.mdrr_diagnostic, - cmds.ease_diagnostic, - cmds.unblind - FROM - @schema.@cm_table_prefixdiagnostics_summary cmds - INNER JOIN @schema.@cm_table_prefixanalysis cma ON cmds.analysis_id = cma.analysis_id - INNER JOIN @schema.@database_table dmd ON dmd.database_id = cmds.database_id - INNER JOIN @schema.@cg_table_prefixcohort_definition cgcd1 ON cmds.target_id = cgcd1.cohort_definition_id - INNER JOIN @schema.@cg_table_prefixcohort_definition cgcd2 ON cmds.comparator_id = cgcd2.cohort_definition_id - INNER JOIN @schema.@cg_table_prefixcohort_definition cgcd3 ON cmds.outcome_id = cgcd3.cohort_definition_id - - where cgcd1.cohort_definition_id in (@targets) - {@use_comparators}?{and cgcd2.cohort_definition_id in (@comparators)} - and cgcd3.cohort_definition_id in (@outcomes) - {@use_analyses}?{and cma.analysis_id in (@analyses)} - ; - " - - result <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - cm_table_prefix = resultDatabaseSettings$cmTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - database_table = resultDatabaseSettings$databaseTable, - - targets = paste0(targetIds, collapse = ','), - comparators = paste0(comparatorIds, collapse = ','), - outcomes = paste0(outcomeIds, collapse = ','), - analyses = paste0(analysisIds, collapse = ','), - - use_comparators = !is.null(comparatorIds), - use_analyses = !is.null(analysisIds) - ) - - # adding percent fail for summary - result$summaryValue <- apply( - X = result[, grep('Diagnostic', colnames(result))], - MARGIN = 1, - FUN = function(x){ - - if(sum(x %in% c('FAIL'))>0){ - return('Fail') - } else if(sum(x %in% c('WARNING')) >0){ - return(sum(x %in% c('WARNING'))) - } else{ - return('Pass') - } - } - ) - - return( - result - ) -} diff --git a/R/cohort-method-main.R b/R/cohort-method-main.R deleted file mode 100644 index ba8c9181..00000000 --- a/R/cohort-method-main.R +++ /dev/null @@ -1,293 +0,0 @@ -# @file cohort-method-main.R -# -# Copyright 2024 Observational Health Data Sciences and Informatics -# -# This file is part of PatientLevelPrediction -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -#' The location of the cohort method module helper file -#' -#' @details -#' Returns the location of the cohort method helper file -#' -#' @return -#' string location of the cohort method helper file -#' -#' @export -cohortMethodHelperFile <- function(){ - fileLoc <- system.file('cohort-method-www', "cohort-method.html", package = "OhdsiShinyModules") - return(fileLoc) -} - -#' The viewer of the main cohort method module -#' -#' @param id the unique reference id for the module -#' -#' @return -#' The user interface to the cohort method results viewer -#' -#' @export -cohortMethodViewer <- function(id) { - ns <- shiny::NS(id) - - shinydashboard::box( - status = 'info', - width = 12, - title = shiny::span( shiny::icon("chart-column"), 'Cohort Method'), - solidHeader = TRUE, - - infoHelperViewer( - id = "helper", - helpLocation= system.file("cohort-method-www", "cohort-method.html", package = utils::packageName()) - ), - - # Input selection of T, C and Os - inputSelectionViewer(ns("input-selection")), - - shiny::conditionalPanel( - condition = 'input.generate != 0', - ns = shiny::NS(ns("input-selection")), - - shiny::tabsetPanel( - type = 'pills', - id = ns('mainPanel'), - - shiny::tabPanel( - title = "Diagnostics", - cohortMethodDiagnosticsSummaryViewer(ns("cmDiganostics")) - ), - - shiny::tabPanel( - title = "Results", - cohortMethodResultSummaryViewer(ns("cmResults")) - ) - ) - ) - - ) -} - - -#' The module server for the main cohort method module -#' -#' @param id the unique reference id for the module -#' @param connectionHandler a connection to the database with the results -#' @param resultDatabaseSettings a named list containing the PLE results database connection details -#' -#' @return -#' the PLE results viewer main module server -#' -#' @export -cohortMethodServer <- function( - id, - connectionHandler, - resultDatabaseSettings - ) { - - shiny::moduleServer( - id, - function(input, output, session) { - - dataFolder <- NULL - - targetIds <- getCmCohorts( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - type = 'target' - ) - outcomeIds <- getCmCohorts( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - type = 'outcome' - ) - comparatorIds <- getCmCohorts( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - type = 'comparator' - ) - analysisIds <- getCmAnalyses( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - inputSelected <- inputSelectionServer( - id = "input-selection", - inputSettingList = list( - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'targetIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Target: ', - choices = targetIds, - selected = targetIds[1], - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'outcomeIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Outcome: ', - choices = outcomeIds, - selected = outcomeIds[1], - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - createInputSetting( - rowNumber = 2, - columnWidth = 6, - varName = 'comparatorIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Comparator: ', - choices = comparatorIds, - selected = comparatorIds[1], - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - - createInputSetting( - rowNumber = 2, - columnWidth = 6, - varName = 'analysisIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Analysis: ', - choices = analysisIds, - selected = analysisIds[1], - multiple = T, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ) - ) - ) - - cohortMethodDiagnosticsSummaryServer( - id = "cmDiganostics", - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - inputSelected = inputSelected - ) - - cohortMethodResultSummaryServer( - id = "cmResults", - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - inputSelected = inputSelected - ) - - } - ) -} - -getCmCohorts <- function( - connectionHandler, - resultDatabaseSettings, - type = 'target' -){ - - sql <- " - SELECT DISTINCT - cgcd1.cohort_name as names, - cgcd1.cohort_definition_id - FROM - @schema.@cm_table_prefixresult cmds - INNER JOIN - @schema.@cg_table_prefixcohort_definition cgcd1 - ON cmds.@type_id = cgcd1.cohort_definition_id; - " - - result <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - cm_table_prefix = resultDatabaseSettings$cmTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - type = type - ) - - res <- result$cohortDefinitionId - names(res) <- result$names - - return( - res - ) -} - -getCmAnalyses <- function( - connectionHandler, - resultDatabaseSettings -){ - - sql <- " - SELECT DISTINCT - cma.analysis_id, - cma.description as names - FROM - @schema.@cm_table_prefixresult cmds - INNER JOIN - @schema.@cm_table_prefixanalysis cma - ON cmds.analysis_id = cma.analysis_id - ; - " - - result <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - cm_table_prefix = resultDatabaseSettings$cmTablePrefix - ) - - res <- result$analysisId - names(res) <- result$names - - return( - res - ) - -} diff --git a/R/components-data-viewer.R b/R/components-data-viewer.R index 8ad40662..6f544539 100644 --- a/R/components-data-viewer.R +++ b/R/components-data-viewer.R @@ -9,18 +9,20 @@ #' #' @param id string #' @param downloadedFileName string, desired name of downloaded data file. can use the name from the module that is being used -#' +#' @param boxTitle the title added to the box +#' @family {Utils} #' @return shiny module UI #' @family {Utils} resultTableViewer <- function( id = "result-table", - downloadedFileName = NULL + downloadedFileName = NULL, + boxTitle = 'Table' ) { ns <- shiny::NS(id) shiny::div(# UI shinydashboard::box( width = "100%", - title = shiny::span(shiny::icon("table"), "Table"), + title = shiny::span(shiny::icon("table"), boxTitle), shiny::fluidPage( shiny::fluidRow( shiny::column( @@ -148,6 +150,7 @@ ohdsiReactableTheme <- reactable::reactableTheme( #' @param id string, table id must match resultsTableViewer function #' @param df reactive that returns a data frame #' @param colDefsInput named list of reactable::colDefs +#' @param details The details of the results such as cohort names and database names #' @param selectedCols string vector of columns the reactable should display to start by default. Defaults to ALL if not specified. #' @param sortedCols string vector of columns the reactable should sort by by default. Defaults to no sort if not specified. #' @param elementId optional string vector of element Id name for custom dropdown filtering if present in the customColDef list. Defaults to NULL. @@ -156,13 +159,14 @@ ohdsiReactableTheme <- reactable::reactableTheme( #' actions must be a column in df #' @param downloadedFileName string, desired name of downloaded data file. can use the name from the module that is being used #' @param groupBy The columns to group by -#' +#' @family {Utils} #' @return shiny module server #' @family {Utils} resultTableServer <- function( id, #string df, #data.frame colDefsInput, + details = data.frame(), # details about the data.frame such as target and database name selectedCols = NULL, sortedCols = NULL, elementId = NULL, @@ -174,6 +178,13 @@ resultTableServer <- function( id, function(input, output, session) { + + # find the columns that are set to show=F + colNames <- names(colDefsInput) + hideCol <- unlist(lapply(colDefsInput, function(x) ifelse(is.null(x$show), F, !x$show))) + hideColNames <- colNames[hideCol] + + # convert a data.frame to a reactive if(!inherits(df, 'reactive')){ df <- shiny::reactiveVal(df) @@ -199,8 +210,8 @@ resultTableServer <- function( if(!is.null(selectedCols)){ intersect(colnames(newdf()), selectedCols) } - else{ - colnames(newdf()) + else{ # edited to restrict to colDef - show = T columns + setdiff(colnames(newdf()), hideColNames) } }) @@ -245,12 +256,13 @@ resultTableServer <- function( onClick <- NULL } + output$columnSelector <- shiny::renderUI({ shinyWidgets::pickerInput( inputId = session$ns('dataCols'), label = 'Select Columns to Display: ', - choices = colnames(newdf()), + choices = setdiff(colnames(newdf()), hideColNames), # edited to only show columns show = T selected = selectedColumns(), choicesOpt = list(style = rep_len("color: black;", 999)), multiple = T, @@ -266,6 +278,7 @@ resultTableServer <- function( ) }) + #need to try adding browser() to all reactives to see why selected cols isnt working @@ -323,7 +336,19 @@ function filterMinValue(rows, columnId, filterValue) { }); } " - +#use fuzzy text matching for global table search +fuzzySearch<- reactable::JS('function(rows, columnIds, filterValue) { + + // Create a case-insensitive RegEx pattern that performs a fuzzy search. + const pattern = new RegExp(filterValue, "i"); + + return rows.filter(function(row) { + return columnIds.some(function(columnId) { + return pattern.test(row.values[columnId]); + }); + }); +}') + output$resultData <- reactable::renderReactable({ if (is.null(input$dataCols)) { data = newdf() @@ -339,45 +364,50 @@ function filterMinValue(rows, columnId, filterValue) { } else{ height <- NULL } - - reactable::reactable( - data, - columns = colDefs(), - onClick = onClick, - groupBy = groupBy, - #these can be turned on/off and will overwrite colDef args - sortable = TRUE, - resizable = TRUE, - filterable = TRUE, - searchable = TRUE, - showPageSizeOptions = TRUE, - outlined = TRUE, - showSortIcon = TRUE, - striped = TRUE, - highlight = TRUE, - defaultColDef = reactable::colDef(align = "left"), - defaultSorted = sortedColumns(), - rowStyle = list( - height = height - ), - elementId = elementIdName() - #, experimental - #theme = ohdsiReactableTheme - ) + # htmltools::browsable( + # tagList( + # matchSorterDep, + reactable::reactable( + data, + columns = colDefs(), + onClick = onClick, + groupBy = groupBy, + #these can be turned on/off and will overwrite colDef args + sortable = TRUE, + resizable = TRUE, + filterable = TRUE, + searchable = TRUE, + searchMethod = fuzzySearch, + showPageSizeOptions = TRUE, + outlined = TRUE, + showSortIcon = TRUE, + striped = TRUE, + highlight = TRUE, + #defaultColDef = reactable::colDef(align = "left"), + defaultSorted = sortedColumns(), + rowStyle = list( + height = height + ), + elementId = elementIdName() + #, experimental + #theme = ohdsiReactableTheme + ) + # ) + # ) }) # download full data button output$downloadDataFull <- shiny::downloadHandler( filename = function() { - paste('result-data-full-', downloadedFileName, Sys.Date(), '.csv', sep = '') + paste('result-data-full-', downloadedFileName, Sys.Date(), '.xlsx', sep = '') }, content = function(con) { - utils::write.csv( - x = df(), - file = con, - row.names = F - ) + wb <- openxlsx::buildWorkbook(x = list( + details = details, + results = df() + )) + openxlsx::saveWorkbook(wb = wb, file = con) } ) diff --git a/R/components-helpInfo.R b/R/components-helpInfo.R index eae6f807..77f46a3c 100644 --- a/R/components-helpInfo.R +++ b/R/components-helpInfo.R @@ -6,7 +6,7 @@ infoHelperViewer <- function( shinydashboard::box( collapsible = TRUE, - collapsed = FALSE, + collapsed = TRUE, title = shiny::span( shiny::icon("circle-question"), "Help & Information"), width = "100%", shiny::htmlTemplate(helpLocation) diff --git a/R/components-largeTableViewer.R b/R/components-largeTableViewer.R index 0e1c8220..67f42a95 100644 --- a/R/components-largeTableViewer.R +++ b/R/components-largeTableViewer.R @@ -103,7 +103,7 @@ LargeDataTable <- R6::R6Class( #' @param baseQuery base sql query #' @param countQuery count query string (should match query). Can be auto generated with sub query #' (default) but this will likely result in slow results -#' @family {LargeTables} +#' @family {LargeTables} createLargeSqlQueryDt <- function(connectionHandler = NULL, connectionDetails = NULL, baseQuery, diff --git a/R/data-diagnostic-drill.R b/R/data-diagnostic-drill.R index 2da1c2a5..39b62774 100644 --- a/R/data-diagnostic-drill.R +++ b/R/data-diagnostic-drill.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {DataDiagnostics} #' @return #' The user interface to the summary module #' @@ -53,7 +53,7 @@ dataDiagnosticDrillViewer <- function(id) { #' @param id the unique reference id for the module #' @param connectionHandler the connection to the prediction result database #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {DataDiagnostics} #' @return #' The server to the summary module #' diff --git a/R/data-diagnostic-main.R b/R/data-diagnostic-main.R index 8a460c12..30fd6a0f 100644 --- a/R/data-diagnostic-main.R +++ b/R/data-diagnostic-main.R @@ -21,7 +21,7 @@ #' #' @details #' Returns the location of the data-diagnostic helper file -#' +#' @family {DataDiagnostics} #' @return #' string location of the data-diagnostic helper file #' @@ -37,7 +37,7 @@ dataDiagnosticHelperFile <- function(){ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {DataDiagnostics} #' @return #' The user interface to the data-diagnostic viewer module #' @@ -72,7 +72,7 @@ dataDiagnosticViewer <- function(id = 'dataDiag') { #' @param id the unique reference id for the module #' @param connectionHandler a connection to the database with the results #' @param resultDatabaseSettings a list containing the data-diagnostic result schema -#' +#' @family {DataDiagnostics} #' @return #' The server for the data-diagnostic module #' diff --git a/R/data-diagnostic-summary.R b/R/data-diagnostic-summary.R index b9b72614..9486a591 100644 --- a/R/data-diagnostic-summary.R +++ b/R/data-diagnostic-summary.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {DataDiagnostics} #' @return #' The user interface to the summary module #' @@ -47,7 +47,7 @@ dataDiagnosticSummaryViewer <- function(id) { #' @param id the unique reference id for the module #' @param connectionHandler the connection to the prediction result database #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {DataDiagnostics} #' @return #' The server to the summary module #' diff --git a/R/datasources-main.R b/R/datasources-main.R index 16ee7743..23ab5d7e 100644 --- a/R/datasources-main.R +++ b/R/datasources-main.R @@ -22,6 +22,7 @@ #' Define the helper file for the module #' #' @return The helper html file for the datasources module +#' @family {Utils} #' @export #' @family {Utils} datasourcesHelperFile <- function() { @@ -37,6 +38,7 @@ datasourcesHelperFile <- function() { #' @param id The unique id for the datasources viewer namespace #' #' @return The UI for the datasources module +#' @family {Utils} #' @export #' @family {Utils} datasourcesViewer <- function(id) { @@ -47,15 +49,7 @@ datasourcesViewer <- function(id) { width = "100%", title = shiny::span(shiny::icon("database"), "Data Sources"), solidHeader = TRUE, - - shinydashboard::box( - collapsible = TRUE, - collapsed = FALSE, - title = shiny::span( shiny::icon("circle-question"), "Help & Information"), - width = "100%", - shiny::htmlTemplate(system.file("datasources-www", "datasources.html", package = utils::packageName())) - ), - + shiny::tabsetPanel( type = 'pills', id = ns('mainPanel'), @@ -79,6 +73,7 @@ datasourcesViewer <- function(id) { #' @param resultDatabaseSettings A named list containing the cohort generator results database details (schema, table prefix) #' #' @return The server for the datasources module +#' @family {Utils} #' @export #' @family {Utils} datasourcesServer <- function( @@ -175,6 +170,9 @@ datasourcesServer <- function( resultTableServer(id = "datasourcesTable", df = datasourcesData, colDefsInput = datasourcesColList, + selectedCols = c("cdmSourceName", "cdmSourceAbbreviation", "cdmHolder", + "sourceReleaseDate", "cdmReleaseDate", "cdmVersion", + "vocabularyVersion", "maxObsPeriodEndDate"), downloadedFileName = "datasourcesTable-") return(invisible(NULL)) diff --git a/R/cohort-method-attrition.R b/R/estimation-cohort-method-attrition.R similarity index 99% rename from R/cohort-method-attrition.R rename to R/estimation-cohort-method-attrition.R index 31ed7da5..649b5b06 100644 --- a/R/cohort-method-attrition.R +++ b/R/estimation-cohort-method-attrition.R @@ -19,7 +19,7 @@ #' The module viewer for rendering the PLE attrition results #' #' @param id the unique reference id for the module -#' +#' @family {Estimation} #' @return #' The user interface to the cohort method attrition #' @@ -44,7 +44,7 @@ cohortMethodAttritionViewer <- function(id) { #' @param selectedRow the selected row from the main results table #' @param connectionHandler the connection to the PLE results database #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {Estimation} #' @return #' the PLE attrition results content server #' diff --git a/R/cohort-method-covariateBalance.R b/R/estimation-cohort-method-covariateBalance.R similarity index 81% rename from R/cohort-method-covariateBalance.R rename to R/estimation-cohort-method-covariateBalance.R index 52f56bee..70410569 100644 --- a/R/cohort-method-covariateBalance.R +++ b/R/estimation-cohort-method-covariateBalance.R @@ -20,7 +20,7 @@ #' The module viewer for rendering the PLE covariate balance analysis #' #' @param id the unique reference id for the module -#' +#' @family {Estimation} #' @return #' The user interface to the cohort method covariate balance results #' @@ -30,6 +30,22 @@ cohortMethodCovariateBalanceViewer <- function(id) { ns <- shiny::NS(id) shiny::div( + + shiny::tabsetPanel( + type = 'pills', + id = ns('covariateBalance'), + + shiny::tabPanel( + title = "Covariate Balance Table", + resultTableViewer( + ns("balanceTable"), + downloadedFileName = "covariateBalanceTable-" + ) + ), + + shiny::tabPanel( + title = "Covariate Balance Plot", + shiny::uiOutput(outputId = ns("hoverInfoBalanceScatter")), plotly::plotlyOutput(ns("balancePlot")), @@ -42,8 +58,12 @@ cohortMethodCovariateBalanceViewer <- function(id) { shiny::textInput(ns("covariateHighlight"), "Highlight covariates containing:", ), shiny::actionButton(ns("covariateHighlightButton"), "Highlight") + ) + ) - ) + ) + + #) } @@ -55,7 +75,7 @@ cohortMethodCovariateBalanceViewer <- function(id) { #' @param connectionHandler the connection to the PLE results database #' @param resultDatabaseSettings a list containing the result schema and prefixes #' @param metaAnalysisDbIds metaAnalysisDbIds -#' +#' @family {Estimation} #' @return #' the PLE covariate balance content server #' @@ -65,12 +85,17 @@ cohortMethodCovariateBalanceServer <- function( selectedRow, connectionHandler, resultDatabaseSettings, - metaAnalysisDbIds = NULL) { + metaAnalysisDbIds = NULL + ) { shiny::moduleServer( id, function(input, output, session) { + options <- getCmOptions( + connectionHandler, + resultDatabaseSettings + ) balance <- shiny::reactive({ row <- selectedRow() @@ -85,7 +110,7 @@ cohortMethodCovariateBalanceServer <- function( comparatorId = row$comparatorId, databaseId = row$databaseId, analysisId = row$analysisId)}, - error = function(e){return(NULL)} + error = function(e){return(data.frame())} ) return(balance) }) @@ -233,28 +258,91 @@ cohortMethodCovariateBalanceServer <- function( ggplot2::ggsave(file = file, plot = balanceSummaryPlot(), width = 12, height = 5.5) }) + #covariate balance table + + #first join to nice database names + + balanceNice <- shiny::reactive( + { + balance <- balance() + dbNames <- getDatabaseName(connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings) + comb <- dplyr::inner_join(balance, dbNames) %>% + dplyr::relocate("cdmSourceAbbreviation", .after = "databaseId") %>% + dplyr::select(-c("databaseId")) + } + ) + + #load custom colDefs + cmBalanceColList <- ParallelLogger::loadSettingsFromJson( + system.file("components-columnInformation", + "cohortMethod-covariate-balance-colDefs.json", + package = "OhdsiShinyModules" + ) + ) + + #then render the balance table + renderBalanceTable <- shiny::reactive( + { + balanceNice() + } + ) + + resultTableServer( + id = "balanceTable", + df = renderBalanceTable, + colDefsInput = cmBalanceColList, + downloadedFileName = "covariateBalanceTable-" + ) + + + + } ) } +#fetching data functions + +getDatabaseName <- function( + connectionHandler, + resultDatabaseSettings +){ + + sql <- 'select distinct d.cdm_source_abbreviation, i.database_id + from @result_schema.@cm_table_prefixCOVARIATE_BALANCE i + inner join @result_schema.@database_table_name d + on d.database_id = i.database_id + ;' + + resultTable <- connectionHandler$queryDb( + sql = sql, + result_schema = resultDatabaseSettings$schema, + cm_table_prefix = resultDatabaseSettings$cmTablePrefix, + database_table_name = resultDatabaseSettings$databaseTable + ) + + return(resultTable) +} + getCohortMethodCovariateBalanceShared <- function( connectionHandler, resultDatabaseSettings, targetId, comparatorId, analysisId, + #covariateAnalysisId, databaseId = NULL ) { - shiny::withProgress(message = 'Extracting covariate balance', value = 0, { + #shiny::withProgress(message = 'Extracting covariate balance', value = 0, { - shiny::incProgress(1/6, detail = paste("Writing sql")) + #shiny::incProgress(1/6, detail = paste("Writing sql")) sql <- " SELECT cmscb.database_id, - cmscb.covariate_id, cmc.covariate_name, - -- cmc.covariate_analysis_id analysis_id, #TODO: once @table_prefixanalysis_id bug fixed + --cmc.analysis_id analysis_id, cmscb.target_mean_before before_matching_mean_treated, cmscb.comparator_mean_before before_matching_mean_comparator, abs(cmscb.std_diff_before) abs_before_matching_std_diff, --absBeforeMatchingStdDiff @@ -269,10 +357,11 @@ getCohortMethodCovariateBalanceShared <- function( cmscb.target_id = @target_id AND cmscb.comparator_id = @comparator_id AND cmscb.analysis_id = @analysis_id + --AND cmc.covariate_analysis_id = @covariate_analysis_id AND cmscb.database_id = '@database_id' " - shiny::incProgress(1/3, detail = paste("Extracting")) + #shiny::incProgress(1/3, detail = paste("Extracting")) result <- connectionHandler$queryDb( sql = sql, results_schema = resultDatabaseSettings$schema, @@ -280,11 +369,12 @@ getCohortMethodCovariateBalanceShared <- function( target_id = targetId, comparator_id = comparatorId, analysis_id = analysisId, + #covariate_analysis_id = covariateAnalysisId, database_id = databaseId ) - shiny::incProgress(3/3, detail = paste("Done - nrows: ", nrow(result))) - }) + #shiny::incProgress(3/3, detail = paste("Done - nrows: ", nrow(result))) + # }) return( result @@ -320,7 +410,7 @@ getCohortMethodCovariateBalanceSummary <- function( balanceAfter <- balance %>% dplyr::group_by(.data$databaseId) %>% dplyr::summarise(covariateCount = dplyr::n(), - qs = stats::quantile(.data$afterMatchingStdDiff, c(0, 0.25, 0.5, 0.75, 1)), prob = c("ymin", "lower", "median", "upper", "ymax")) %>% + qs = stats::quantile(.data$absAfterMatchingStdDiff, c(0, 0.25, 0.5, 0.75, 1)), prob = c("ymin", "lower", "median", "upper", "ymax")) %>% tidyr::spread(key = "prob", value = "qs") balanceAfter[, "type"] <- afterLabel @@ -364,7 +454,7 @@ plotCohortMethodCovariateBalanceScatterPlotNew <- function( x1 = limits[2], xref = "paper", y0 = 0, - y1 = limits[2], + y1 = limits[2], line = list(color = color, dash = 'dash') ) } @@ -378,7 +468,17 @@ plotCohortMethodCovariateBalanceScatterPlotNew <- function( colors = colors ) %>% plotly::layout( - shapes = list(xyline(limits)), + #shapes = list(xyline(limits)), + shapes = list(list( + type = "line", + x0 = 0, + x1 = ~max(absBeforeMatchingStdDiff, absAfterMatchingStdDiff), + xref = "x", + y0 = 0, + y1 = ~max(absBeforeMatchingStdDiff, absAfterMatchingStdDiff), + yref = "y", + line = list(color = "grey", dash = "dash") + )), plot_bgcolor = "#e5ecf6", xaxis = list(title = beforeLabel, range = limits), yaxis = list(title = afterLabel, range = limits) @@ -407,8 +507,8 @@ plotCohortMethodCovariateBalanceSummary <- function(balanceSummary, upper = .data$upper, ymax = .data$ymax, group = .data$databaseId)) + - ggplot2::geom_errorbar(ggplot2::aes(ymin = .data$ymin, ymax = .data$ymin), size = 1) + - ggplot2::geom_errorbar(ggplot2::aes(ymin = .data$ymax, ymax = .data$ymax), size = 1) + + ggplot2::geom_errorbar(ggplot2::aes(ymin = .data$ymin, ymax = .data$ymin), linewidth = 1) + + ggplot2::geom_errorbar(ggplot2::aes(ymin = .data$ymax, ymax = .data$ymax), linewidth = 1) + ggplot2::geom_boxplot(stat = "identity", fill = grDevices::rgb(0, 0, 0.8, alpha = 0.25), size = 1) + ggplot2::geom_hline(yintercept = 0) + ggplot2::scale_x_continuous(limits = c(0.5, max(vizData$x) + 1.75)) + @@ -464,3 +564,34 @@ plotCohortMethodCovariateBalanceSummary <- function(balanceSummary, plot <- gridExtra::grid.arrange(data_table, plot, ncol = 2) return(plot) } + +getCmOptions <- function(connectionHandler, + resultDatabaseSettings){ + + sql <- 'select distinct covariate_analysis_id, covariate_analysis_name + from @result_schema.@cm_table_prefixCOVARIATE_ANALYSIS;' + + #shiny::incProgress(1/3, detail = paste("Created SQL - Extracting targets")) + + covariateAnalyses <- connectionHandler$queryDb( + sql = sql, + result_schema = resultDatabaseSettings$schema, + cm_table_prefix = resultDatabaseSettings$cmTablePrefix + ) + covariateAnalysisIds <- covariateAnalyses$covariateAnalysisId + names(covariateAnalysisIds) <- covariateAnalyses$covariateAnalysisName + + return( + list( + covariateAnalysisIds = covariateAnalysisIds + ) + ) + +} + + + + + + + diff --git a/R/estimation-cohort-method-diagnostics.R b/R/estimation-cohort-method-diagnostics.R new file mode 100644 index 00000000..56430cf2 --- /dev/null +++ b/R/estimation-cohort-method-diagnostics.R @@ -0,0 +1,264 @@ +estimationCmDiagnosticViewer <- function(id=1) { + ns <- shiny::NS(id) + + resultTableViewer(ns("cmDiagnosticsTable")) + +} + + +estimationCmDiagnosticServer <- function( + id, + connectionHandler, + resultDatabaseSettings = list(port = 1), + targetIds, + comparatorIds, + outcomeId +) { + shiny::moduleServer( + id, + function(input, output, session) { + + + + cmDiagnostics <- shiny::reactive({ + estimationGetCmDiagnostics( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + comparatorIds = comparatorIds, + outcomeId = outcomeId + ) + }) + + resultTableServer( + id = "cmDiagnosticsTable", + df = cmDiagnostics, + colDefsInput = estimationGetCmDiagnosticColDefs(), + selectedCols = c( + 'databaseName', + 'analysis', + 'target', + 'comparator', + 'summaryValue' + ) + ) + + + } + ) +} + + +estimationGetCmDiagnostics <- function( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + comparatorIds = comparatorIds, + outcomeId = outcomeId +){ + targetIds <- targetIds() + comparatorIds <- comparatorIds() + outcomeId <- outcomeId() + + sql <- " + SELECT DISTINCT + dmd.cdm_source_abbreviation database_name, + cma.description analysis, + cgcd1.cohort_name target, + cgcd2.cohort_name comparator, + cgcd3.cohort_name outcome, + cmds.max_sdm, + cmds.shared_max_sdm, + cmds.equipoise, + cmds.mdrr, + cmds.ease, + cmds.balance_diagnostic, + cmds.shared_balance_diagnostic, -- added back + cmds.equipoise_diagnostic, + cmds.mdrr_diagnostic, + cmds.ease_diagnostic, + cmds.unblind + FROM + @schema.@cm_table_prefixdiagnostics_summary cmds + INNER JOIN @schema.@cm_table_prefixanalysis cma ON cmds.analysis_id = cma.analysis_id + INNER JOIN @schema.@database_table dmd ON dmd.database_id = cmds.database_id + INNER JOIN @schema.@cg_table_prefixcohort_definition cgcd1 ON cmds.target_id = cgcd1.cohort_definition_id + INNER JOIN @schema.@cg_table_prefixcohort_definition cgcd2 ON cmds.comparator_id = cgcd2.cohort_definition_id + INNER JOIN @schema.@cg_table_prefixcohort_definition cgcd3 ON cmds.outcome_id = cgcd3.cohort_definition_id + + where cgcd1.cohort_definition_id in (@targets) + {@use_comparators}?{and cgcd2.cohort_definition_id in (@comparators)} + and cgcd3.cohort_definition_id in (@outcomes) + {@use_analyses}?{and cma.analysis_id in (@analyses)} + ; + " + print(comparatorIds) + + result <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + cm_table_prefix = resultDatabaseSettings$cmTablePrefix, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix, + database_table = resultDatabaseSettings$databaseTable, + + targets = paste0(targetIds, collapse = ','), + comparators = paste0(comparatorIds, collapse = ','), + outcomes = paste0(outcomeId, collapse = ','), + + use_comparators = ifelse(is.null(comparatorIds), F, T), + use_analyses = F + ) + + # adding percent fail for summary + result$summaryValue <- apply( + X = result[, grep('Diagnostic', colnames(result))], + MARGIN = 1, + FUN = function(x){ + + if(sum(x %in% c('FAIL'))>0){ + return('Fail') + } else if(sum(x %in% c('WARNING')) >0){ + return(sum(x %in% c('WARNING'))) + } else{ + return('Pass') + } + } + ) + + # add summaryValue after outcome + result <- result %>% + dplyr::relocate("summaryValue", .after = "outcome") + + return( + result + ) + +} + + +estimationGetCmDiagnosticColDefs <- function(){ + result <- list( + databaseName = reactable::colDef( + header = withTooltip( + "Database", + "The database name" + ), + sticky = "left" + ), + target = reactable::colDef( + header = withTooltip( + "Target", + "The target cohort of interest" + ), + sticky = "left" + ), + comparator = reactable::colDef( + header = withTooltip( + "Comparator", + "The comparator cohort of interest" + ), + sticky = "left" + ), + outcome = reactable::colDef( + show = F + ), + summaryValue = reactable::colDef( + header = withTooltip( + "Diagnostic", + "The overall result of the diagostics" + ), + style = function(value) { + color <- 'orange' + if(is.na(value)){ + color <- 'black' + }else if(value == 'Pass'){ + color <- '#AFE1AF' + }else if(value == 'Fail'){ + color <- '#E97451' + } + list(background = color) + } + ), + analysis = reactable::colDef( + header = withTooltip( + "Analysis", + "The analysis name" + ) + ), + + mdrr = reactable::colDef( + header = withTooltip( + "MDRR", + "The minimum detectible relative risk" + ), + format = reactable::colFormat(digits = 4) + ), + ease = reactable::colDef( + header = withTooltip( + "EASE", + "The expected absolute systematic error" + ), + format = reactable::colFormat(digits = 4) + ), + maxSdm = reactable::colDef( + header = withTooltip( + "Max SDM", + "The maximum absolute standardized difference of mean" + ), + format = reactable::colFormat(digits = 4) + ), + sharedMaxSdm = reactable::colDef( + header = withTooltip( + "Shared Max SDM", + "The maximum absolute standardized difference of mean of the shared balance (shared across outcomes)" + ), + format = reactable::colFormat(digits = 4) + ), + equipoise = reactable::colDef( + header = withTooltip( + "Equipoise", + "The fraction of the study population with a preference score between 0.3 and 0.7" + ), + format = reactable::colFormat(digits = 4) + ), + balanceDiagnostic = reactable::colDef( + header = withTooltip( + "Balance Diagnostic", + "Pass / warning / fail classification of the balance diagnostic (Max SDM)" + ) + ), + mdrrDiagnostic = reactable::colDef( + header = withTooltip( + "MDRR Diagnostic", + "Pass / warning / fail classification of the MDRR diagnostic" + ) + ), + sharedBalanceDiagnostic = reactable::colDef( + header = withTooltip( + "Shared Balance Diagnostic", + "Pass / warning / fail classification of the shared balance diagnostic (Shared Max SDM)" + ) + ), + easeDiagnostic = reactable::colDef( + header = withTooltip( + "Ease Diagnostic", + "Pass / warning / fail classification of the EASE diagnostic" + ) + ), + equipoiseDiagnostic = reactable::colDef( + header = withTooltip( + "Equipoise Diagnostic", + "Pass / warning / fail classification of the equipoise diagnostic" + ) + ), + + unblind = reactable::colDef( + header = withTooltip( + "Unblind", + "If the value is 1 then the diagnostics passed and results can be unblinded" + ) + ) + ) + + return(result) +} \ No newline at end of file diff --git a/R/cohort-method-full-result.R b/R/estimation-cohort-method-full-result.R similarity index 93% rename from R/cohort-method-full-result.R rename to R/estimation-cohort-method-full-result.R index 2c05c501..7f4631cd 100644 --- a/R/cohort-method-full-result.R +++ b/R/estimation-cohort-method-full-result.R @@ -1,4 +1,4 @@ -cohortMethodFullResultViewer <- function(id) { +estimationCmFullResultViewer <- function(id) { ns <- shiny::NS(id) shiny::div( @@ -49,7 +49,7 @@ cohortMethodFullResultViewer <- function(id) { } -cohortMethodFullResultServer <- function( +estimationCmFullResultServer <- function( id, connectionHandler, resultDatabaseSettings, @@ -76,11 +76,11 @@ cohortMethodFullResultServer <- function( "cdmSourceAbbreviation" ) %>% dplyr::rename( - 'Target' = .data$target, - 'Comparator' = .data$comparator, - 'Outcome' = .data$outcome, - 'Analysis' = .data$description, - 'Database' = .data$cdmSourceAbbreviation + Target = "target", + Comparator = "comparator", + Outcome = "outcome", + Analysis = "description", + Database = "cdmSourceAbbreviation" ) }) diff --git a/R/cohort-method-kaplainMeier.R b/R/estimation-cohort-method-kaplainMeier.R similarity index 99% rename from R/cohort-method-kaplainMeier.R rename to R/estimation-cohort-method-kaplainMeier.R index 87906c13..9f8bbf0d 100644 --- a/R/cohort-method-kaplainMeier.R +++ b/R/estimation-cohort-method-kaplainMeier.R @@ -20,7 +20,7 @@ #' The module viewer for rendering the PLE Kaplan Meier curve #' #' @param id the unique reference id for the module -#' +#' @family {Estimation} #' @return #' The module viewer for Kaplan Meier objects #' @@ -45,7 +45,7 @@ cohortMethodKaplanMeierViewer <- function(id) { #' @param selectedRow the selected row from the main results table #' @param connectionHandler the connection to the PLE results database #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {Estimation} #' @return #' the PLE Kaplain Meier content server #' diff --git a/R/estimation-cohort-method-plots.R b/R/estimation-cohort-method-plots.R new file mode 100644 index 00000000..314a9c93 --- /dev/null +++ b/R/estimation-cohort-method-plots.R @@ -0,0 +1,156 @@ +estimationCmPlotsViewer <- function(id=1) { + ns <- shiny::NS(id) + shinyWidgets::addSpinner( + shiny::plotOutput(ns('esCohortMethodPlot')), + spin = 'rotating-plane' + ) +} + + +estimationCmPlotsServer <- function( + id, + connectionHandler, + resultDatabaseSettings = list(port = 1), + cmData +) { + shiny::moduleServer( + id, + function(input, output, session) { + + height <- shiny::reactive({ + if(is.null(cmData()$target)){ + return(100) + } + length(unique(cmData()$target))*250 + 250 + }) + + output$esCohortMethodPlot <- shiny::renderPlot( + estimationCreateCmPlot( + data = cmData + ), + height = height + ) + + } + ) +} + +estimationCreateCmPlot <- function(data) { + data <- data() + if(nrow(data) == 0){ + shiny::showNotification('No results to plot') + return(NULL) + } + data <- data[!is.na(data$calibratedRr),] + if(nrow(data) == 0){ + shiny::showNotification('No results to plot') + return(NULL) + } + data$database <- data$cdmSourceAbbreviation + + if(is.null(data$comparator)){ + shiny::showNotification('No results to plot') + return(NULL) + } + + + renameDf <- data.frame( + shortName = paste0( + 1:length(unique(data$comparator)), + ') ', + substring(sort(unique(data$comparator)), 1,50), + '...' + ), + comparator = sort(unique(data$comparator)) + ) + + + data <- merge( + data, + renameDf, + by = "comparator" + ) + + # make sure bayesian is at top + db <- unique(data$database) + bInd <- grep('bayesian', tolower(db)) + withoutb <- db[-bInd] + b <- db[bInd] + data$database <- factor( + x = data$database, + levels = c(b, sort(withoutb)) + ) + metadata <- data[data$database == b,] + + breaks <- c(0.1, 0.25, 0.5, 1, 2, 4, 6, 8) + + ### Add table above the graph + renameDf$comparator <- sapply( + strwrap(renameDf$comparator, width = 150, simplify = FALSE), + paste, + collapse = "\n" + ) + + tt <- gridExtra::ttheme_default( + base_size = 8, + colhead=list(fg_params = list(parse=TRUE)) + ) + tbl <- gridExtra::tableGrob( + renameDf, + rows=NULL, + theme=tt + ) + plotList <- list(tbl) # adding table first + + for(target in unique(data$target)){ # per targets + + title <- sprintf("%s", target) + plotList[[length(plotList) + 1]] <- ggplot2::ggplot( + data = data %>% dplyr::filter(.data$target == !!target), + ggplot2::aes(x = .data$calibratedRr, y = .data$shortName)) + + ggplot2::geom_vline(xintercept = 1, size = 0.5) + + ggplot2::geom_point(color = "#000088", alpha = 0.8) + + ggplot2::geom_errorbarh( + ggplot2::aes( + xmin = .data$calibratedCi95Lb, + xmax = .data$calibratedCi95Ub + ), + height = 0.5, + color = "#000088", + alpha = 0.8 + ) + + ggplot2::scale_x_log10( + "Effect size (Hazard Ratio)", + breaks = breaks, + labels = breaks + ) + + + # shade the bayesian + ggplot2::geom_rect( + data = metadata %>% dplyr::filter(.data$target == !!target), + ggplot2::aes(fill = .data$database), + xmin = -Inf, + xmax = Inf, + ymin = -Inf, + ymax = Inf, + alpha = 0.2 + ) + + + ggplot2::coord_cartesian(xlim = c(0.1, 10)) + + ggplot2::facet_grid(.data$database ~ .data$description) + + ggplot2::ggtitle(title) + + ggplot2::theme( + axis.title.y = ggplot2::element_blank(), + panel.grid.minor = ggplot2::element_blank(), + strip.text.y.right = ggplot2::element_text(angle = 0), + legend.position = "none" + ) + } + + plot <- do.call( + gridExtra::grid.arrange, + list(grobs = plotList, ncol =1) + ) + + return(plot) +} diff --git a/R/cohort-method-populationCharacteristics.R b/R/estimation-cohort-method-populationCharacteristics.R similarity index 98% rename from R/cohort-method-populationCharacteristics.R rename to R/estimation-cohort-method-populationCharacteristics.R index c37cde83..c87a934f 100644 --- a/R/cohort-method-populationCharacteristics.R +++ b/R/estimation-cohort-method-populationCharacteristics.R @@ -20,7 +20,7 @@ #' The module viewer for rendering the PLE population characteristics #' #' @param id the unique reference id for the module -#' +#' @family {Estimation} #' @return #' The user interface to the cohort method population characteristics objects #' @@ -41,7 +41,7 @@ cohortMethodPopulationCharacteristicsViewer <- function(id) { #' @param selectedRow the selected row from the main results table #' @param connectionHandler the connection to the PLE results database #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {Estimation} #' @return #' the PLE population characteristics content server #' @@ -149,7 +149,8 @@ cohortMethodPopulationCharacteristicsServer <- function( digits = 2 ), sortable = F - ) + ), + label = reactable::colDef(show = T) ) ) @@ -251,7 +252,7 @@ getCohortMethodPopChar <- function( .data$covariateName ) - # remove text before covariateNames + # remove text before covariateNames - TODO generalize this? txtRms <- c( 'age group: ', 'condition_era group during day -365 through 0 days relative to index: ', @@ -260,7 +261,7 @@ getCohortMethodPopChar <- function( for(txtRm in txtRms){ result$covariateName <- gsub(txtRm,'', result$covariateName) } - + return( result ) diff --git a/R/cohort-method-power.R b/R/estimation-cohort-method-power.R similarity index 98% rename from R/cohort-method-power.R rename to R/estimation-cohort-method-power.R index aa57bf91..2acec47c 100644 --- a/R/cohort-method-power.R +++ b/R/estimation-cohort-method-power.R @@ -20,7 +20,7 @@ #' The module viewer for rendering the PLE power analysis #' #' @param id the unique reference id for the module -#' +#' @family {Estimation} #' @return #' The user interface to the cohort method power calculation results #' @@ -44,7 +44,7 @@ cohortMethodPowerViewer <- function(id) { #' @param selectedRow the selected row from the main results table #' @param connectionHandler the connection to the PLE results database #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {Estimation} #' @return #' the PLE systematic error power server #' @@ -177,11 +177,7 @@ prepareCohortMethodPowerTable <- function( connectionHandler , resultDatabaseSettings ) { - #analyses <- getCohortMethodAnalyses( - # connectionHandler = connectionHandler, - # resultDatabaseSettings = resultDatabaseSettings - #) - #table <- merge(mainResults, analyses) + table <- mainResults alpha <- 0.05 power <- 0.8 diff --git a/R/cohort-method-propensityModel.R b/R/estimation-cohort-method-propensityModel.R similarity index 98% rename from R/cohort-method-propensityModel.R rename to R/estimation-cohort-method-propensityModel.R index 53877b20..9cc201bc 100644 --- a/R/cohort-method-propensityModel.R +++ b/R/estimation-cohort-method-propensityModel.R @@ -20,7 +20,7 @@ #' The module viewer for rendering the PLE propensity score model covariates/coefficients #' #' @param id the unique reference id for the module -#' +#' @family {Estimation} #' @return #' The user interface to the cohort method propensity score model covariates/coefficients #' @@ -42,7 +42,7 @@ cohortMethodPropensityModelViewer <- function(id) { #' @param selectedRow the selected row from the main results table #' @param connectionHandler the connection to the PLE results database #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {Estimation} #' @return #' the PLE propensity score model #' diff --git a/R/cohort-method-propensityScoreDistribution.R b/R/estimation-cohort-method-propensityScoreDistribution.R similarity index 99% rename from R/cohort-method-propensityScoreDistribution.R rename to R/estimation-cohort-method-propensityScoreDistribution.R index 08ae4384..7a5eb204 100644 --- a/R/cohort-method-propensityScoreDistribution.R +++ b/R/estimation-cohort-method-propensityScoreDistribution.R @@ -20,7 +20,7 @@ #' The module viewer for rendering the propensity score distribution #' #' @param id the unique reference id for the module -#' +#' @family {Estimation} #' @return #' The user interface to the cohort method propensity score distribution #' @@ -49,7 +49,7 @@ cohortMethodPropensityScoreDistViewer <- function(id) { #' @param connectionHandler the connection to the PLE results database #' @param resultDatabaseSettings a list containing the result schema and prefixes #' @param metaAnalysisDbIds metaAnalysisDbIds -#' +#' @family {Estimation} #' @return #' the PLE propensity score distribution content server #' diff --git a/R/cohort-method-resultSummary.R b/R/estimation-cohort-method-results.R similarity index 62% rename from R/cohort-method-resultSummary.R rename to R/estimation-cohort-method-results.R index 4e8ea8df..499a9a73 100644 --- a/R/cohort-method-resultSummary.R +++ b/R/estimation-cohort-method-results.R @@ -17,15 +17,7 @@ # limitations under the License. -#' The module viewer for rendering the cohort method results -#' -#' @param id the unique reference id for the module -#' -#' @return -#' The user interface to the cohort method diagnostics viewer -#' -#' @export -cohortMethodResultSummaryViewer <- function(id) { +estimationCmResultsViewer <- function(id) { ns <- shiny::NS(id) shiny::tabsetPanel( @@ -34,13 +26,7 @@ cohortMethodResultSummaryViewer <- function(id) { shiny::tabPanel( title = "Table", - #shinydashboard::box( - # status = 'info', - # width = '100%', - # title = shiny::span('Result Summary'), - # solidHeader = TRUE, - resultTableViewer(ns("resultSummaryTable")) - # ) + resultTableViewer(ns("resultSummaryTable")) ), shiny::tabPanel( @@ -51,7 +37,7 @@ cohortMethodResultSummaryViewer <- function(id) { shiny::icon("arrow-left"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4" ), - cohortMethodFullResultViewer(ns("cmFullResults")) + estimationCmFullResultViewer(ns("cmFullResults")) ) ) @@ -60,22 +46,13 @@ cohortMethodResultSummaryViewer <- function(id) { } -#' The module server for rendering the PLE diagnostics summary -#' -#' @param id the unique reference id for the module -#' @param connectionHandler the connection to the PLE results database -#' @param resultDatabaseSettings a list containing the result schema and prefixes -#' @param inputSelected The target id, comparator id, outcome id and analysis id selected by the user -#' -#' @return -#' the PLE diagnostics summary results -#' -#' @export -cohortMethodResultSummaryServer <- function( +estimationCmResultsServer <- function( id, connectionHandler, resultDatabaseSettings, - inputSelected + targetIds, + comparatorIds, + outcomeId ) { shiny::moduleServer( @@ -88,43 +65,67 @@ cohortMethodResultSummaryServer <- function( shiny::updateTabsetPanel(session, "resultPanel", selected = "Table") }) - data <- shiny::reactive({ - getCmResultData( - connectionHandler, + # extract results from CM tables + cmData <- shiny::reactive({ + estimationGetCmResultData( + connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - inputSelected = inputSelected + targetIds = targetIds, + comparatorIds = comparatorIds, + outcomeId = outcomeId + ) + }) + + # extract results from ES tables if tables exist + esData <- shiny::reactive({ + tryCatch( + { + estimationGetCMMetaEstimation( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + outcomeId = outcomeId + ) + }, error = function(e){print('CM ES error');return(NULL)} ) }) + + data <- shiny::reactive({ + rbind(cmData(), esData()) + }) resultTableOutputs <- resultTableServer( id = "resultSummaryTable", df = data, - colDefsInput = getCmResultSummaryTableColDef(), - addActions = c('results') + colDefsInput = estimationGetCmResultSummaryTableColDef(), + addActions = c('results') # TODO wont work for esData ) selectedRow <- shiny::reactiveVal(value = NULL) shiny::observeEvent(resultTableOutputs$actionCount(), { - if(resultTableOutputs$actionType() == 'results'){ + if(resultTableOutputs$actionType() == 'results'){ # add an and here to only work for cmData selectedRow(data()[resultTableOutputs$actionIndex()$index,]) shiny::updateTabsetPanel(session, "resultPanel", selected = "Results") } }) - cohortMethodFullResultServer( + estimationCmFullResultServer( id = "cmFullResults", connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, selectedRow = selectedRow, actionCount = resultTableOutputs$actionCount ) + + + return(data) } ) } -getCmResultSummaryTableColDef <- function(){ +estimationGetCmResultSummaryTableColDef <- function(){ result <- list( analysisId = reactable::colDef(show = F), @@ -144,7 +145,12 @@ getCmResultSummaryTableColDef <- function(){ ) ), - targetId = reactable::colDef(show = F), + targetId = reactable::colDef(header = withTooltip( + "Target ID", + "The ID of the target cohort of interest" + ) + ), + target = reactable::colDef( header = withTooltip( "Target", @@ -153,7 +159,12 @@ getCmResultSummaryTableColDef <- function(){ minWidth = 300 ), - comparatorId = reactable::colDef(show = F), + comparatorId = reactable::colDef(header = withTooltip( + "Comparator ID", + "The ID of the comparator cohort of interest" + ) + ), + comparator = reactable::colDef( header = withTooltip( "Comparator", @@ -162,7 +173,12 @@ getCmResultSummaryTableColDef <- function(){ minWidth = 300 ), - outcomeId = reactable::colDef(show = F), + outcomeId = reactable::colDef(header = withTooltip( + "Outcome ID", + "The ID of the outcome of interest" + ) + ), + outcome = reactable::colDef( header = withTooltip( "Outcome", @@ -260,21 +276,24 @@ getCmResultSummaryTableColDef <- function(){ return(result) } -getCmResultData <- function( +estimationGetCmResultData <- function( connectionHandler, resultDatabaseSettings, - inputSelected + targetIds, + comparatorIds, + outcomeId, + runEvidenceSynthesis = F ) { - targetIds = inputSelected()$targetIds - outcomeIds = inputSelected()$outcomeIds - comparatorIds = inputSelected()$comparatorIds - analysisIds = inputSelected()$analysisIds + targetIds = targetIds() + comparatorIds = comparatorIds() + outcomeId = outcomeId() - if(is.null(comparatorIds) || is.null(targetIds) || is.null(outcomeIds) || is.null(analysisIds)){ + if(is.null(comparatorIds) || is.null(targetIds) || is.null(outcomeId) ){ return(NULL) } + sql <- " SELECT cma.analysis_id, @@ -333,7 +352,6 @@ FROM where cmr.target_id in (@targets) {@use_comparators}?{and cmr.comparator_id in (@comparators)} and cmr.outcome_id in (@outcomes) - {@use_analyses}?{and cmr.analysis_id in (@analyses)} ; " @@ -346,14 +364,148 @@ FROM targets = paste0(targetIds, collapse = ','), comparators = paste0(comparatorIds, collapse = ','), - outcomes = paste0(outcomeIds, collapse = ','), - analyses = paste0(analysisIds, collapse = ','), - + outcomes = paste0(outcomeId, collapse = ','), use_comparators = !is.null(comparatorIds), - use_analyses = !is.null(analysisIds) ) return( result ) } + + +estimationGetCMMetaEstimation <- function( + connectionHandler, + resultDatabaseSettings, + targetIds, + outcomeId +){ + targetIds <- targetIds() + outcomeId <- outcomeId() + + sql <- "select + r.analysis_id, + a.description, + 0 as database_id, + ev.evidence_synthesis_description as cdm_source_abbreviation, + r.target_id, + c1.cohort_name as target, + r.outcome_id, + c3.cohort_name as outcome, + r.comparator_id, + c2.cohort_name as comparator, + NULL as rr, + NULL as ci_95_lb, + NULL as ci_95_ub, + NULL as p, + NULL as log_rr, + NULL as se_log_rr, + 0 as target_subjects, + 0 as comparator_subjects, + 0 as target_days, + 0 as comparator_days, + 0 as target_outcomes, + 0 as comparator_outcomes, + r.calibrated_rr, + r.calibrated_ci_95_lb, + r.calibrated_ci_95_ub, + r.calibrated_p, + r.calibrated_log_rr, + r.calibrated_se_log_rr, + 1 unblind + + from + @schema.@es_table_prefixcm_result as r + inner join + @schema.@cm_table_prefixtarget_comparator_outcome as tco + on + r.target_id = tco.target_id and + r.comparator_id = tco.comparator_id and + r.outcome_id = tco.outcome_id + + inner join + + @schema.@es_table_prefixcm_diagnostics_summary as unblind + on + r.analysis_id = unblind.analysis_id and + r.target_id = unblind.target_id and + r.comparator_id = unblind.comparator_id and + r.outcome_id = unblind.outcome_id + + inner join + @schema.@cg_table_prefixcohort_definition as c1 + on c1.cohort_definition_id = r.target_id + + inner join + @schema.@cg_table_prefixcohort_definition as c2 + on c2.cohort_definition_id = r.comparator_id + + inner join + @schema.@cg_table_prefixcohort_definition as c3 + on c3.cohort_definition_id = r.outcome_id + + inner join + @schema.@cm_table_prefixanalysis as a + on a.analysis_id = r.analysis_id + + inner join + @schema.@es_table_prefixanalysis as ev + on ev.evidence_synthesis_analysis_id = r.evidence_synthesis_analysis_id + + where + r.calibrated_rr != 0 and + tco.outcome_of_interest = 1 and + unblind.unblind = 1 and + r.target_id in (@target_ids) and + r.outcome_id = @outcome_id + ;" + + result <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + cm_table_prefix = resultDatabaseSettings$cmTablePrefix, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix, + es_table_prefix = resultDatabaseSettings$esTablePrefix, + outcome_id = outcomeId, + target_ids = paste0(targetIds, collapse = ',') + ) %>% + dplyr::mutate( + calibratedP = ifelse( + .data$calibratedRr < 1, + computeTraditionalP( + logRr = .data$calibratedLogRr, + seLogRr = .data$calibratedSeLogRr, + twoSided = FALSE, + upper = TRUE + ), + .data$calibratedP / 2) + ) + + return(unique(result)) +} + + +# Function to format results +# used by both cm and sccs +computeTraditionalP <- function( + logRr, + seLogRr, + twoSided = TRUE, + upper = TRUE +) +{ + z <- logRr/seLogRr + + pUpperBound <- 1 - stats::pnorm(z) + pLowerBound <- stats::pnorm(z) + + if (twoSided) { + return(2 * pmin(pUpperBound, pLowerBound)) + } + else if (upper) { + return(pUpperBound) + } + else { + return(pLowerBound) + } +} diff --git a/R/cohort-method-systematicError.R b/R/estimation-cohort-method-systematicError.R similarity index 99% rename from R/cohort-method-systematicError.R rename to R/estimation-cohort-method-systematicError.R index cc89cafb..fa1ce54d 100644 --- a/R/cohort-method-systematicError.R +++ b/R/estimation-cohort-method-systematicError.R @@ -20,7 +20,7 @@ #' The module viewer for rendering the PLE systematic error objects #' #' @param id the unique reference id for the module -#' +#' @family {Estimation} #' @return #' The user interface to the cohort method systematic error module #' @@ -52,7 +52,7 @@ cohortMethodSystematicErrorViewer <- function(id) { #' @param selectedRow the selected row from the main results table #' @param connectionHandler the connection handler to the result databases #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {Estimation} #' @return #' the PLE systematic error content server #' diff --git a/R/estimation-main.R b/R/estimation-main.R new file mode 100644 index 00000000..21bb7bd4 --- /dev/null +++ b/R/estimation-main.R @@ -0,0 +1,414 @@ +# @file Estimation-main.R +# +# Copyright 2024 Observational Health Data Sciences and Informatics +# +# This file is part of OhdsiShinyModules +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +#' The location of the estimation module helper file +#' +#' @details +#' Returns the location of the characterization helper file +#' @family {Estimation} +#' @return +#' string location of the characterization helper file +#' +#' @export +estimationHelperFile <- function(){ + fileLoc <- system.file('estimation-www', "estimation.html", package = "OhdsiShinyModules") + return(fileLoc) +} + +#' The module viewer for exploring characterization studies +#' +#' @details +#' The user specifies the id for the module +#' +#' @param id the unique reference id for the module +#' @family {Estimation} +#' @return +#' The user interface to the characterization viewer module +#' +#' @export +estimationViewer <- function(id=1) { + ns <- shiny::NS(id) + + shinydashboard::box( + status = 'info', + width = '100%', + title = shiny::span( shiny::icon("table"), "Estimation Viewer"), + solidHeader = TRUE, + + # pick a targetId of interest + shiny::uiOutput(ns("targetSelection")), + + inputSelectionDfViewer(id = ns('targetSelected'), title = 'Selected'), + + + # first show diagnostics with: + # database, analysis, pass/fail, viewResult/viewDiagnostic + # extracts from SCCS/CM/Evidence Synthesis + + shiny::conditionalPanel( + condition = 'input.targetSelect', + ns = ns, + + shiny::tabsetPanel( + type = 'pills', + id = ns('mainPanel'), + + shiny::tabPanel( + title = 'Diagnostics', + shiny::tabsetPanel( + type = 'pills', + id = ns('diagnosticsPanel') + ) + ), + + shiny::tabPanel( + title = 'Results', + shiny::tabsetPanel( + type = 'pills', + id = ns('resultsPanel') + ) + ), + ) + ) # end conditional panel + + ) + +} + + +#' The module server for exploring estimation studies +#' +#' @details +#' The user specifies the id for the module +#' +#' @param id the unique reference id for the module +#' @param connectionHandler a connection to the database with the results +#' @param resultDatabaseSettings a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix +#' @family {Estimation} +#' @return +#' The server for the estimation module +#' +#' @export +estimationServer <- function( + id, + connectionHandler, + resultDatabaseSettings = list(port = 1) +) { + shiny::moduleServer( + id, + function(input, output, session) { + + # this function checks tables exist for the tabs + # and returns the tabs that should be displayed + # as the tables exist + estimationTypes <- getEstimationTypes( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings + ) + + # add the tabs based on results + types <- list( + c("Cohort Method","estimationCmDiagnosticViewer", "estimationCmDiagnostic", "diagnosticsPanel", "Cohort Method"), + c("SCCS", "estimationSccsDiagnosticViewer", "estimationSccsDiagnostic", "diagnosticsPanel", "SCCS"), + c("Cohort Method", "estimationCmResultsViewer", "estimationCmResults", "resultsPanel", "Cohort Method Table"), + c("Cohort Method", "estimationCmPlotsViewer", "estimationCmPlots", "resultsPanel", "Cohort Method Plot"), + c("SCCS", "estimationSccsResultsViewer", "estimationSccsResults", "resultsPanel", "SCCS Table"), + c("SCCS", "estimationSccsPlotsViewer", "estimationSccsPlots", "resultsPanel", "SCCS Plot") + ) + selectValD <- T + selectValR <- T + for( type in types){ + if(type[1] %in% estimationTypes){ + shiny::insertTab( + inputId = type[4], + tab = shiny::tabPanel( + title = type[5], + do.call(what = type[2], args = list(id = session$ns(type[3]))) + ), + select = ifelse(type[4] == "diagnosticsPanel", selectValD, selectValR) + ) + if(type[4] == "diagnosticsPanel"){ + selectValD = F + } else{ + selectValR = F + } + } + + } + + + + # use the function in report-main to get parent Ts with all children Ts, the outcomes for the Ts and the Cs + options <- getTandOs( + connectionHandler, + resultDatabaseSettings, + includeCharacterization = F, + includeCohortIncidence = F, + includeCohortMethod = "Cohort Method" %in% estimationTypes, + includePrediction = F, + includeSccs = "SCCS" %in% estimationTypes # slow so turning off + ) + + # Targets + targets <- lapply(options$groupedTs, function(x) x$cohortId) + targets <- unlist(targets) + + # initial outcomes for first T + outcomeDf <- options$tos[[1]] + outcomes <- shiny::reactiveVal(outcomeDf) + initialOutcomes <- outcomeDf$outcomeId + names(initialOutcomes ) <- outcomeDf$outcomeName + + shiny::observeEvent(input$targetId,{ + + outcomes(unique( + do.call( + 'rbind', + lapply( + options$groupedTs[[which(targets == input$targetId)]]$subsets$targetId, + function(id){ + if(id %in% names(options$tos)){ + return(options$tos[[which(id == names(options$tos))]]) + } else{ + return(NULL) + } + } + ) + ) + )) + + + if(length(outcomes()$outcomeId)>0){ + outcomesVector <- outcomes()$outcomeId + names(outcomesVector) <- outcomes()$outcomeName + + shinyWidgets::updatePickerInput( + session = session, + inputId = 'outcomeId', + label = 'Outcome: ', + choices = outcomesVector, + selected = outcomesVector[1] + ) + } + }) + # end observed targetId + + output$targetSelection <- shiny::renderUI({ + shiny::fluidRow( + shiny::div( + shinyWidgets::pickerInput( + inputId = session$ns('targetId'), + label = 'Target: ', + choices = targets, + selected = targets[1], + multiple = FALSE, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + dropupAuto = F, + size = 10, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 500 + ) + ), + shinyWidgets::pickerInput( + inputId = session$ns('outcomeId'), + label = 'Outcome: ', + choices = initialOutcomes, + selected = initialOutcomes[1], + multiple = FALSE, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + liveSearch = TRUE, + dropupAuto = F, + size = 10, + liveSearchStyle = "contains", + liveSearchPlaceholder = "Type here to search", + virtualScroll = 500 + ) + ), + style = 'margin-left: 2%; width: 78%; display: inline-block; vertical-align: middle;' + ), + shiny::div( + shiny::actionButton( + inputId = session$ns('targetSelect'), + label = 'Select', + icon = shiny::icon('redo') + ), + style = 'display: inline-block; vertical-align: bottom; margin-bottom: 20px' + ) + ) + }) + + + targetSelected <- shiny::reactiveVal(NULL) + comparatorIds <- shiny::reactiveVal(NULL) + targetIds <- shiny::reactiveVal(NULL) + outcomeId <- shiny::reactiveVal(NULL) + + shiny::observeEvent(input$targetSelect, { + + targetSelected( + data.frame( + Target = names(targets)[targets == input$targetId], + Outcome = outcomes()$outcomeName[outcomes()$outcomeId == input$outcomeId] + ) + ) + inputSelectionDfServer( + id = 'targetSelected', + dataFrameRow = targetSelected, + ncol = 1 + ) + + #======================================== + # code to update diagnostics database + #======================================== + # get all the ids that are children of the id selected + targetIdsTemp <- options$groupedTs[[which(targets == input$targetId)]]$subsets$targetId + + comparators <- do.call( + 'rbind', + lapply( + options$groupedTs[[which(targets == input$targetId)]]$subsets$targetId, + function(id){ + if(id %in% names(options$cs)){ + return(options$cs[[which(id == names(options$cs))]]) + } else{ + return(NULL) + } + } + ) + ) + targetIds(targetIdsTemp) + comparatorIds(comparators$comparatorId) + outcomeId(input$outcomeId) + }) + + #======================================= + # SERVERS + #======================================= + if('Cohort Method' %in% estimationTypes){ + estimationCmDiagnosticServer( + id = 'estimationCmDiagnostic', + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + comparatorIds = comparatorIds, + outcomeId = outcomeId + ) + + cmData <- estimationCmResultsServer( + id = 'estimationCmResults', + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + comparatorIds = comparatorIds, + outcomeId = outcomeId + ) + + estimationCmPlotsServer( + id = 'estimationCmPlots', + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + cmData = cmData + ) + } + + if('SCCS' %in% estimationTypes){ + estimationSccsDiagnosticServer( + id = 'estimationSccsDiagnostic', + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + outcomeId = outcomeId + ) + + sccsData <- estimationSccsResultsServer( + id = 'estimationSccsResults', + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + outcomeId = outcomeId + ) + + estimationSccsPlotsServer( + id = 'estimationSccsPlots', + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + sccsData = sccsData + ) + } + + } + ) +} + + + + +getEstimationTypes <- function( + connectionHandler, + resultDatabaseSettings +){ + + results <- c() + + conn <- DatabaseConnector::connect( + connectionDetails = connectionHandler$connectionDetails + ) + on.exit(DatabaseConnector::disconnect(conn)) + tbls <- DatabaseConnector::getTableNames( + connection = conn, + databaseSchema = resultDatabaseSettings$schema + ) + + # Cohort Method + if(paste0( + resultDatabaseSettings$cmTablePrefix, + c('result') + ) %in% tbls){ + results <- c(results, "Cohort Method") + } + + # SCCS + if(paste0( + resultDatabaseSettings$sccsTablePrefix, + 'result' + ) %in% tbls){ + results <- c(results, "SCCS") + } + + # Evidence Synthesis + if( + paste0( + resultDatabaseSettings$esTablePrefix, + 'cm_result' + ) %in% tbls || + paste0( + resultDatabaseSettings$esTablePrefix, + 'sccs_result' + ) %in% tbls + + ){ + results <- c(results, "Evidence Synthesis") + } + + return(results) +} diff --git a/R/estimation-sccs-diagnostics.R b/R/estimation-sccs-diagnostics.R new file mode 100644 index 00000000..81a5fe06 --- /dev/null +++ b/R/estimation-sccs-diagnostics.R @@ -0,0 +1,261 @@ +estimationSccsDiagnosticViewer <- function(id=1) { + ns <- shiny::NS(id) + resultTableViewer(ns("sccsDiagnosticsTable")) +} + + +estimationSccsDiagnosticServer <- function( + id, + connectionHandler, + resultDatabaseSettings = list(port = 1), + targetIds, + outcomeId +) { + shiny::moduleServer( + id, + function(input, output, session) { + + + + sccsDiagnostics <- shiny::reactive({ + estimationGetSccsDiagnostics( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + outcomeId = outcomeId + ) + }) + + resultTableServer( + id = "sccsDiagnosticsTable", + df = sccsDiagnostics, + colDefsInput = estimationGetSccsDiagnosticColDefs(), + selectedCols = c( + 'databaseName', + 'analysis', + 'target', + 'indication', + 'summaryValue' + ) + ) + + + } + ) +} + + +estimationGetSccsDiagnostics <- function( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + targetIds = targetIds, + outcomeId = outcomeId +){ + targetIds <- targetIds() + outcomeId <- outcomeId() + + sql <- " + SELECT + d.cdm_source_abbreviation as database_name, + a.description as analysis, + c2.cohort_name as target, + c3.cohort_name as indication, + c.cohort_name as outcome, + cov.covariate_name, + ds.* + FROM @schema.@sccs_table_prefixdiagnostics_summary ds + inner join + @schema.@sccs_table_prefixexposures_outcome_set eos + on ds.exposures_outcome_set_id = eos.exposures_outcome_set_id + inner join + @schema.@cg_table_prefixcohort_definition as c + on c.cohort_definition_id = eos.outcome_id + + INNER JOIN + @schema.@database_table_prefix@database_table d + on d.database_id = ds.database_id + + INNER JOIN + @schema.@sccs_table_prefixanalysis a + on a.analysis_id = ds.analysis_id + + INNER JOIN + @schema.@sccs_table_prefixcovariate cov + on cov.covariate_id = ds.covariate_id and + cov.exposures_outcome_set_id = ds.exposures_outcome_set_id and + cov.analysis_id = ds.analysis_id and + cov.database_id = ds.database_id + + inner join + @schema.@cg_table_prefixcohort_definition as c2 + on cov.era_id = c2.cohort_definition_id + + left join + @schema.@cg_table_prefixcohort_definition as c3 + on eos.nesting_cohort_id = c3.cohort_definition_id + + where + + cov.era_id in (@target_ids) + and eos.outcome_id in (@outcome_ids) + ; + " + result <- connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_table_prefix = resultDatabaseSettings$databaseTablePrefix, + database_table = resultDatabaseSettings$databaseTable, + + target_ids = paste0(targetIds, collapse = ','), + outcome_ids = paste0(outcomeId, collapse = ','), + snakeCaseToCamelCase = TRUE + ) + + result <- result %>% + dplyr::select(-c("analysisId","exposuresOutcomeSetId","databaseId","covariateId")) + + result$summaryValue <- apply( + X = result[, grep('Diagnostic', colnames(result))], + MARGIN = 1, + FUN = function(x){ + + if(sum(x %in% c('FAIL'))>0){ + return('Fail') + } else if(sum(x %in% c('WARNING')) >0){ + return(sum(x %in% c('WARNING'), na.rm = T)) + } else{ + return('Pass') + } + } + ) + + # add summaryValue after outcome + result <- result %>% + dplyr::relocate("summaryValue", .after = "outcome") + + return( + result + ) + +} + + + + + +estimationGetSccsDiagnosticColDefs <- function(){ + result <- list( + databaseName = reactable::colDef( + header = withTooltip( + "Database", + "The database name" + ) + ), + target = reactable::colDef( + header = withTooltip( + "Target", + "The target cohort of interest " + ) + ), + outcome = reactable::colDef( + header = withTooltip( + "Outcome", + "The outcome of interest " + ) + ), + indication = reactable::colDef( + header = withTooltip( + "Indication", + "The indication of interest " + ) + ), + summaryValue = reactable::colDef( + header = withTooltip( + "Diagnostic", + "The overall result of the diagostics" + ), + style = function(value) { + color <- 'orange' + if(is.na(value)){ + color <- 'black' + }else if(value == 'Pass'){ + color <- '#AFE1AF' + }else if(value == 'Fail'){ + color <- '#E97451' + } + list(background = color) + } + ), + analysis = reactable::colDef( + header = withTooltip( + "Analysis", + "The analysis name " + ) + ), + covariateName = reactable::colDef( + header = withTooltip( + "Time Period", + "The time period of interest" + ) + ), + mdrr = reactable::colDef( + header = withTooltip( + "mdrr", + "The minimum detectible relative risk" + ) + ), + ease = reactable::colDef( + header = withTooltip( + "ease", + "The ..." + ) + ), + timeTrendP = reactable::colDef( + header = withTooltip( + "timeTrendP", + "The ..." + ) + ), + preExposureP = reactable::colDef( + header = withTooltip( + "preExposureP", + "The ..." + ) + ), + mdrrDiagnostic = reactable::colDef( + header = withTooltip( + "mdrrDiagnostic", + "The ..." + ) + ), + easeDiagnostic = reactable::colDef( + header = withTooltip( + "easeDiagnostic", + "The ..." + ) + ), + timeTrendDiagnostic = reactable::colDef( + header = withTooltip( + "timeTrendDiagnostic", + "The ..." + ) + ), + preExposureDiagnostic = reactable::colDef( + header = withTooltip( + "preExposureDiagnostic", + "The ..." + ) + ), + + unblind = reactable::colDef( + header = withTooltip( + "unblind", + "If the value is 1 then the diagnostics passed and results can be unblinded" + ) + ) + ) + + return(result) +} \ No newline at end of file diff --git a/R/estimation-sccs-plots.R b/R/estimation-sccs-plots.R new file mode 100644 index 00000000..aad17c61 --- /dev/null +++ b/R/estimation-sccs-plots.R @@ -0,0 +1,163 @@ +estimationSccsPlotsViewer <- function(id=1) { + ns <- shiny::NS(id) + shinyWidgets::addSpinner( + output = shiny::plotOutput(ns('esSccsPlot')), + spin = 'rotating-plane' + ) +} + + +estimationSccsPlotsServer <- function( + id, + connectionHandler, + resultDatabaseSettings = list(port = 1), + sccsData +) { + shiny::moduleServer( + id, + function(input, output, session) { + + height <- shiny::reactive({ + if(is.null(sccsData()$indication)){ + return(100) + } + length(unique(sccsData()$indication))*200 + 200 + }) + + output$esSccsPlot <- shiny::renderPlot( + estimationCreateSccsPlot( + data = sccsData + ), + height = height + ) + + } + ) +} + +estimationCreateSccsPlot <- function(data) { + data <- data() + if(nrow(data) == 0){ + shiny::showNotification('No results to plot') + return(NULL) + } + data <- data[!is.na(data$calibratedRr),] + if(nrow(data) == 0){ + shiny::showNotification('No results to plot') + return(NULL) + } + data$database <- data$databaseName + data$type <- data$covariateName + data$indication[is.null(data$indication)] <- 'no indication' + data$indication[is.na(data$indication)] <- 'no indication' + + if(is.null(data)){ + shiny::showNotification('No results to plot') + return(NULL) + } + if(nrow(data) == 0){ + shiny::showNotification('No results to plot') + return(NULL) + } + + # change the description to add at bottom + renameDf <- data.frame( + shortName = paste0( + 1:length(unique(data$description)), + ') ', + substring(sort(unique(data$description)), 1, 15), + '...' + ), + description = sort(unique(data$description)) + ) + + data <- merge( + x = data, + y = renameDf, + by = 'description' + ) + + # make sure bayesian is at top + db <- unique(data$database) + bInd <- grep('bayesian', tolower(db)) + withoutb <- db[-bInd] + b <- db[bInd] + data$database <- factor( + x = data$database, + levels = c(b, sort(withoutb)) + ) + metadata <- data[data$database == b,] + + breaks <- c(0.1, 0.25, 0.5, 1, 2, 4, 6, 8) + + # TODO loop over target-indications pairs + + ### Add table above the graph + renameDf$description <- sapply( + strwrap(renameDf$description, width = 50, simplify = FALSE), + paste, + collapse = "\n" + ) + + tt <- gridExtra::ttheme_default( + base_size = 8, + colhead=list(fg_params = list(parse=TRUE)) + ) + tbl <- gridExtra::tableGrob( + renameDf, + rows=NULL, + theme=tt + ) + plotList <- list(tbl) # adding table first + + for(indication in unique(data$indication)){ # TODO do indication + target combo? + plotList[[length(plotList)+1]] <- ggplot2::ggplot( + data = data %>% dplyr::filter(.data$indication == !!indication), #restrict to indication + ggplot2::aes(x = .data$calibratedRr, y = .data$type) + ) + + ggplot2::geom_vline(xintercept = 1, size = 0.5) + + ggplot2::geom_point(color = "#000088", alpha = 0.8) + + ggplot2::geom_errorbarh( + ggplot2::aes( + xmin = .data$calibratedCi95Lb, + xmax = .data$calibratedCi95Ub + ), + height = 0.5, + color = "#000088", + alpha = 0.8 + ) + + ggplot2::scale_x_log10( + "Effect size (Incidence Rate Ratio)", + breaks = breaks, + labels = breaks + ) + + + # shade the bayesian + ggplot2::geom_rect( + data = metadata %>% dplyr::filter(.data$indication == !!indication), + ggplot2::aes(fill = .data$database), + xmin = -Inf, + xmax = Inf, + ymin = -Inf, + ymax = Inf, + alpha = 0.2 + ) + + + ggplot2::coord_cartesian(xlim = c(0.1, 10)) + + ggplot2::facet_grid(.data$database ~ .data$shortName) + + ggplot2::ggtitle(indication) + + ggplot2::theme( + axis.title.y = ggplot2::element_blank(), + panel.grid.minor = ggplot2::element_blank(), + strip.text.y.right = ggplot2::element_text(angle = 0), + legend.position = "none" + ) +} + + plot <- do.call( + gridExtra::grid.arrange, + list(grobs = plotList, ncol =1) + ) + + return(plot) +} diff --git a/R/sccs-results-full.R b/R/estimation-sccs-results-full.R similarity index 51% rename from R/sccs-results-full.R rename to R/estimation-sccs-results-full.R index 279349d8..f7977f29 100644 --- a/R/sccs-results-full.R +++ b/R/estimation-sccs-results-full.R @@ -1,4 +1,4 @@ -sccsFullResultViewer <- function(id) { +estimationSccsFullResultViewer <- function(id) { ns <- shiny::NS(id) shiny::div( @@ -16,11 +16,15 @@ sccsFullResultViewer <- function(id) { shiny::tabPanel( "Power", shiny::div(shiny::strong("Table 1."), "For each variable of interest: the number of cases (people with at least one outcome), the number of years those people were observed, the number of outcomes, the number of subjects with at least one exposure, the number of patient-years exposed, the number of outcomes while exposed, and the minimum detectable relative risk (MDRR)."), - resultTableViewer(ns('powerTable')) + shinyWidgets::addSpinner( + resultTableViewer(ns('powerTable')) + ) ), shiny::tabPanel( "Attrition", - shiny::plotOutput(ns("attritionPlot"), width = 600, height = 500), + shinyWidgets::addSpinner( + shiny::plotOutput(ns("attritionPlot"), width = 600, height = 500) + ), shiny::div( shiny::strong("Figure 1."), "Attrition, showing the number of cases (number of subjects with at least one outcome), and number of outcomes (number of ocurrences of the outcome) after each step in the study.") @@ -39,17 +43,23 @@ sccsFullResultViewer <- function(id) { ), shiny::tabPanel( "Age spline", - shiny::plotOutput(ns("ageSplinePlot")), + shinyWidgets::addSpinner( + shiny::plotOutput(ns("ageSplinePlot")) + ), shiny::div(shiny::strong("Figure 2a."), "Spline fitted for age.") ), shiny::tabPanel( "Season spline", - shiny::plotOutput(ns("seasonSplinePlot")), + shinyWidgets::addSpinner( + shiny::plotOutput(ns("seasonSplinePlot")) + ), shiny::div(shiny::strong("Figure 2b."), "Spline fitted for season") ), shiny::tabPanel( "Calendar time spline", - shiny::plotOutput(ns("calendarTimeSplinePlot")), + shinyWidgets::addSpinner( + shiny::plotOutput(ns("calendarTimeSplinePlot")) + ), shiny::div(shiny::strong("Figure 2c."), "Spline fitted for calendar time") ) ) @@ -57,12 +67,16 @@ sccsFullResultViewer <- function(id) { shiny::tabPanel( "Spanning", shiny::radioButtons(ns("spanningType"), label = "Type:", choices = c("Age", "Calendar time")), - shiny::plotOutput(ns("spanningPlot")), + shinyWidgets::addSpinner( + shiny::plotOutput(ns("spanningPlot")) + ), shiny::div(shiny::strong("Figure 3."), "Number of subjects observed for 3 consecutive months, centered on the indicated month.") ), shiny::tabPanel( "Time trend", - shiny::plotOutput(ns("timeTrendPlot"), height = 600), + shinyWidgets::addSpinner( + shiny::plotOutput(ns("timeTrendPlot"), height = 600) + ), shiny::div( shiny::strong("Figure 4."), "The ratio of observed to expected outcomes per month. The expected count is computing either assuming a constant rate (bottom plot) or adjusting for calendar time, seasonality, and / or age, as specified in the model (top plot)." @@ -70,7 +84,9 @@ sccsFullResultViewer <- function(id) { ), shiny::tabPanel( "Time to event", - shiny::plotOutput(ns("timeToEventPlot")), + shinyWidgets::addSpinner( + shiny::plotOutput(ns("timeToEventPlot")) + ), shiny::div( shiny::strong("Figure 5."), "The number of events and subjects observed per week relative to the start of the first exposure (indicated by the thick vertical line)." @@ -78,13 +94,17 @@ sccsFullResultViewer <- function(id) { ), shiny::tabPanel( "Event dep. observation", - shiny::plotOutput(ns("eventDepObservationPlot")), + shinyWidgets::addSpinner( + shiny::plotOutput(ns("eventDepObservationPlot")) + ), shiny::div(shiny::strong("Figure 6."), "Histograms for the number of months between the first occurrence of the outcome and the end of observation, stratified by whether the end of observation was censored (inferred as not being equal to the end of database time), or uncensored (inferred as having the subject still be observed at the end of database time)." ) ), shiny::tabPanel( "Systematic error", - shiny::plotOutput(ns("controlEstimatesPlot")), + shinyWidgets::addSpinner( + shiny::plotOutput(ns("controlEstimatesPlot")) + ), shiny::div(shiny::strong("Figure 7."), "Systematic error. Effect size estimates for the negative controls (true incidence rate ratio = 1) and positive controls (true incidence rate ratio > 1), before and after calibration. Estimates below the diagonal dashed lines are statistically significant (alpha = 0.05) different from the true effect size. A well-calibrated @@ -97,7 +117,7 @@ sccsFullResultViewer <- function(id) { } -sccsFullResultServer <- function( +estimationSccsFullResultServer <- function( id, connectionHandler, resultDatabaseSettings, @@ -114,18 +134,21 @@ sccsFullResultServer <- function( shiny::updateTabsetPanel(session, "fullTabsetPanel", selected = "Power") }) + # show what was selected modifiedRow <- shiny::reactive({ selectedRow() %>% dplyr::select( "covariateName", + 'indication', "outcome", "description", "databaseName" ) %>% dplyr::rename( - 'Outcome' = .data$outcome, - 'Analysis' = .data$description, - 'Database' = .data$databaseName + Indication = "indication", + Outcome = "outcome", + Analysis = "description", + Database = "databaseName" ) }) @@ -218,13 +241,13 @@ sccsFullResultServer <- function( if (is.null(row)) { return(NULL) } else { - attrition <- getSccsAttrition( + attrition <- estimationGetSccsAttrition( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - outcomeId = row$outcomeId, databaseId = row$databaseId, analysisId = row$analysisId, - covariateId = row$covariateId + covariateId = row$covariateId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId ) drawAttritionDiagram(attrition) } @@ -235,11 +258,11 @@ sccsFullResultServer <- function( if (is.null(row)) { return(NULL) } else { - resTargetTable <- getSccsModel( + resTargetTable <- estimationGetSccsModel( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, exposureId = row$eraId, - outcomeId = row$outcomeId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId, databaseId = row$databaseId, analysisId = row$analysisId ) @@ -261,11 +284,11 @@ sccsFullResultServer <- function( if (is.null(row)) { return(NULL) } else { - timeTrend <- getSccsTimeTrend( + timeTrend <- estimationGetSccsTimeTrend( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, exposureId = row$eraId, - outcomeId = row$outcomeId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId, databaseId = row$databaseId, analysisId = row$analysisId ) @@ -283,10 +306,10 @@ sccsFullResultServer <- function( if (is.null(row)) { return(NULL) } else { - timeToEvent <- getSccsTimeToEvent( + timeToEvent <- estimationGetSccsTimeToEvent( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - outcomeId = row$outcomeId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId, exposureId = row$eraId, covariateId = row$covariateId, databaseId = row$databaseId, @@ -301,10 +324,10 @@ sccsFullResultServer <- function( if (is.null(row)) { return(NULL) } else { - eventDepObservation <- getSccsEventDepObservation( + eventDepObservation <- estimationGetSccsEventDepObservation( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - outcomeId = row$outcomeId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId, databaseId = row$databaseId, analysisId = row$analysisId ) @@ -318,19 +341,19 @@ sccsFullResultServer <- function( return(NULL) } else { if (input$spanningType == "Age") { - ageSpanning <- getSccsAgeSpanning( + ageSpanning <- estimationGetSccsAgeSpanning( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - outcomeId = row$outcomeId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId, databaseId = row$databaseId, analysisId = row$analysisId ) plotSpanning(ageSpanning, type = "age") } else { - calendarTimeSpanning <- getSccsCalendarTimeSpanning( + calendarTimeSpanning <- estimationGetSccsCalendarTimeSpanning( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - outcomeId = row$outcomeId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId, databaseId = row$databaseId, analysisId = row$analysisId ) @@ -345,10 +368,10 @@ sccsFullResultServer <- function( if (is.null(row)) { return(NULL) } else { - ageSpline <- getSccsSpline( + ageSpline <- estimationGetSccsSpline( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - outcomeId = row$outcomeId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId, databaseId = row$databaseId, analysisId = row$analysisId, splineType = "age" @@ -365,10 +388,10 @@ sccsFullResultServer <- function( if (is.null(row)) { return(NULL) } else { - seasonSpline <- getSccsSpline( + seasonSpline <- estimationGetSccsSpline( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - outcomeId = row$outcomeId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId, databaseId = row$databaseId, analysisId = row$analysisId, splineType = "season" @@ -385,10 +408,10 @@ sccsFullResultServer <- function( if (is.null(row)) { return(NULL) } else { - calendarTimeSpline <- getSccsSpline( + calendarTimeSpline <- estimationGetSccsSpline( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - outcomeId = row$outcomeId, + exposuresOutcomeSetId = row$exposuresOutcomeSetId, databaseId = row$databaseId, analysisId = row$analysisId, splineType = "calendar time" @@ -405,14 +428,18 @@ sccsFullResultServer <- function( if (is.null(row)) { return(NULL) } else { - controlEstimates <- getSccsControlEstimates( + controlEstimates <- estimationGetSccsControlEstimates( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, covariateId = row$covariateId, databaseId = row$databaseId, - analysisId = row$analysisId + analysisId = row$analysisId, + eraId = row$eraId ) - plotControlEstimates(controlEstimates) + plotControlEstimates( + controlEstimates = controlEstimates$plotResult, + ease = controlEstimates$ease + ) } }) @@ -421,7 +448,354 @@ sccsFullResultServer <- function( } +estimationGetSccsAttrition <- function( + connectionHandler, + resultDatabaseSettings, + databaseId, + analysisId, + covariateId, + exposuresOutcomeSetId +) { + sql <- " + SELECT * + FROM @schema.@sccs_table_prefixattrition + + WHERE database_id = '@database_id' + AND analysis_id = @analysis_id + AND exposures_outcome_set_id = @exposures_outcome_set_id + AND covariate_id = @covariate_id + " + connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + analysis_id = analysisId, + covariate_id = covariateId, + exposures_outcome_set_id = exposuresOutcomeSetId, + snakeCaseToCamelCase = TRUE + ) +} + + +estimationGetSccsModel <- function( + connectionHandler, + resultDatabaseSettings, + exposuresOutcomeSetId, + databaseId, + analysisId, + exposureId +) { + sql <- " + SELECT + CASE + WHEN era.era_name IS NULL THEN sc.covariate_name + ELSE CONCAT(sc.covariate_name, ' : ', era.era_name) + END as covariate_name, + scr.covariate_id, scr.rr, scr.ci_95_lb, scr.ci_95_ub + FROM @schema.@sccs_table_prefixcovariate_result scr + INNER JOIN @schema.@sccs_table_prefixcovariate sc ON ( + sc.exposures_outcome_set_id = scr.exposures_outcome_set_id AND + sc.database_id = scr.database_id AND + sc.analysis_id = scr.analysis_id AND + sc.covariate_id = scr.covariate_id + ) + LEFT JOIN @schema.@cg_table_prefixcohort_definition cd + ON cd.cohort_definition_id = sc.era_id + LEFT JOIN @schema.@sccs_table_prefixera era ON ( + era.exposures_outcome_set_id = scr.exposures_outcome_set_id AND + era.database_id = scr.database_id AND + era.analysis_id = scr.analysis_id AND + era.era_id = sc.era_id + ) + + WHERE scr.database_id = '@database_id' + AND scr.analysis_id = @analysis_id + --AND sc.era_id = @exposure_id + --AND scr.rr IS NOT NULL + AND scr.exposures_outcome_set_id = @exposures_outcome_set_id + " + + connectionHandler$queryDb(sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix, + database_id = databaseId, + analysis_id = analysisId, + exposure_id = exposureId, + exposures_outcome_set_id = exposuresOutcomeSetId, + snakeCaseToCamelCase = TRUE) +} + + +estimationGetSccsTimeTrend <- function( + connectionHandler, + resultDatabaseSettings, + exposureId, + exposuresOutcomeSetId, + databaseId, + analysisId +) { + sql <- " + SELECT * + FROM @schema.@sccs_table_prefixtime_trend + WHERE database_id = '@database_id' + AND analysis_id = @analysis_id + AND exposures_outcome_set_id = @exposures_outcome_set_id + " + connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + analysis_id = analysisId, + exposures_outcome_set_id = exposuresOutcomeSetId, + snakeCaseToCamelCase = TRUE + ) +} + +estimationGetSccsTimeToEvent <- function( + connectionHandler, + resultDatabaseSettings, + exposureId, + exposuresOutcomeSetId, + covariateId, + databaseId, + analysisId +) { + + sql <- " + SELECT pre_exposure_p + FROM @schema.@sccs_table_prefixdiagnostics_summary + + WHERE database_id = '@database_id' + AND covariate_id = @covariate_id + AND analysis_id = @analysis_id + AND exposures_outcome_set_id = @exposures_outcome_set_id + " + + p <- connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + analysis_id = analysisId, + exposures_outcome_set_id = exposuresOutcomeSetId, + covariate_id = covariateId, + snakeCaseToCamelCase = TRUE + ) + + # if NULL set to NA so code below works + if(is.null(p$preExposureP)){ + p$preExposureP <- NA + } + + sql <- " + SELECT * , @p as p + FROM @schema.@sccs_table_prefixtime_to_event + + WHERE database_id = '@database_id' + AND era_id = @exposure_id + AND analysis_id = @analysis_id + AND exposures_outcome_set_id = @exposures_outcome_set_id; + " + + timeToEvent <- connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + analysis_id = analysisId, + exposures_outcome_set_id = exposuresOutcomeSetId, + exposure_id = exposureId, + p = ifelse(is.na(p$preExposureP), -1, p$preExposureP), + snakeCaseToCamelCase = TRUE + ) + + + return(timeToEvent) +} + + +estimationGetSccsEventDepObservation <- function( + connectionHandler, + resultDatabaseSettings, + exposuresOutcomeSetId, + databaseId, + analysisId +) { + sql <- " + SELECT * + FROM @schema.@sccs_table_prefixevent_dep_observation + + WHERE database_id = '@database_id' + AND analysis_id = @analysis_id + AND exposures_outcome_set_id = @exposures_outcome_set_id; + " + connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + analysis_id = analysisId, + exposures_outcome_set_id = exposuresOutcomeSetId, + snakeCaseToCamelCase = TRUE + ) +} +estimationGetSccsAgeSpanning <- function( + connectionHandler, + resultDatabaseSettings, + exposuresOutcomeSetId, + databaseId, + analysisId +) { + sql <- " + SELECT * + FROM @schema.@sccs_table_prefixage_spanning + + WHERE database_id = '@database_id' + AND analysis_id = @analysis_id + AND exposures_outcome_set_id = @exposures_outcome_set_id + " + connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + analysis_id = analysisId, + exposures_outcome_set_id = exposuresOutcomeSetId, + snakeCaseToCamelCase = TRUE + ) +} +estimationGetSccsCalendarTimeSpanning <- function( + connectionHandler, + resultDatabaseSettings, + exposuresOutcomeSetId, + databaseId, + analysisId +) { + sql <- " + SELECT * + FROM @schema.@sccs_table_prefixcalendar_time_spanning + + WHERE database_id = '@database_id' + AND analysis_id = @analysis_id + AND exposures_outcome_set_id = @exposures_outcome_set_id + " + connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + analysis_id = analysisId, + exposures_outcome_set_id = exposuresOutcomeSetId, + snakeCaseToCamelCase = TRUE + ) +} +estimationGetSccsSpline <- function( + connectionHandler, + resultDatabaseSettings, + exposuresOutcomeSetId, + databaseId, + analysisId, + splineType = "age" +) { + + sql <- " + SELECT * + FROM @schema.@sccs_table_prefixspline + + WHERE database_id = '@database_id' + AND analysis_id = @analysis_id + AND exposures_outcome_set_id = @exposures_outcome_set_id + AND spline_type = '@spline_type'; + " + connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + spline_type = splineType, + analysis_id = analysisId, + exposures_outcome_set_id = exposuresOutcomeSetId, + snakeCaseToCamelCase = TRUE + ) +} + + + +estimationGetSccsControlEstimates <- function( + connectionHandler, + resultDatabaseSettings, + databaseId, + analysisId, + covariateId, + eraId +) { + + sql <- " + SELECT r.ci_95_lb, r.ci_95_ub, r.log_rr, r.se_log_rr, + r.calibrated_ci_95_lb, r.calibrated_ci_95_ub, r.calibrated_log_rr, + r.calibrated_se_log_rr, r.exposures_outcome_set_id, + e.true_effect_size, c.exposures_outcome_set_id + + FROM + @schema.@sccs_table_prefixresult r + INNER JOIN + @schema.@sccs_table_prefixexposure e + on r.exposures_outcome_set_id = e.exposures_outcome_set_id + + INNER JOIN + @schema.@sccs_table_prefixcovariate c + on e.era_id = c.era_id + and e.exposures_outcome_set_id = c.exposures_outcome_set_id + and c.database_id = r.database_id + and c.analysis_id = r.analysis_id + and c.covariate_id = r.covariate_id + + WHERE r.database_id = '@database_id' + AND r.analysis_id = @analysis_id + AND r.covariate_id = @covariate_id + AND e.true_effect_size is not NULL + -- AND e.era_id = @era_id + ; + " + + res <- connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + covariate_id = covariateId, + analysis_id = analysisId, + era_id = eraId, + snakeCaseToCamelCase = TRUE + ) + + # get ease for the plot + sql <- "SELECT top 1 ds.ease + FROM @schema.@sccs_table_prefixdiagnostics_summary ds + WHERE ds.database_id = '@database_id' + AND ds.analysis_id = @analysis_id + AND ds.covariate_id = @covariate_id;" + + ease <- connectionHandler$queryDb( + sql, + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + database_id = databaseId, + covariate_id = covariateId, + analysis_id = analysisId, + snakeCaseToCamelCase = TRUE + ) + + return(list( + plotResult = res, + ease = ease$ease + ) + ) +} diff --git a/R/sccs-results.R b/R/estimation-sccs-results.R similarity index 61% rename from R/sccs-results.R rename to R/estimation-sccs-results.R index 0f5b7e54..75fad592 100644 --- a/R/sccs-results.R +++ b/R/estimation-sccs-results.R @@ -1,4 +1,4 @@ -sccsResultsViewer <- function(id = "sccs-results") { +estimationSccsResultsViewer <- function(id = "sccs-results") { ns <- shiny::NS(id) shiny::tabsetPanel( @@ -8,18 +8,17 @@ sccsResultsViewer <- function(id = "sccs-results") { shiny::tabPanel( title = "Table", resultTableViewer(ns("resultSummaryTable")) - #) ), shiny::tabPanel( title = "Results", shiny::actionButton( - inputId = ns('goBackCmResults'), + inputId = ns('goBackSccsResults'), label = "Back To Result Summary", shiny::icon("arrow-left"), style="color: #fff; background-color: #337ab7; border-color: #2e6da4" ), - sccsFullResultViewer(ns("sccsFullResults")) + estimationSccsFullResultViewer(ns("sccsFullResults")) ) ) @@ -29,62 +28,67 @@ sccsResultsViewer <- function(id = "sccs-results") { } -sccsResultsServer <- function( +estimationSccsResultsServer <- function( id, connectionHandler, resultDatabaseSettings = list(port = 1), - inputSelected + targetIds, + outcomeId ) { ns <- shiny::NS(id) shiny::moduleServer(id, function(input, output, session) { shiny::observeEvent( - eventExpr = input$goBackCmResults, + eventExpr = input$goBackSccsResults, { shiny::updateTabsetPanel(session, "resultPanel", selected = "Table") } ) - data <- shiny::reactive({ - - exposure <- inputSelected()$exposure - if (is.character(exposure)) { - exposureGroup <- strsplit(exposure, " ")[[1]] - targetId <- exposureGroup[[1]] - indidcationId <- exposureGroup[[2]] - } else { - targetId <- -1 - indidcationId <- -1 - } - - getSccsResults( + sccsData <- shiny::reactive({ + estimationGetSccsResults( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, - exposureIds = targetId, - outcomeIds = inputSelected()$outcome, - #databaseIds = inputSelected()$database, - analysisIds = inputSelected()$analysis, - indicationIds = indidcationId + exposureIds = targetIds, + outcomeIds = outcomeId ) }) + # add evidence synth if existsesData <- shiny::reactive({ + esData <- shiny::reactive({ + tryCatch( + { + estimationGetSccsEsResults( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + exposureIds = targetIds, + outcomeIds = outcomeId + ) + }, error = function(e){print('SCCS ES error');return(NULL)} + ) + }) + + data <- shiny::reactive({ + rbind(sccsData(), esData()) + }) + resultTableOutputs <- resultTableServer( id = "resultSummaryTable", df = data, - colDefsInput = getSccsResultSummaryTableColDef(), + colDefsInput = estimationGetSccsResultSummaryTableColDef(), addActions = c('results') ) selectedRow <- shiny::reactiveVal(value = NULL) shiny::observeEvent(resultTableOutputs$actionCount(), { - if(resultTableOutputs$actionType() == 'results'){ + if(resultTableOutputs$actionType() == 'results'){ # TODO only work if non meta selectedRow(data()[resultTableOutputs$actionIndex()$index,]) shiny::updateTabsetPanel(session, "resultPanel", selected = "Results") } }) - sccsFullResultServer( + estimationSccsFullResultServer( id = "sccsFullResults", connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, @@ -92,13 +96,14 @@ sccsResultsServer <- function( actionCount = resultTableOutputs$actionCount ) - + # return data for plot server + return(data) } ) } -getSccsResultSummaryTableColDef <- function(){ +estimationGetSccsResultSummaryTableColDef <- function(){ results <- list( @@ -108,6 +113,7 @@ getSccsResultSummaryTableColDef <- function(){ covariateAnalysisId = reactable::colDef(show = F), analysisId = reactable::colDef(show = F), outcomeId = reactable::colDef(show = F), + indicationId = reactable::colDef(show = F), outcomeSubjects = reactable::colDef(show = F), outcomeEvents = reactable::colDef(show = F), outcomeObservationPeriods = reactable::colDef(show = F), @@ -118,6 +124,7 @@ getSccsResultSummaryTableColDef <- function(){ observedDays = reactable::colDef(show = F), mdrr = reactable::colDef(show = F), unblind = reactable::colDef(show = F), + exposuresOutcomeSetId = reactable::colDef(show = F), logRr = reactable::colDef(show = F), seLogRr = reactable::colDef(show = F), @@ -139,6 +146,22 @@ getSccsResultSummaryTableColDef <- function(){ "Data source", "Data source" )), + target = reactable::colDef( + filterable = TRUE, + header = withTooltip( + "Target", + "Target Cohort" + ), + minWidth = 300 + ), + indication = reactable::colDef( + filterable = TRUE, + header = withTooltip( + "Indication", + "Target cohort is nested in this indication" + ), + minWidth = 300 + ), outcome = reactable::colDef( filterable = TRUE, header = withTooltip( @@ -216,18 +239,14 @@ getSccsResultSummaryTableColDef <- function(){ return(results) } -getSccsResults <- function(connectionHandler, +estimationGetSccsResults <- function(connectionHandler, resultDatabaseSettings, exposureIds, - outcomeIds, - #databaseIds, - analysisIds, - indicationIds = NULL) { - - if (any(indicationIds == -1)) { - indicationIds <- NULL - } - + outcomeIds + ) { + exposureIds <- exposureIds() + outcomeIds <- outcomeIds() + sql <- " SELECT @@ -241,7 +260,11 @@ getSccsResults <- function(connectionHandler, a.description, eos.outcome_id, cg1.cohort_name as outcome, - + cg2.cohort_name as target, + cg3.cohort_name as indication, + eos.nesting_cohort_id as indication_id, + eos.exposures_outcome_set_id, + sr.outcome_subjects, sr.outcome_events, sr.outcome_observation_periods, @@ -305,12 +328,19 @@ getSccsResults <- function(connectionHandler, inner join @schema.@cg_table_prefixcohort_definition cg1 on cg1.cohort_definition_id = eos.outcome_id + + inner join + @schema.@cg_table_prefixcohort_definition as cg2 + on cg2.cohort_definition_id = sc.era_id - WHERE sr.analysis_id IN (@analysis_ids) - -- AND sr.database_id IN (@database_ids) - AND eos.outcome_id IN (@outcome_ids) + left join + @schema.@cg_table_prefixcohort_definition as cg3 + on eos.nesting_cohort_id = cg3.cohort_definition_id + + WHERE + eos.outcome_id IN (@outcome_ids) AND sc.era_id IN (@exposure_ids) - {@use_indications} ? {and eos.nesting_cohort_id IN (@indication_ids)} : {and eos.nesting_cohort_id IS NULL} + ; " results <- connectionHandler$queryDb( @@ -320,12 +350,8 @@ getSccsResults <- function(connectionHandler, database_table = resultDatabaseSettings$databaseTable, sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - #database_ids = paste(quoteLiterals(databaseIds), collapse = ','), - analysis_ids = paste(analysisIds, collapse = ','), outcome_ids = paste(outcomeIds, collapse = ','), exposure_ids = paste(exposureIds, collapse = ','), - use_indications = !is.null(indicationIds), - indication_ids = indicationIds, snakeCaseToCamelCase = TRUE ) @@ -333,3 +359,114 @@ getSccsResults <- function(connectionHandler, } +estimationGetSccsEsResults <- function( + connectionHandler, + resultDatabaseSettings, + exposureIds, + outcomeIds +) { + + exposureIds <- exposureIds() + outcomeIds <- outcomeIds() + +sql <- "select distinct + ev.evidence_synthesis_description as database_name, + 0 as database_id, + cov.covariate_id, -- exists? + cov.covariate_name, + cov.era_id, + 0 as covariate_analysis_id, + esr.analysis_id, + a.description, + eos.outcome_id, + c3.cohort_name as outcome, + c1.cohort_name as target, + c4.cohort_name as indication, + eos.nesting_cohort_id as indication_id, + eos.exposures_outcome_set_id, + esr.outcome_subjects, + esr.outcome_events, + esr.outcome_observation_periods, + esr.covariate_subjects, + esr.covariate_days, + esr.covariate_eras, + esr.covariate_outcomes, + esr.observed_days, + esr.rr, + esr.ci_95_lb, + esr.ci_95_ub, + esr.p, + esr.log_rr, + esr.se_log_rr, + esr.calibrated_rr, + esr.calibrated_ci_95_lb, + esr.calibrated_ci_95_ub, + esr.calibrated_p, + esr.calibrated_log_rr, + esr.calibrated_se_log_rr, + NULL as llr, + esd.mdrr, + esd.unblind as unblind + + from + @schema.@es_table_prefixsccs_result as esr + inner join + @schema.@sccs_table_prefixexposures_outcome_set as eos + on + esr.exposures_outcome_set_id = eos.exposures_outcome_set_id + + inner join + @schema.@sccs_table_prefixcovariate as cov + on + esr.covariate_id = cov.covariate_id and + esr.analysis_id = cov.analysis_id and + esr.exposures_outcome_set_id = cov.exposures_outcome_set_id + + inner join + + @schema.@es_table_prefixsccs_diagnostics_summary as esd + on + esr.analysis_id = esd.analysis_id and + esr.exposures_outcome_set_id = esd.exposures_outcome_set_id and + esr.covariate_id = esd.covariate_id and + esr.evidence_synthesis_analysis_id = esd.evidence_synthesis_analysis_id + + inner join + @schema.@cg_table_prefixcohort_definition as c1 + on c1.cohort_definition_id = cov.era_id + + inner join + @schema.@cg_table_prefixcohort_definition as c3 + on c3.cohort_definition_id = eos.outcome_id + + inner join + @schema.@sccs_table_prefixanalysis as a + on a.analysis_id = esr.analysis_id + + inner join + @schema.@es_table_prefixanalysis as ev + on ev.evidence_synthesis_analysis_id = esr.evidence_synthesis_analysis_id + + left join + @schema.@cg_table_prefixcohort_definition as c4 + on eos.nesting_cohort_id = c4.cohort_definition_id + + where + esr.calibrated_rr != 0 and + esd.unblind = 1 and + cov.era_id in (@target_ids) and + eos.outcome_id in (@outcome_id) + ;" + +result <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + es_table_prefix = resultDatabaseSettings$esTablePrefix, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix, + outcome_id = paste0(outcomeIds, collapse = ','), + target_ids = paste0(exposureIds, collapse = ',') +) + +return(result) +} diff --git a/R/evidence-synth-cm.R b/R/evidence-synth-cm.R deleted file mode 100644 index e87e7d97..00000000 --- a/R/evidence-synth-cm.R +++ /dev/null @@ -1,549 +0,0 @@ -evidenceSynthesisCmViewer <- function(id=1) { - ns <- shiny::NS(id) - - shiny::div( - - inputSelectionViewer(ns("input-selection-cm")), - - shiny::conditionalPanel( - condition = 'input.generate != 0', - ns = shiny::NS(ns("input-selection-cm")), - - shiny::tabsetPanel( - type = 'pills', - id = ns('esCohortMethodTabs'), - - # diagnostic view - shiny::tabPanel( - title = 'Diagnostics', - resultTableViewer(ns("diagnosticsCmSummaryTable")) - ), - - shiny::tabPanel( - "Plot", - shiny::plotOutput(ns('esCohortMethodPlot')) - ), - shiny::tabPanel( - "Table", - resultTableViewer(ns("esCohortMethodTable")) - ) - ) - ) - ) -} - - -evidenceSynthesisCmServer <- function( - id, - connectionHandler, - resultDatabaseSettings = list(port = 1) -) { - shiny::moduleServer( - id, - function(input, output, session) { - - targetIds <- getEsCmTargetIds( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - outcomeIds <- getEsOutcomeIds( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - inputSelected <- inputSelectionServer( - id = "input-selection-cm", - inputSettingList = list( - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'targetIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Target: ', - choices = targetIds, - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ), - createInputSetting( - rowNumber = 1, - columnWidth = 6, - varName = 'outcomeIds', - uiFunction = 'shinyWidgets::pickerInput', - uiInputs = list( - label = 'Outcome: ', - choices = outcomeIds, - multiple = F, - options = shinyWidgets::pickerOptions( - actionsBox = TRUE, - liveSearch = TRUE, - size = 10, - liveSearchStyle = "contains", - liveSearchPlaceholder = "Type here to search", - virtualScroll = 50 - ) - ) - ) - ) - ) - - # plots and tables - cmdata <- shiny::reactive({ - unique( - rbind( - getCMEstimation( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - targetId = inputSelected()$targetIds, - outcomeId = inputSelected()$outcomeIds - ), - getMetaEstimation( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - targetId = inputSelected()$targetIds, - outcomeId = inputSelected()$outcomeIds - ) - ) - ) - }) - - - diagSumData <- shiny::reactive({ - getEvidenceSynthCmDiagnostics( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - inputSelected = inputSelected, - targetIds = inputSelected()$targetIds, - outcomeIds = inputSelected()$outcomeIds - ) - }) - - - resultTableServer( - id = "diagnosticsCmSummaryTable", - df = diagSumData, - colDefsInput = getColDefsESDiag( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - ) - - output$esCohortMethodPlot <- shiny::renderPlot( - createPlotForAnalysis( - cmdata() - ) - ) - - - resultTableServer( - id = "esCohortMethodTable", - df = cmdata, - colDefsInput = list( - targetId = reactable::colDef(show = F), - outcomeId = reactable::colDef(show = F), - comparatorId = reactable::colDef(show = F), - analysisId = reactable::colDef(show = F), - description = reactable::colDef( - filterable = TRUE, - header = withTooltip( - "Analysis", - "Analysis" - )), - database = reactable::colDef( - filterable = TRUE, - header = withTooltip( - "Data source", - "Data source" - )), - calibratedRr = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.HR", - "Hazard ratio (calibrated)" - )), - calibratedCi95Lb = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.LB", - "Lower bound of the 95 percent confidence interval (calibrated)" - )), - calibratedCi95Ub = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.UB", - "Upper bound of the 95 percent confidence interval (calibrated)" - )), - calibratedP = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.P", - "Two-sided p-value (calibrated)" - )), - calibratedLogRr = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.Log.HR", - "Log of Hazard ratio (calibrated)" - )), - calibratedSeLogRr = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.Se.Log.HR", - "Log Standard Error of Hazard ratio (calibrated)" - )), - target = reactable::colDef( - minWidth = 300 - ), - outcome = reactable::colDef( - minWidth = 300 - ), - comparator = reactable::colDef( - minWidth = 300 - ) - ) - ) - - } - ) - -} - - -getEsCmTargetIds <- function( - connectionHandler, - resultDatabaseSettings -){ - - sql <- "select distinct - c1.cohort_name as target, - r.target_id - - from - @schema.@cm_table_prefixresult as r - inner join - @schema.@cg_table_prefixcohort_definition as c1 - on c1.cohort_definition_id = r.target_id - ;" - - result <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - cm_table_prefix = resultDatabaseSettings$cmTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix - ) - - output <- as.list(result$targetId) - names(output) <- result$target - - return(output) - -} - -getCMEstimation <- function( - connectionHandler, - resultDatabaseSettings, - targetId, - outcomeId -){ - - if(is.null(targetId)){ - return(NULL) - } - - sql <- "select - c1.cohort_name as target, - c2.cohort_name as comparator, - c3.cohort_name as outcome, - r.target_id, r.comparator_id, r.outcome_id, r.analysis_id, - a.description, - db.cdm_source_abbreviation as database, r.calibrated_rr, - r.calibrated_ci_95_lb, r.calibrated_ci_95_ub, r.calibrated_p, - r.calibrated_log_rr, r.calibrated_se_log_rr - - from - @schema.@cm_table_prefixresult as r - inner join - @schema.@cm_table_prefixtarget_comparator_outcome as tco - on - r.target_id = tco.target_id and - r.comparator_id = tco.comparator_id and - r.outcome_id = tco.outcome_id - - inner join - - @schema.@cm_table_prefixdiagnostics_summary as unblind - on - r.analysis_id = unblind.analysis_id and - r.target_id = unblind.target_id and - r.comparator_id = unblind.comparator_id and - r.outcome_id = unblind.outcome_id and - r.database_id = unblind.database_id - - inner join - @schema.@database_table as db - on db.database_id = r.database_id - - inner join - @schema.@cg_table_prefixcohort_definition as c1 - on c1.cohort_definition_id = r.target_id - - inner join - @schema.@cg_table_prefixcohort_definition as c2 - on c2.cohort_definition_id = r.comparator_id - - inner join - @schema.@cg_table_prefixcohort_definition as c3 - on c3.cohort_definition_id = r.outcome_id - - inner join - @schema.@cm_table_prefixanalysis as a - on a.analysis_id = r.analysis_id - - where - r.calibrated_rr != 0 and - tco.outcome_of_interest = 1 and - unblind.unblind = 1 and - r.target_id = @target_id and - r.outcome_id = @outcome_id - ;" - - result <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - database_table = resultDatabaseSettings$databaseTable, - cm_table_prefix = resultDatabaseSettings$cmTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - outcome_id = outcomeId, - target_id = targetId - ) %>% - dplyr::mutate( - calibratedP = ifelse( - .data$calibratedRr < 1, - computeTraditionalP( - logRr = .data$calibratedLogRr, - seLogRr = .data$calibratedSeLogRr, - twoSided = FALSE, - upper = TRUE - ), - .data$calibratedP / 2) - ) - - return(result) -} - -getMetaEstimation <- function( - connectionHandler, - resultDatabaseSettings, - targetId, - outcomeId -){ - - if(is.null(targetId)){ - return(NULL) - } - - sql <- "select - c1.cohort_name as target, - c2.cohort_name as comparator, - c3.cohort_name as outcome, - r.target_id, r.comparator_id, r.outcome_id, r.analysis_id, - a.description, - ev.evidence_synthesis_description as database, - r.calibrated_rr, - r.calibrated_ci_95_lb, r.calibrated_ci_95_ub, r.calibrated_p, - r.calibrated_log_rr, r.calibrated_se_log_rr - - from - @schema.@es_table_prefixcm_result as r - inner join - @schema.@cm_table_prefixtarget_comparator_outcome as tco - on - r.target_id = tco.target_id and - r.comparator_id = tco.comparator_id and - r.outcome_id = tco.outcome_id - - inner join - - @schema.@es_table_prefixcm_diagnostics_summary as unblind - on - r.analysis_id = unblind.analysis_id and - r.target_id = unblind.target_id and - r.comparator_id = unblind.comparator_id and - r.outcome_id = unblind.outcome_id - - inner join - @schema.@cg_table_prefixcohort_definition as c1 - on c1.cohort_definition_id = r.target_id - - inner join - @schema.@cg_table_prefixcohort_definition as c2 - on c2.cohort_definition_id = r.comparator_id - - inner join - @schema.@cg_table_prefixcohort_definition as c3 - on c3.cohort_definition_id = r.outcome_id - - inner join - @schema.@cm_table_prefixanalysis as a - on a.analysis_id = r.analysis_id - - inner join - @schema.@es_table_prefixanalysis as ev - on ev.evidence_synthesis_analysis_id = r.evidence_synthesis_analysis_id - - where - r.calibrated_rr != 0 and - tco.outcome_of_interest = 1 and - unblind.unblind = 1 and - r.target_id = @target_id and - r.outcome_id = @outcome_id - ;" - - result <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - cm_table_prefix = resultDatabaseSettings$cmTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - es_table_prefix = resultDatabaseSettings$esTablePrefix, - outcome_id = outcomeId, - target_id = targetId - ) %>% - dplyr::mutate( - calibratedP = ifelse( - .data$calibratedRr < 1, - computeTraditionalP( - logRr = .data$calibratedLogRr, - seLogRr = .data$calibratedSeLogRr, - twoSided = FALSE, - upper = TRUE - ), - .data$calibratedP / 2) - ) - - return(unique(result)) -} - -getEvidenceSynthCmDiagnostics <- function( - connectionHandler, - resultDatabaseSettings, - inputSelected, - targetIds, - outcomeIds -){ - - if(is.null(targetIds)){ - return(NULL) - } - - cmDiagTemp <- getCmDiagnosticsData( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - inputSelected = inputSelected - ) - - if(is.null(cmDiagTemp)){ - return(NULL) - } - - # select columns of interest and rename for consistency - cmDiagTemp <- diagnosticSummaryFormat( - data = shiny::reactive({cmDiagTemp}), - idCols = c('databaseName','target'), - namesFrom = c('analysis','comparator','outcome') - ) - - # return - return(cmDiagTemp) -} - - -createPlotForAnalysis <- function(data) { - - if(is.null(data$comparator)){ - return(NULL) - } - - compText <- data.frame( - comparatorText = paste0('Comp', 1:length(unique(data$comparator))), - comparator = unique(data$comparator) - ) - - data <- merge( - data, - compText, - by = "comparator" - ) - - # make sure bayesian is at bottom - db <- unique(data$database) - bInd <- grep('bayesian', tolower(db)) - withoutb <- db[-bInd] - b <- db[bInd] - data$database <- factor( - x = data$database, - levels = c(sort(withoutb), b) - ) - metadata <- data[data$database == b,] - - breaks <- c(0.1, 0.25, 0.5, 1, 2, 4, 6, 8) - title <- sprintf("%s", data$outcome[1]) - plot <- ggplot2::ggplot( - data = data, - ggplot2::aes(x = .data$calibratedRr, y = .data$comparatorText)) + - ggplot2::geom_vline(xintercept = 1, size = 0.5) + - ggplot2::geom_point(color = "#000088", alpha = 0.8) + - ggplot2::geom_errorbarh( - ggplot2::aes( - xmin = .data$calibratedCi95Lb, - xmax = .data$calibratedCi95Ub - ), - height = 0.5, - color = "#000088", - alpha = 0.8 - ) + - ggplot2::scale_x_log10( - "Effect size (Hazard Ratio)", - breaks = breaks, - labels = breaks - ) + - - # shade the bayesian - ggplot2::geom_rect( - data = metadata, - ggplot2::aes(fill = .data$database), - xmin = -Inf, - xmax = Inf, - ymin = -Inf, - ymax = Inf, - alpha = 0.2 - ) + - - ggplot2::coord_cartesian(xlim = c(0.1, 10)) + - ggplot2::facet_grid(.data$database ~ .data$description) + - ggplot2::ggtitle(title) + - ggplot2::theme( - axis.title.y = ggplot2::element_blank(), - panel.grid.minor = ggplot2::element_blank(), - strip.text.y.right = ggplot2::element_text(angle = 0), - legend.position = "none" - ) + - ggplot2::labs( - caption = paste( - apply( - X = compText, - MARGIN = 1, - FUN = function(x){paste0(paste(substring(x, 1, 50),collapse = ': ', sep=':'), '...')} - ), - collapse = '\n ') - ) - - return(plot) -} diff --git a/R/evidence-synth-main.R b/R/evidence-synth-main.R deleted file mode 100644 index f270ab2c..00000000 --- a/R/evidence-synth-main.R +++ /dev/null @@ -1,275 +0,0 @@ -#' The location of the evidence synthesis module helper file -#' -#' @details -#' Returns the location of the evidence synthesis helper file -#' -#' @return -#' string location of the evidence synthesis helper file -#' -#' @export -evidenceSynthesisHelperFile <- function(){ - fileLoc <- system.file('evidence-synthesis-www', "evidence-synthesis.html", package = "OhdsiShinyModules") - return(fileLoc) -} - -#' The module viewer for exploring evidence-synthesis -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' -#' @return -#' The user interface to the evidence-synthesis viewer module -#' -#' @export -evidenceSynthesisViewer <- function(id=1) { - ns <- shiny::NS(id) - - shinydashboard::box( - status = 'info', - width = 12, - title = shiny::span( shiny::icon("sliders"), 'Evidence Synthesis'), - solidHeader = TRUE, - - infoHelperViewer( - id = "helper", - helpLocation= system.file("evidence-synthesis-www", "evidence-synthesis.html", package = utils::packageName()) - ), - - # add two buttons - CM or SCCs - shiny::tabsetPanel( - id = ns('typeTab'), - type = 'pills', - - shiny::tabPanel( - title = 'Cohort Method', - evidenceSynthesisCmViewer(ns('cohortMethodTab')) - ), - shiny::tabPanel( - title = 'Self Controlled Case Series', - evidenceSynthesisSccsViewer(ns('sccsTab')) - ) - - ) - - ) - -} - -#' The module server for exploring PatientLevelPrediction -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' @param connectionHandler a connection to the database with the results -#' @param resultDatabaseSettings a list containing the result schema and prefixes -#' -#' @return -#' The server for the PatientLevelPrediction module -#' -#' @export -evidenceSynthesisServer <- function( - id, - connectionHandler, - resultDatabaseSettings = list(port = 1) -) { - shiny::moduleServer( - id, - function(input, output, session) { - - evidenceSynthesisCmServer( - id = 'cohortMethodTab', - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - evidenceSynthesisSccsServer( - id = 'sccsTab', - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - } - ) -} - -# Function to get outcome ids -# used by both cm and sccs -getEsOutcomeIds <- function( - connectionHandler, - resultDatabaseSettings -) { - sql <- "select distinct - c1.cohort_name as outcome, - r.outcome_id - - from - @schema.@cm_table_prefixresult as r - inner join - @schema.@cm_table_prefixtarget_comparator_outcome as tco - on - r.target_id = tco.target_id and - r.comparator_id = tco.comparator_id and - r.outcome_id = tco.outcome_id - - inner join - @schema.@cg_table_prefixcohort_definition as c1 - on c1.cohort_definition_id = r.outcome_id - - where - tco.outcome_of_interest = 1 - ;" - - result <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - cm_table_prefix = resultDatabaseSettings$cmTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix - ) - - output <- as.list(result$outcomeId) - names(output) <- result$outcome - - return(output) - -} - -# Function to format results -# used by both cm and sccs -computeTraditionalP <- function( - logRr, - seLogRr, - twoSided = TRUE, - upper = TRUE -) -{ - z <- logRr/seLogRr - - pUpperBound <- 1 - stats::pnorm(z) - pLowerBound <- stats::pnorm(z) - - if (twoSided) { - return(2 * pmin(pUpperBound, pLowerBound)) - } - else if (upper) { - return(pUpperBound) - } - else { - return(pLowerBound) - } -} - - - -# Functions to get column formatting and names -# used by both cm and sccs -getOACcombinations <- function( - connectionHandler, - resultDatabaseSettings -){ - - sql <- "SELECT DISTINCT - CONCAT(cma.description, '_', cgcd2.cohort_name) as col_names - FROM - @schema.@cm_table_prefixdiagnostics_summary cmds - INNER JOIN @schema.@cm_table_prefixanalysis cma - ON cmds.analysis_id = cma.analysis_id - INNER JOIN @schema.@cg_table_prefixcohort_definition cgcd2 - ON cmds.comparator_id = cgcd2.cohort_definition_id - - union - - SELECT - CONCAT(a.description, '_', cov.covariate_name) as col_names - - FROM @schema.@sccs_table_prefixdiagnostics_summary ds - - INNER JOIN - @schema.@sccs_table_prefixanalysis a - on a.analysis_id = ds.analysis_id - - INNER JOIN - @schema.@sccs_table_prefixcovariate cov - on cov.covariate_id = ds.covariate_id and - cov.exposures_outcome_set_id = ds.exposures_outcome_set_id and - cov.analysis_id = ds.analysis_id - ;" - - result <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, - cm_table_prefix = resultDatabaseSettings$cmTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - database_table = resultDatabaseSettings$databaseTable - ) - - res <- result$colNames - names(res) <- result$colNames - - return(res) -} - -getColDefsESDiag <- function( - connectionHandler, - resultDatabaseSettings -){ - - fixedColumns = list( - databaseName = reactable::colDef( - header = withTooltip( - "Database", - "The database name" - ), - sticky = "left" - ), - target = reactable::colDef( - header = withTooltip( - "Target", - "The target cohort of interest " - ), - sticky = "left" - ) - ) - - outcomes <- getEsOutcomeIds( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - analyses <- getOACcombinations( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - colnameFormat <- merge(unique(names(analyses)), unique(names(outcomes))) - colnameFormat <- apply(colnameFormat, 1, function(x){paste(x, collapse = '_', sep = '_')}) - - styleList <- lapply( - colnameFormat, - FUN = function(x){ - reactable::colDef( - header = withTooltip( - substring(x,1,40), - x - ), - style = function(value) { - color <- 'orange' - if(is.na(value)){ - color <- 'black' - }else if(value == 'Pass'){ - color <- '#AFE1AF' - }else if(value == 'Fail'){ - color <- '#E97451' - } - list(background = color) - } - ) - } - ) - names(styleList) <- colnameFormat - result <- append(fixedColumns, styleList) - - return(result) -} diff --git a/R/evidence-synth-sccs.R b/R/evidence-synth-sccs.R deleted file mode 100644 index 839242dc..00000000 --- a/R/evidence-synth-sccs.R +++ /dev/null @@ -1,536 +0,0 @@ -evidenceSynthesisSccsViewer <- function(id=1) { - ns <- shiny::NS(id) - - shiny::div( - - inputSelectionViewer(ns("input-selection-sccs")), - - shiny::conditionalPanel( - condition = 'input.generate != 0', - ns = shiny::NS(ns("input-selection-sccs")), - - shiny::tabsetPanel( - type = 'pills', - id = ns('esSccsTabs'), - - # diagnostic view - shiny::tabPanel( - title = 'Diagnostics', - resultTableViewer(ns("diagnosticsSccsSummaryTable")) - ), - - shiny::tabPanel( - "Plot", - shiny::plotOutput(ns('esSccsPlot')) - ), - shiny::tabPanel( - "Table", - resultTableViewer(ns("esSccsTable")) - ) - ) - ) - ) -} - -evidenceSynthesisSccsServer <- function( - id, - connectionHandler, - resultDatabaseSettings = list(port = 1) -) { - shiny::moduleServer( - id, - function(input, output, session) { - - outcomeIds <- getEsOutcomeIds( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - exposureIndicationInput <- .getSccsExposureIndicationSelection(connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings) - - inputSelected <- inputSelectionServer( - id = "input-selection-sccs", - inputSettingList = list( - exposureIndicationInput, - createInputSetting( - rowNumber = 2, - columnWidth = 12, - varName = 'outcomeIds', - uiFunction = 'shinyWidgets::virtualSelectInput', - uiInputs = list( - label = 'Outcome: ', - choices = outcomeIds, - multiple = F, - search = TRUE - ) - ) - ) - ) - - - - diagSumData <- shiny::reactive({ - getEvidenceSynthSccsDiagnostics( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - inputSelected = inputSelected, - exposure = inputSelected()$exposure, - outcomeIds = inputSelected()$outcomeIds - ) - }) - - # SCCS plots and tables - resultTableServer( - id = "diagnosticsSccsSummaryTable", - df = diagSumData, - colDefsInput = getColDefsESDiag( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - ) - - sccsData <- shiny::reactive({ - unique( - getSccsEstimation( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - exposure = inputSelected()$exposure, - outcomeId = inputSelected()$outcomeIds - ) - ) - }) - - output$esSccsPlot <- shiny::renderPlot({ - sccsRes <- sccsData() - shiny::validate(shiny::need(hasData(sccsRes), "No valid data for selected target")) - createPlotForSccsAnalysis(sccsRes) - }) - - - resultTableServer( - id = "esSccsTable", - df = sccsData, - colDefsInput = list( - targetId = reactable::colDef(show = F), - outcomeId = reactable::colDef(show = F), - analysisId = reactable::colDef(show = F), - description = reactable::colDef( - filterable = TRUE, - header = withTooltip( - "Analysis", - "Analysis" - ), - minWidth = 300 - ), - database = reactable::colDef( - filterable = TRUE, - header = withTooltip( - "Data source", - "Data source" - )), - calibratedRr = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.IRR", - "Incidence rate ratio (calibrated)" - )), - calibratedCi95Lb = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.LB", - "Lower bound of the 95 percent confidence interval (calibrated)" - )), - calibratedCi95Ub = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.UB", - "Upper bound of the 95 percent confidence interval (calibrated)" - )), - calibratedP = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.P", - "Two-sided p-value (calibrated)" - )), - calibratedLogRr = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.Log.IRR", - "Log of Incidence rate ratio (calibrated)" - )), - calibratedSeLogRr = reactable::colDef( - format = reactable::colFormat(digits = 3), - header = withTooltip( - "Cal.Se.Log.IRR", - "Log Standard Error of Incidence rate ratio (calibrated)" - )), - target = reactable::colDef( - minWidth = 300 - ), - outcome = reactable::colDef( - minWidth = 300 - ) - ) - ) - - } - ) - -} - -getSccsTargets <- function( - connectionHandler, - resultDatabaseSettings -){ - - output <- sccsGetExposureIndications( - connectionHandler, - resultDatabaseSettings - ) - return(output) - -} - -getSccsEstimation <- function( - connectionHandler, - resultDatabaseSettings, - exposure, - outcomeId -){ - - if (is.null(outcomeId)) { - return(NULL) - } - - if (is.character(exposure)) { - exposureGroup <- strsplit(exposure, " ")[[1]] - targetId <- exposureGroup[[1]] - indicationIds <- exposureGroup[[2]] - } else { - targetId <- -1 - indicationIds <- -1 - } - - if (any(indicationIds == -1)) { - indicationIds <- NULL - } - - sql <- "select - c1.cohort_name as target, - c3.cohort_name as outcome, - cov.era_id as target_id, eos.outcome_id, r.analysis_id, - a.description, - cov.covariate_name as type, - db.cdm_source_abbreviation as database, - r.calibrated_rr, - r.calibrated_ci_95_lb, - r.calibrated_ci_95_ub, - r.calibrated_p, - r.calibrated_log_rr, - r.calibrated_se_log_rr - - from - @schema.@sccs_table_prefixresult as r - inner join - @schema.@sccs_table_prefixexposures_outcome_set as eos - on - r.exposures_outcome_set_id = eos.exposures_outcome_set_id - - inner join - @schema.@sccs_table_prefixcovariate as cov - on - r.covariate_id = cov.covariate_id and - r.database_id = cov.database_id and - r.analysis_id = cov.analysis_id and - r.exposures_outcome_set_id = cov.exposures_outcome_set_id - - inner join - @schema.@sccs_table_prefixexposure as ex - on - ex.era_id = cov.era_id and - ex.exposures_outcome_set_id = cov.exposures_outcome_set_id - - inner join - - @schema.@sccs_table_prefixdiagnostics_summary as unblind - on - r.analysis_id = unblind.analysis_id and - r.exposures_outcome_set_id = unblind.exposures_outcome_set_id and - r.covariate_id = unblind.covariate_id and - r.database_id = unblind.database_id - - inner join - @schema.@database_table as db - on db.database_id = r.database_id - - inner join - @schema.@cg_table_prefixcohort_definition as c1 - on c1.cohort_definition_id = cov.era_id - - inner join - @schema.@cg_table_prefixcohort_definition as c3 - on c3.cohort_definition_id = eos.outcome_id - - inner join - @schema.@sccs_table_prefixanalysis as a - on a.analysis_id = r.analysis_id - - where - r.calibrated_rr != 0 and - --ex.true_effect_size != 1 and - cov.covariate_name in ('Main', 'Second dose') and - unblind.unblind = 1 and - cov.era_id = @target_id and - eos.outcome_id = @outcome_id - {@use_indications} ? {and eos.nesting_cohort_id IN (@indication_ids)} : {and eos.nesting_cohort_id IS NULL} - ;" - - result <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - database_table = resultDatabaseSettings$databaseTable, - sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - outcome_id = outcomeId, - target_id = targetId, - indication_ids = indicationIds, - use_indications = !is.null(indicationIds) - ) - - sql <- "select distinct - c1.cohort_name as target, - c3.cohort_name as outcome, - cov.era_id as target_id, eos.outcome_id, r.analysis_id, - a.description, - cov.covariate_name as type, - ev.evidence_synthesis_description as database, - r.calibrated_rr, - r.calibrated_ci_95_lb, - r.calibrated_ci_95_ub, - r.calibrated_p, - r.calibrated_log_rr, - r.calibrated_se_log_rr - - from - @schema.@es_table_prefixsccs_result as r - inner join - @schema.@sccs_table_prefixexposures_outcome_set as eos - on - r.exposures_outcome_set_id = eos.exposures_outcome_set_id - - inner join - @schema.@sccs_table_prefixcovariate as cov - on - r.covariate_id = cov.covariate_id and - r.analysis_id = cov.analysis_id and - r.exposures_outcome_set_id = cov.exposures_outcome_set_id - - inner join - @schema.@sccs_table_prefixexposure as ex - on - ex.era_id = cov.era_id and - ex.exposures_outcome_set_id = cov.exposures_outcome_set_id - - inner join - - @schema.@es_table_prefixsccs_diagnostics_summary as unblind - on - r.analysis_id = unblind.analysis_id and - r.exposures_outcome_set_id = unblind.exposures_outcome_set_id and - r.covariate_id = unblind.covariate_id and - r.evidence_synthesis_analysis_id = unblind.evidence_synthesis_analysis_id - - inner join - @schema.@cg_table_prefixcohort_definition as c1 - on c1.cohort_definition_id = cov.era_id - - inner join - @schema.@cg_table_prefixcohort_definition as c3 - on c3.cohort_definition_id = eos.outcome_id - - inner join - @schema.@sccs_table_prefixanalysis as a - on a.analysis_id = r.analysis_id - - inner join - @schema.@es_table_prefixanalysis as ev - on ev.evidence_synthesis_analysis_id = r.evidence_synthesis_analysis_id - - where - r.calibrated_rr != 0 and - --ex.true_effect_size != 1 and - cov.covariate_name in ('Main', 'Second dose') and - unblind.unblind = 1 and - cov.era_id = @target_id and - eos.outcome_id = @outcome_id - {@use_indications} ? {and eos.nesting_cohort_id IN (@indication_ids)} : {and eos.nesting_cohort_id IS NULL} - ;" - - result2 <- connectionHandler$queryDb( - sql = sql, - schema = resultDatabaseSettings$schema, - es_table_prefix = resultDatabaseSettings$esTablePrefix, - sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - outcome_id = outcomeId, - target_id = targetId, - indication_ids = indicationIds, - use_indications = !is.null(indicationIds) - ) - - return(rbind(result,result2)) - -} - -createPlotForSccsAnalysis <- function( - data -){ - - if(is.null(data)){ - return(NULL) - } - - # change the description to add at bottom - renameDf <- data.frame( - shortName = paste0( - 1:length(unique(data$description)), - ') ', - substring(sort(unique(data$description)), 1, 15), - '...' - ), - description = sort(unique(data$description)) - ) - data <- merge( - x = data, - y = renameDf, - by = 'description' - ) - - # make sure bayesian is at bottom - db <- unique(data$database) - bInd <- grep('bayesian', tolower(db)) - withoutb <- db[-bInd] - b <- db[bInd] - data$database <- factor( - x = data$database, - levels = c(sort(withoutb), b) - ) - metadata <- data[data$database == b,] - - breaks <- c(0.1, 0.25, 0.5, 1, 2, 4, 6, 8) - plot <- ggplot2::ggplot( - data = data, - ggplot2::aes(x = .data$calibratedRr, y = .data$type) - ) + - ggplot2::geom_vline(xintercept = 1, size = 0.5) + - ggplot2::geom_point(color = "#000088", alpha = 0.8) + - ggplot2::geom_errorbarh( - ggplot2::aes( - xmin = .data$calibratedCi95Lb, - xmax = .data$calibratedCi95Ub - ), - height = 0.5, - color = "#000088", - alpha = 0.8 - ) + - ggplot2::scale_x_log10( - "Effect size (Incidence Rate Ratio)", - breaks = breaks, - labels = breaks - ) + - - # shade the bayesian - ggplot2::geom_rect( - data = metadata, - ggplot2::aes(fill = .data$database), - xmin = -Inf, - xmax = Inf, - ymin = -Inf, - ymax = Inf, - alpha = 0.2 - ) + - - ggplot2::coord_cartesian(xlim = c(0.1, 10)) + - ggplot2::facet_grid(.data$database ~ .data$shortName) + - ggplot2::ggtitle(data$outcome[1]) + - ggplot2::theme( - axis.title.y = ggplot2::element_blank(), - panel.grid.minor = ggplot2::element_blank(), - strip.text.y.right = ggplot2::element_text(angle = 0), - legend.position = "none" - ) - - ### Add table below the graph - renameDf$description <- sapply( - strwrap(renameDf$description, width = 50, simplify = FALSE), - paste, - collapse = "\n" - ) - - tt <- gridExtra::ttheme_default( - base_size = 8, - colhead=list(fg_params = list(parse=TRUE)) - ) - tbl <- gridExtra::tableGrob( - renameDf, - rows=NULL, - theme=tt - ) - plot <- gridExtra::grid.arrange( - plot, - tbl, - nrow = 2, - as.table = TRUE - ) - - return(plot) -} - -getEvidenceSynthSccsDiagnostics <- function( - connectionHandler, - resultDatabaseSettings, - inputSelected, - exposure, - outcomeIds -){ - - if(is.null(exposure)){ - return(NULL) - } - - if (is.character(exposure)) { - exposureGroup <- strsplit(exposure, " ")[[1]] - targetId <- exposureGroup[[1]] - indicationIds <- exposureGroup[[2]] - } else { - targetId <- -1 - indicationIds <- -1 - } - - if (any(indicationIds == -1)) { - indicationIds <- NULL - } - - sccsDiagTemp <- getSccsAllDiagnosticsSummary( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - targetIds = targetId, - indicationIds = indicationIds, - outcomeIds = outcomeIds - ) - - if(is.null(sccsDiagTemp)){ - return(NULL) - } - - # select columns of interest and rename for consistency - sccsDiagTemp <- diagnosticSummaryFormat( - data = shiny::reactive({sccsDiagTemp}), - idCols = c('databaseName','target'), - namesFrom = c('analysis','covariateName','outcome') - ) - - # return - return(sccsDiagTemp) -} diff --git a/R/helper-getPredictionProtocol.R b/R/helper-getPredictionProtocol.R index 54eed021..ed0d6a24 100644 --- a/R/helper-getPredictionProtocol.R +++ b/R/helper-getPredictionProtocol.R @@ -1,15 +1,15 @@ createPredictionProtocol <- function( connectionHandler, resultDatabaseSettings, - modelDesignId, - output, - intermediatesDir = file.path(tempdir(), 'plp-prot') + modelDesignId, + output, + intermediatesDir = file.path(tempdir(), 'plp-prot') ){ #require('CirceR') # get the data #protocolLoc <- 'modules/prediction/documents/main.Rmd' - protocolLoc <- system.file('prediction-document', "main.Rmd", package = "OhdsiShinyModules") + protocolLoc <- system.file('patient-level-prediction-document', "main.Rmd", package = "OhdsiShinyModules") if(!dir.exists(intermediatesDir)){ dir.create(intermediatesDir) diff --git a/R/helpers-componentsCreateCustomColDefList.R b/R/helpers-componentsCreateCustomColDefList.R index d629cf46..a6403d80 100644 --- a/R/helpers-componentsCreateCustomColDefList.R +++ b/R/helpers-componentsCreateCustomColDefList.R @@ -10,7 +10,7 @@ #' whatever raw column names are passed in #' @param customColDefOptions A list of lists, where the inner lists are any custom options from #' reactable::colDef for each column -#' +#' @family {Utils} #' @return A named list of reactable::colDef objects #' @export #' @family {Utils} @@ -55,7 +55,7 @@ createCustomColDefList <- function(rawColNames, niceColNames = NULL, result[[i]] <- do.call(reactable::colDef, colDefOptions) } - + names(result) <- rawColNames return(result) @@ -65,13 +65,32 @@ createCustomColDefList <- function(rawColNames, niceColNames = NULL, # examples # Define custom column definitions # customColDefs <- createCustomColDefList( -# rawColNames = mydf$raw, -# niceColNames = c("Name", "Age", "Country"), -# tooltipText = c("Person's Name", "Person's Age", "Country"), +# rawColNames = colnames(comb), +# niceColNames = c("Database Name", +# "Covariate Name", +# "Mean Target Before Matching", +# "Mean Comparator Before Matching", +# "Abs Val StdDiff Before Matching", +# "Mean Target After Matching", +# "Mean Comparator After Matching", +# "Abs Val StdDiff After Matching"), +# tooltipText = c("The name of the database", +# "The name of the covariate", +# "Mean (Proportion) in Target Before Matching", +# "Mean (Proportion) in Comparator Before Matching", +# "Absolute Value of the Standardized Mean Difference Before Matching", +# "Mean (Proportion) in Target After Matching", +# "Mean (Proportion) in Comparator Before Matching", +# "Absolute Value of the Standardized Mean Difference After Matching"), # customColDefOptions = list( # list(NULL), # No aggregation for "Name" column -# list(aggregate = "mean"), # Aggregate "Age" column using mean -# list(NULL) # No aggregation for "Country" column +# list(NULL), # Aggregate "Age" column using mean +# list(NULL), +# list(NULL), +# list(NULL), # No aggregation for "Name" column +# list(NULL), # Aggregate "Age" column using mean +# list(NULL), +# list(NULL)# No aggregation for "Country" column # ) # ) @@ -83,7 +102,7 @@ createCustomColDefList <- function(rawColNames, niceColNames = NULL, #' Make a label for an html button #' #' @param label The desired label for hte button -#' +#' @family {Utils} #' @return html code to make a button label #' @export #' @family {Utils} diff --git a/R/helpers-example.R b/R/helpers-example.R new file mode 100644 index 00000000..a5949a97 --- /dev/null +++ b/R/helpers-example.R @@ -0,0 +1,20 @@ +#' A connection details to an example result database +#' +#' @details +#' Finds the location within the package of an sqlite database with example results for 1) CohortGenerator, +#' 2) Characterization, 3) PatientLevelPrediction, 4) CohortMethod, 5) SelfControlledCaseSeries and 6) CohortIncidence +#' @family {Example} +#' @return +#' The connection details to an example result database +#' +#' @export +getExampleConnectionDetails <- function(){ + server <- system.file("extdata", "results.sqlite", package = "OhdsiShinyModules") + + cd <- DatabaseConnector::createConnectionDetails( + dbms = 'sqlite', + server = server + ) + + return(cd) +} \ No newline at end of file diff --git a/R/helpers-migrations.R b/R/helpers-migrations.R index b67f4ec2..e77e7cd5 100644 --- a/R/helpers-migrations.R +++ b/R/helpers-migrations.R @@ -3,7 +3,7 @@ #' Get Migrations #' @description #' Checks to see if migrations are present in the database for a given table prefix -#' +#' @family {Utils} #' @noRd getMigrations <- function(connectionHandler, resultDatabaseSettings, tablePrefix) { migrations <- data.frame() @@ -23,7 +23,7 @@ getMigrations <- function(connectionHandler, resultDatabaseSettings, tablePrefix #' Migration present #' @description #' Given a data.frame of migrations check if a migration number is present -#' +#' @family {Utils} #' @noRd migrationPresent <- function(migrations, migrationId) { if (nrow(migrations) == 0) { diff --git a/R/helpers-sccsPlots.R b/R/helpers-sccsPlots.R index e2b7f066..947557f5 100644 --- a/R/helpers-sccsPlots.R +++ b/R/helpers-sccsPlots.R @@ -120,7 +120,9 @@ plotTimeTrend <- function(timeTrend) { plotTimeToEventSccs <- function(timeToEvent) { - + if(nrow(timeToEvent) == 0){ + shiny::validate('No Rows') + } events <- timeToEvent %>% dplyr::transmute(.data$week, type = "Events", @@ -173,24 +175,21 @@ plotTimeToEventSccs <- function(timeToEvent) { drawAttritionDiagram <- function(attrition) { - formatNumber <- function(x) { - return(formatC(x, big.mark = ",")) - } - + addStep <- function(data, attrition, row) { data$leftBoxText[length(data$leftBoxText) + 1] <- paste(attrition$description[row], "\n", "Cases: ", - formatNumber(attrition$outcomeSubjects[row]), + format(attrition$outcomeSubjects[row], scientific = FALSE), "\n", "Outcomes: ", - formatNumber(attrition$outcomeEvents[row]), + format(attrition$outcomeEvents[row], scientific = FALSE), sep = "") data$rightBoxText[length(data$rightBoxText) + 1] <- paste("Cases: ", - formatNumber(data$currentCases - attrition$outcomeSubjects[row]), + format(data$currentCases - attrition$outcomeSubjects[row], scientific = FALSE), "\n", "Outcomes: ", - formatNumber(data$currentOutcomes - attrition$outcomeEvents[row]), + format(data$currentOutcomes - attrition$outcomeEvents[row], scientific = FALSE), sep = "") data$currentCases <- attrition$outcomeSubjects[row] data$currentOutcomes <- attrition$outcomeEvents[row] @@ -199,10 +198,10 @@ drawAttritionDiagram <- function(attrition) { data <- list(leftBoxText = c(paste("All outcomes occurrences:\n", "Cases: ", - formatNumber(attrition$outcomeSubjects[1]), + format(attrition$outcomeSubjects[1], scientific = FALSE), "\n", "Outcomes: ", - formatNumber(attrition$outcomeEvents[1]), + format(attrition$outcomeEvents[1], scientific = FALSE), sep = "")), rightBoxText = c(""), currentCases = attrition$outcomeSubjects[1], @@ -300,12 +299,23 @@ drawAttritionDiagram <- function(attrition) { } plotEventDepObservation <- function(eventDepObservation, maxMonths = 12) { + if(nrow(eventDepObservation) == 0){ + shiny::validate('No Rows') + } + eventDepObservation <- eventDepObservation %>% dplyr::filter(.data$monthsToEnd <= maxMonths) %>% dplyr::mutate( outcomes = pmax(0, .data$outcomes), censoring = ifelse(.data$censored == 1, "Censored", "Uncensored") ) + if(nrow(eventDepObservation) == 0){ + shiny::validate('No Rows after filtering') + } + if(is.infinite(max(eventDepObservation$monthsToEnd))){ + shiny::validate('Infinite max') + } + timeBreaks <- 0:ceiling(max(eventDepObservation$monthsToEnd)) timeLabels <- timeBreaks * 30.5 @@ -335,7 +345,20 @@ plotEventDepObservation <- function(eventDepObservation, maxMonths = 12) { } plotSpanning <- function(spanning, type = "age") { + + if(nrow(spanning) == 0){ + shiny::validate('No rows') + } + if (type == "age") { + + if(is.infinite(min(spanning$ageMonth))){ + shiny::validate('infinte min age month') + } + if(is.infinite(max(spanning$ageMonth))){ + shiny::validate('infinte max age month') + } + spanning <- spanning %>% dplyr::mutate(x = .data$ageMonth) labels <- seq(ceiling(min(spanning$ageMonth) / 12), floor(max(spanning$ageMonth) / 12)) @@ -537,7 +560,16 @@ cyclicSplineDesign <- function(x, knots, ord = 4) { X1 } -plotControlEstimates <- function(controlEstimates) { +plotControlEstimates <- function( + controlEstimates, + ease = NULL + ) { + if(nrow(controlEstimates) == 0){ + shiny::validate('No rows') + } + + titleText <- paste('Ease: ', ease) + size <- 2 labelY <- 0.7 d <- rbind(data.frame(yGroup = "Uncalibrated", @@ -556,7 +588,10 @@ plotControlEstimates <- function(controlEstimates) { d <- d[!is.na(d$ci95Lb),] d <- d[!is.na(d$ci95Ub),] if (nrow(d) == 0) { - return(NULL) + shiny::validate('No rows') + } + if (nrow(d) == 1) { + shiny::validate('Only one row so cannot aggregate') } d$Group <- as.factor(d$trueRr) d$Significant <- d$ci95Lb > d$trueRr | d$ci95Ub < d$trueRr @@ -630,7 +665,8 @@ plotControlEstimates <- function(controlEstimates) { strip.text.x = theme, strip.text.y = theme, strip.background = ggplot2::element_blank(), - legend.position = "none") + legend.position = "none") + + ggplot2::ggtitle(label = titleText) return(plot) } diff --git a/R/home-main.R b/R/home-main.R new file mode 100644 index 00000000..e566173f --- /dev/null +++ b/R/home-main.R @@ -0,0 +1,101 @@ +#' The location of the home module helper file +#' +#' @details +#' Returns the location of the home helper file +#' @family {Home} +#' @return +#' string location of the home helper file +#' +#' @export +homeHelperFile <- function(){ + fileLoc <- system.file('home-www', "home.html", package = "OhdsiShinyModules") + return(fileLoc) +} + +#' The module viewer for exploring home +#' +#' @details +#' The user specifies the id for the module +#' @family {Home} +#' @param id the unique reference id for the module +#' +#' @return +#' The user interface to the home viewer module +#' +#' @export +homeViewer <- function(id=1) { + ns <- shiny::NS(id) + + screens <- list( + shinyglide::screen( + shiny::p("Assure Executive Summary"), + shiny::p("Study Name"), + shiny::p("The study question was..."), + shiny::p(""), + next_label="Estimation Results" + ), + shinyglide::screen( + shiny::p("Estimation Results"), + shiny::p("User Inputs are possible"), + shiny::numericInput( + inputId = ns("n"), + label = "n", + value = 10, + min = 10 + ), + next_label="Prediction Results" + ), + shinyglide::screen( + shiny::p("Cool plot here"), + shiny::plotOutput(ns("cool_plot")) + ) + ) + + shinydashboard::box( + status = 'info', width = 12, + title = shiny::span( shiny::icon("house"), "Executive Summary"), + solidHeader = TRUE, + + shinyglide::glide( + height = "350px", + screens + ) + + ) + +} + + +#' The module server for exploring home +#' +#' @details +#' The user specifies the id for the module +#' +#' @param id the unique reference id for the module +#' @param connectionHandler a connection to the database with the results +#' @param resultDatabaseSettings a list containing the prediction result schema and connection details +#' @family {Home} +#' @return +#' The server for the home module +#' +#' @export +homeServer <- function( + id, + connectionHandler, + resultDatabaseSettings = list(port = 1) +) { + shiny::moduleServer( + id, + function(input, output, session) { + + output$cool_plot <- shiny::renderPlot({ + graphics::hist( + stats::rnorm(input$n), + main = paste("n =", input$n), + xlab = "" + ) + }) + + } + ) +} \ No newline at end of file diff --git a/R/patient-level-prediction-calibration.R b/R/patient-level-prediction-calibration.R index 5f1ba549..ba241700 100644 --- a/R/patient-level-prediction-calibration.R +++ b/R/patient-level-prediction-calibration.R @@ -26,7 +26,7 @@ #' #' @return #' The user interface to the prediction model calibration module -#' +#' @family {PatientLevelPrediction} #' @export patientLevelPredictionCalibrationViewer <- function(id) { ns <- shiny::NS(id) @@ -79,7 +79,7 @@ patientLevelPredictionCalibrationViewer <- function(id) { #' @param connectionHandler the connection to the prediction result database #' @param inputSingleView the current tab #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the prediction calibration module #' diff --git a/R/patient-level-prediction-covariateSummary.R b/R/patient-level-prediction-covariateSummary.R index e9f6d237..0b854253 100644 --- a/R/patient-level-prediction-covariateSummary.R +++ b/R/patient-level-prediction-covariateSummary.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the covariate summary module #' @@ -85,7 +85,7 @@ patientLevelPredictionCovariateSummaryViewer <- function(id) { #' @param connectionHandler the connection to the prediction result database #' @param inputSingleView the current tab #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the covariate summary module #' diff --git a/R/patient-level-prediction-cutoff.R b/R/patient-level-prediction-cutoff.R index b0ede12f..776cf77f 100644 --- a/R/patient-level-prediction-cutoff.R +++ b/R/patient-level-prediction-cutoff.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the prediction cut-off module #' @@ -96,7 +96,7 @@ patientLevelPredictionCutoffViewer <- function(id) { #' @param connectionHandler the connection to the prediction result database #' @param inputSingleView the current tab #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the prediction cut-off module #' diff --git a/R/patient-level-prediction-designSummary.R b/R/patient-level-prediction-designSummary.R index 2f34649d..1e9d86c3 100644 --- a/R/patient-level-prediction-designSummary.R +++ b/R/patient-level-prediction-designSummary.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the prediction design module #' @@ -48,7 +48,7 @@ patientLevelPredictionDesignSummaryViewer <- function(id) { #' @param id the unique reference id for the module #' @param connectionHandler the connection to the prediction result database #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the prediction design module #' diff --git a/R/patient-level-prediction-diagnostics.R b/R/patient-level-prediction-diagnostics.R index 85121fe1..2732c176 100644 --- a/R/patient-level-prediction-diagnostics.R +++ b/R/patient-level-prediction-diagnostics.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the prediction diagnostic module #' @@ -62,7 +62,7 @@ patientLevelPredictionDiagnosticsViewer <- function(id) { #' @param modelDesignId the unique id for the model design #' @param connectionHandler the connection to the prediction result database #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the prediction diagnostic module #' diff --git a/R/patient-level-prediction-discrimination.R b/R/patient-level-prediction-discrimination.R index 0fcca0af..d30dc0ab 100644 --- a/R/patient-level-prediction-discrimination.R +++ b/R/patient-level-prediction-discrimination.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the model discrimination results module #' @@ -149,7 +149,7 @@ patientLevelPredictionDiscriminationViewer <- function(id) { #' @param connectionHandler the connection to the prediction result database #' @param inputSingleView the current tab #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the model discrimination module #' diff --git a/R/patient-level-prediction-main.R b/R/patient-level-prediction-main.R index 943b63e8..400f0a48 100644 --- a/R/patient-level-prediction-main.R +++ b/R/patient-level-prediction-main.R @@ -21,7 +21,7 @@ #' #' @details #' Returns the location of the prediction helper file -#' +#' @family {PatientLevelPrediction} #' @return #' string location of the prediction helper file #' @@ -37,7 +37,7 @@ patientLevelPredictionHelperFile <- function(){ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the PatientLevelPrediction viewer module #' @@ -117,7 +117,7 @@ patientLevelPredictionViewer <- function(id=1) { #' @param id the unique reference id for the module #' @param connectionHandler a connection to the database with the results #' @param resultDatabaseSettings a list containing the prediction result schema and connection details -#' +#' @family {PatientLevelPrediction} #' @return #' The server for the PatientLevelPrediction module #' @@ -264,7 +264,13 @@ patientLevelPredictionServer <- function( file.remove(file.path(protocolOutputLoc, 'main.html')) } tryCatch( - {createPredictionProtocol( + { + shiny::showNotification( + ui = 'Generating protocol - takes some time', + type = 'message' + ) + + createPredictionProtocol( connectionHandler = connectionHandler, resultDatabaseSettings = resultDatabaseSettings, modelDesignId = designSummary$reportId(), diff --git a/R/patient-level-prediction-modelSummary.R b/R/patient-level-prediction-modelSummary.R index 1c2a7faf..8b0a7348 100644 --- a/R/patient-level-prediction-modelSummary.R +++ b/R/patient-level-prediction-modelSummary.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the summary module #' @@ -59,7 +59,7 @@ patientLevelPredictionModelSummaryViewer <- function(id) { #' @param connectionHandler the connection to the prediction result database #' @param resultDatabaseSettings a list containing the result schema and prefixes #' @param modelDesignId a reactable id specifying the prediction model design identifier -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the summary module #' @@ -276,12 +276,7 @@ getModelDesignPerformanceSummary <- function( inner join @schema.@plp_table_prefixmodel_designs as model_designs on model_designs.model_design_id = results.model_design_id - -- and results.target_id = model_designs.target_id - -- and results.outcome_id = model_designs.outcome_id and - -- results.tar_id = model_designs.tar_id and - -- results.population_setting_id = model_designs.population_setting_id - -- and results.plp_data_setting_id = model_designs.plp_data_setting_id - + LEFT JOIN (SELECT c.cohort_id, cd.cohort_name FROM @schema.@plp_table_prefixcohorts c inner join @schema.@cg_table_prefixcohort_definition cd @@ -293,11 +288,11 @@ getModelDesignPerformanceSummary <- function( on c.cohort_definition_id = cd.cohort_definition_id ) AS outcomes ON results.outcome_id = outcomes.cohort_id LEFT JOIN (select dd.database_id, md.cdm_source_abbreviation database_acronym - from @schema.@database_table_prefixdatabase_meta_data md inner join + from @schema.@database_table_prefix@database_table md inner join @schema.@plp_table_prefixdatabase_details dd on md.database_id = dd.database_meta_data_id) AS d ON results.development_database_id = d.database_id LEFT JOIN (select dd.database_id, md.cdm_source_abbreviation database_acronym - from @schema.@database_table_prefixdatabase_meta_data md inner join + from @schema.@database_table_prefix@database_table md inner join @schema.@plp_table_prefixdatabase_details dd on md.database_id = dd.database_meta_data_id) AS v ON results.validation_database_id = v.database_id LEFT JOIN @schema.@plp_table_prefixtars AS tars ON results.tar_id = tars.tar_id @@ -314,6 +309,7 @@ getModelDesignPerformanceSummary <- function( plp_table_prefix = resultDatabaseSettings$plpTablePrefix, model_design_id = modelDesignId(), database_table_prefix = resultDatabaseSettings$databaseTablePrefix, + database_table = resultDatabaseSettings$databaseTable, cg_table_prefix = resultDatabaseSettings$cgTablePrefix ) diff --git a/R/patient-level-prediction-netbenefit.R b/R/patient-level-prediction-netbenefit.R index 9508bc3e..e3f276c1 100644 --- a/R/patient-level-prediction-netbenefit.R +++ b/R/patient-level-prediction-netbenefit.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the net-benefit module #' @@ -71,7 +71,7 @@ patientLevelPredictionNbViewer <- function(id) { #' @param connectionHandler the connection to the prediction result database #' @param inputSingleView the current tab #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the net-benefit module #' diff --git a/R/patient-level-prediction-settings.R b/R/patient-level-prediction-settings.R index 34093503..80731ea6 100644 --- a/R/patient-level-prediction-settings.R +++ b/R/patient-level-prediction-settings.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the settings module #' @@ -64,7 +64,7 @@ patientLevelPredictionSettingsViewer <- function(id) { #' @param connectionHandler the connection to the prediction result database #' @param inputSingleView the current tab #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the settings module #' diff --git a/R/patient-level-prediction-validation.R b/R/patient-level-prediction-validation.R index 0cdff2f7..11d55455 100644 --- a/R/patient-level-prediction-validation.R +++ b/R/patient-level-prediction-validation.R @@ -23,7 +23,7 @@ #' The user specifies the id for the module #' #' @param id the unique reference id for the module -#' +#' @family {PatientLevelPrediction} #' @return #' The user interface to the validation module #' @@ -64,7 +64,7 @@ patientLevelPredictionValidationViewer <- function(id) { #' @param connectionHandler the connection to the prediction result database #' @param inputSingleView the current tab #' @param resultDatabaseSettings a list containing the result schema and prefixes -#' +#' @family {PatientLevelPrediction} #' @return #' The server to the validation module #' diff --git a/R/phevaluator-main.R b/R/phevaluator-main.R index 17ecfd82..6e5b3026 100644 --- a/R/phevaluator-main.R +++ b/R/phevaluator-main.R @@ -21,7 +21,7 @@ #' The location of the phevaluator module helper file #' #' @details Returns the location of the cohort-generator helper file -#' +#' @family {PheValuator} #' @return String location of the phevaluator helper file #' #' @export @@ -36,7 +36,7 @@ phevaluatorHelperFile <- function() { #' The viewer of the phevaluator module #' #' @param id The unique reference id for the module -#' +#' @family {PheValuator} #' @return The user interface to the phevaluator results viewer #' #' @export @@ -49,14 +49,7 @@ phevaluatorViewer <- function(id) { width = "100%", title = shiny::span(shiny::icon("gauge"), "PheValuator"), solidHeader = TRUE, - - shinydashboard::box( - collapsible = TRUE, - collapsed = FALSE, - title = shiny::span( shiny::icon("circle-question"), "Help & Information"), - width = "100%", - shiny::htmlTemplate(system.file("phevaluator-www", "phevaluator.html", package = utils::packageName())) - ), + shinydashboard::box( collapsible = TRUE, @@ -137,7 +130,7 @@ phevaluatorViewer <- function(id) { #' @param id The unique reference id for the module #' @param connectionHandler A connection to the database with the results #' @param resultDatabaseSettings A named list containing the cohort generator results database details (schema, table prefix) -#' +#' @family {PheValuator} #' @return The phevaluator main module server #' #' @export diff --git a/R/report-main.R b/R/report-main.R new file mode 100644 index 00000000..99529e92 --- /dev/null +++ b/R/report-main.R @@ -0,0 +1,1050 @@ +# @file report-main.R +# +# Copyright 2024 Observational Health Data Sciences and Informatics +# +# This file is part of PatientLevelPrediction +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +#' The location of the report module helper file +#' +#' @details +#' Returns the location of the report helper file +#' @family {Report} +#' @return +#' string location of the report helper file +#' +#' @export +reportHelperFile <- function(){ + fileLoc <- system.file('report-www', "report.html", package = "OhdsiShinyModules") + return(fileLoc) +} + +#' The module viewer for the shiny app report module +#' +#' @details +#' The user specifies the id for the module +#' +#' @param id the unique reference id for the module +#' @family {Report} +#' @return +#' The user interface to the home page module +#' +#' @export +reportViewer <- function( + id = 'reportModule' + ) { + ns <- shiny::NS(id) + + shinydashboard::box( + status = 'info', + width = 12, + title = shiny::span( shiny::icon('book'), "Report Generator"), + solidHeader = TRUE, + + + shiny::tabsetPanel( + type = 'hidden',#'pills', + id = ns('mainTab'), + + shiny::tabPanel( + title = 'Select Target', + shiny::uiOutput(ns("targetSelection")) + ), + + shiny::tabPanel( + title = 'Select Cohort Method Target', + shiny::uiOutput(ns("cmTargetSelection")) + ), + + shiny::tabPanel( + title = 'Select Comparator', + shiny::uiOutput(ns("comparatorSelection")) + ), + + shiny::tabPanel( + title = 'Select Outcome', + shiny::uiOutput(ns("outcomeSelection")) + ), + + shiny::tabPanel( + title = 'Generate', + shiny::uiOutput(ns("generateSelection")) + ) + + + ) + + + + + ) +} + +#' The module server for the shiny app report module +#' +#' @details +#' The user specifies the id for the module +#' +#' @param id the unique reference id for the module +#' @param connectionHandler a connection to the database with the results +#' @param resultDatabaseSettings a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix +#' @param server server for the connection to the results for quarto +#' @param username username for the connection to the results for quarto +#' @param password password for the connection to the results for quarto +#' @param dbms dbms for the connection to the results for quarto +#' @family {Report} +#' @return +#' The server for the shiny app home +#' +#' @export +reportServer <- function( + id = 'reportModule', + connectionHandler = NULL, + resultDatabaseSettings = NULL, + server = Sys.getenv("RESULTS_SERVER"), + username = Sys.getenv("RESULTS_USER"), + password = Sys.getenv("RESULTS_PASSWORD"), + dbms = Sys.getenv("RESULTS_DBMS") + ) { + shiny::moduleServer( + id, + function(input, output, session) { + + # get input options + tnos <- getTandOs( + connectionHandler = connectionHandler, + resultDatabaseSettings = resultDatabaseSettings, + includeCohortIncidence = F, # turning off for speed + includeSccs = F # turning off for speed + ) + + ## update input selectors + #============================ + + # Targets + targets <- lapply(tnos$groupedTs, function(x) x$cohortId) + targets <- unlist(targets) + + + # show download button + showDownload <- shiny::reactiveVal(F) + # outcome and comparator data.frames + emptyRow <- data.frame( + id = 0, + name = 'Add', + friendlyName = 'Add' + ) + outcomeDf <- shiny::reactiveVal(emptyRow) + comparatorDf <- shiny::reactiveVal(emptyRow) + + + + output$targetSelection <- shiny::renderUI({ + + shiny::div( + + shiny::fluidRow( + + shiny::column( + width = 12, + shiny::p('First pick a target cohort') + ) + ), + + shiny::fluidRow( + + shiny::column( + width = 6, + shiny::selectInput( + inputId = session$ns('targetId'), + label = 'Target: ', + choices = targets, + selected = 1, + multiple = FALSE, + selectize = TRUE, + width = NULL, + size = NULL + ) + + ), + + shiny::column( + width = 6, + shiny::textInput( + inputId = session$ns('targetName'), + label = 'Friendly target name:', + value = 'Target' + ) + ) + ), + + + shiny::fluidRow( + shiny::column( + width = 10, + shiny::p('') + ), + shiny::column( + width = 2, + shiny::actionButton( + inputId = session$ns('targetNext'), + label = 'Next', + shiny::icon("arrow-right"), + style="color: #fff; background-color: #337ab7; border-color: #2e6da4" + ) + ) + ) + ) + }) + + + cmTargets <- shiny::reactiveVal() + shiny::observeEvent( + input$targetNext, + { + + # change tab to 'Select cmTarget' + shiny::updateTabsetPanel( + session = session, + inputId = 'mainTab', + selected = 'Select Cohort Method Target' + ) + + # get indications for target + if(is.null(input$targetId)){ + return(NULL) + } + subsetTargets <- tnos$groupedTs[[which(unlist(lapply(tnos$groupedTs, function(x) ifelse(is.null(x$cohortId), F, x$cohortId == input$targetId))))]]$subsets + ind <- !is.na(subsetTargets$subsetId) + if(sum(ind)>0){ + cts <- subsetTargets$subsetId[ind] + names(cts) <- subsetTargets$targetName[ind] + } else{ + cts <- '' + names(cts) <- 'No indication' + } + cmTargets(cts) + } + ) + + + output$cmTargetSelection <- shiny::renderUI({ + + shiny::div( + + shiny::fluidRow( + shiny::column( + width = 12, + shiny::p('Now pick the subset used by cohort method (indication and extra inclusions)') + ) + ), + + shiny::fluidRow( + shiny::column( + width = 6, + shiny::selectInput( + inputId = session$ns('cmSubsetId'), + label = 'Pick cohort method target: ', + choices = cmTargets(), + selected = 1, + multiple = FALSE, + selectize = TRUE, + width = NULL, + size = NULL + ) + ), + + shiny::column( + width = 6, + shiny::textInput( + inputId = session$ns('cmTargetName'), + label = 'Friendly indication name: ', + value = 'indication' + ) + ) + ), + + + shiny::fluidRow( + shiny::column( + width = 2, + shiny::actionButton( + inputId = session$ns('cmTargetPrevious'), + label = 'Previous', + shiny::icon("arrow-left"), + style="color: #fff; background-color: #337ab7; border-color: #2e6da4" + ) + ), + shiny::column( + width = 8, + shiny::p('') + ), + shiny::column( + width = 2, + shiny::actionButton( + inputId = session$ns('cmTargetNext'), + label = 'Next', + shiny::icon("arrow-right"), + style="color: #fff; background-color: #337ab7; border-color: #2e6da4" + ) + ) + ) + ) + }) + + comparators <- shiny::reactiveVal() + shiny::observeEvent( + input$cmTargetNext, + { + + # change tab + shiny::updateTabsetPanel( + session = session, + inputId = 'mainTab', + selected = 'Select Comparator' + ) + + if(!is.null(input$cmSubsetId) & !is.null(input$targetId)){ + if(input$cmSubsetId != ''){ + multipler <- ifelse(input$cmSubsetId == 0, 1, 1000) + if(length(which(names(tnos$cs) == as.double(input$targetId)*multipler + as.double(input$cmSubsetId)))>0){ + temp <- tnos$cs[[which(names(tnos$cs) == as.double(input$targetId)*multipler + as.double(input$cmSubsetId))]] + comps <- temp$comparatorId + names(comps) <- temp$comparatorName + comparators(comps) + } else{ + comps <- '' + names(comps) <- 'No Comparator' + comparators(comps) + } + } else{ + shiny::showNotification('No indication available') + } + } + + } + ) + + + shiny::observeEvent( + input$cmTargetPrevious, + { + # change tab + shiny::updateTabsetPanel( + session = session, + inputId = 'mainTab', + selected = 'Select Target' + ) + } + ) + + + output$comparatorSelection <- shiny::renderUI({ + + shiny::div( + + shiny::fluidRow( + shiny::column( + width = 12, + shiny::p('Now pick one or more comparators') + ) + ), + + shiny::fluidRow( + shiny::column( + width = 6, + shiny::selectInput( + inputId = session$ns('comparatorId'), + label = 'Pick comparator: ', + choices = comparators(), + selected = 1, + multiple = FALSE, + selectize = TRUE, + width = NULL, + size = NULL + ) + ), + + shiny::column( + width = 6, + shiny::textInput( + inputId = session$ns('comparatorName'), + label = 'Friendly comparator name: ', + value = 'comparator' + ) + ) + ), + + + shiny::fluidRow( + shiny::column( + width = 2, + shiny::actionButton( + inputId = session$ns('comparatorPrevious'), + label = 'Previous', + shiny::icon("arrow-left"), + style="color: #fff; background-color: #337ab7; border-color: #2e6da4" + ) + ), + shiny::column( + width = 8, + shiny::p('') + ), + shiny::column( + width = 2, + shiny::actionButton( + inputId = session$ns('comparatorNext'), + label = 'Next', + shiny::icon("arrow-right"), + style="color: #fff; background-color: #337ab7; border-color: #2e6da4" + ) + ) + ) + ) + }) + + outcomes <- shiny::reactiveVal() + shiny::observeEvent( + input$comparatorNext, + { + + # change tab to 'Select Outcome' + shiny::updateTabsetPanel( + session = session, + inputId = 'mainTab', + selected = 'Select Outcome' + ) + + if(is.null(input$targetId)){ + return(NULL) + } + + multipler <- ifelse(input$cmSubsetId == 0, 1, 1000) + cmTargetId <- as.double(input$targetId)*multipler + as.double(input$cmSubsetId) + + if(length(which(names(tnos$tos) %in% c(input$targetId, cmTargetId) ))>0){ + temp <- tnos$tos[[which(names(tnos$tos) %in% c(input$targetId, cmTargetId))[1] ]] + os <- temp$outcomeId + names(os) <- temp$outcomeName + outcomes(os) + } else{ + os <- '' + names(os) <- 'None' + outcomes(os) + shiny::showNotification('No Outcomes') + } + } + ) + + shiny::observeEvent( + input$comparatorPrevious, + { + # change tab + shiny::updateTabsetPanel( + session = session, + inputId = 'mainTab', + selected = 'Select Cohort Method Target' + ) + } + ) + + + + output$outcomeSelection <- shiny::renderUI({ + + shiny::div( + + shiny::fluidRow( + shiny::column( + width = 12, + shiny::p('Now pick one or more outcomes') + ) + ), + + shiny::fluidRow( + shiny::column( + width = 6, + shiny::selectInput( + inputId = session$ns('outcomeId'), + label = 'Pick outcome: ', + choices = outcomes(), + selected = 1, + multiple = FALSE, + selectize = TRUE, + width = NULL, + size = NULL + ) + ), + + shiny::column( + width = 6, + shiny::textInput( + inputId = session$ns('outcomeName'), + label = 'Friendly outcome name: ', + value = 'outcome' + ) + ) + ), + + #shiny::fluidRow( + #shiny::column( + # width = 2, + # shiny::actionButton( + # inputId = session$ns('addOutcome'), + # label = 'Add Outcome', + # icon = shiny::icon('plus') + # ) + #), + #shiny::column( + # width = 10, + # reactable::reactable(outcomeDf()) + #) + #), + + shiny::fluidRow( + shiny::column( + width = 2, + shiny::actionButton( + inputId = session$ns('outcomePrevious'), + label = 'Previous', + shiny::icon("arrow-left"), + style="color: #fff; background-color: #337ab7; border-color: #2e6da4" + ) + ), + shiny::column( + width = 8, + shiny::p('') + ), + shiny::column( + width = 2, + shiny::actionButton( + inputId = session$ns('outcomeNext'), + label = 'Next', + shiny::icon("arrow-right"), + style="color: #fff; background-color: #337ab7; border-color: #2e6da4" + ) + ) + ) + + ) + }) + + shiny::observeEvent( + input$outcomeNext, + { + + # change tab to 'Select Outcome' + shiny::updateTabsetPanel( + session = session, + inputId = 'mainTab', + selected = 'Generate' + ) + + output$inputTable <- shiny::renderTable( + + + data.frame( + Input = c('Target','Comparator','Outcome','Indication'), + Id = c(input$targetId, input$comparatorId, input$outcomeId, input$cmSubsetId), + Name = c(input$targetName, input$comparatorName, input$outcomeName, input$cmTargetName), + FullName = c(unlist(lapply( + c(input$targetId, input$comparatorId, input$outcomeId), + function(id){tnos$cg$cohortName[tnos$cg$cohortDefinitionId == id]} + )), 'NA') + ) + ) + + } + ) + + shiny::observeEvent( + input$outcomePrevious, + { + # change tab + shiny::updateTabsetPanel( + session = session, + inputId = 'mainTab', + selected = 'Select Comparator' + ) + } + ) + + + # GENERATE + output$generateSelection <- shiny::renderUI({ + + shiny::div( + + shiny::fluidRow( + shiny::column( + width = 12, + shiny::p('First generate the protocol and then download') + ) + ), + + shiny::fluidRow( + shiny::column( + width = 12, + shiny::p('Selected input review: '), + shiny::tableOutput(outputId = session$ns('inputTable')) + ) + ), + + shiny::fluidRow( + shiny::column( + width = 12, + shiny::p('Add study restriction details: ') + ), + shiny::column( + width = 6, + shiny::dateRangeInput( + inputId = session$ns('dateRestriction'), + label = 'Study date restriction', + start = '1990-01-01' + ) + ), + shiny::column( + width = 6, + shiny::sliderInput( + inputId = session$ns('ageRange'), + label = 'Study age range:', + min = 0, + max = 120, + step = 1, + value = c(18,120), + round = T + ) + ) + ), + + shiny::fluidRow( + shiny::column( + width = 2, + shiny::actionButton( + inputId = session$ns('generatePrevious'), + label = 'Previous', + shiny::icon("arrow-left"), + style="color: #fff; background-color: #337ab7; border-color: #2e6da4" + ) + ), + shiny::column( + width = 5, + shiny::actionButton( + inputId = session$ns("generate"), + label = "Generate", + shiny::icon('circle-plus') + ) + ), + shiny::column( + width = 5, + shiny::uiOutput(session$ns('downloadButton')) + ) + ) + + ) # end div + }) + + shiny::observeEvent( + input$generatePrevious, + { + # change tab + shiny::updateTabsetPanel( + session = session, + inputId = 'mainTab', + selected = 'Select Outcome' + ) + } + ) + + output$downloadButton <- shiny::renderUI( + expr = if(showDownload()) { + shiny::downloadButton( + outputId = session$ns("download"), + label = "Download" + ) + } else { + NULL + }) + + # Modals for outcome and comparator + # model pops up with id and name inputs + # add the outcomeDf() or comparatorDf() + # also add remove button + + # remove outcome/comparator button + + + # Downloadable presentation ---- + shiny::observeEvent( + eventExpr = input$generate, + handlerExpr = { + + shiny::withProgress( + message = 'Cleaning files', value = 0, { + # remove file is exists + if(file.exists(file.path(tempdir(), 'presentation.html'))){ + file.remove(file.path(tempdir(), 'presentation.html')) + showDownload(F) + }; + + shiny::incProgress(0.2, detail = "Generating report") + + ReportGenerator::generatePresentationMultiple( + server = server, + username = username, + password = password, + dbms = dbms, + targetId = as.double(input$targetId), + resultsSchema = resultDatabaseSettings$schema, + subsetId = as.double(input$cmSubsetId), + outcomeIds = as.double(input$outcomeId), + comparatorIds = floor(as.double(input$comparatorId)/1000), # (remove subset), + covariateIds = c( # TODO add this as input? + 316139,320128210,443454210, + 4282096210,441542210 + ), + friendlyNames = list( + targetName = input$targetName, + comparatorNames = input$comparatorName, + indicationName = input$cmTargetName, + outcomeNames = input$outcomeName + ), + details = list( + studyPeriod = paste0(input$dateRestriction, collapse = '-'), + restrictions = paste0("Age - ", paste0(input$ageRange,collapse = '-')) + ), + title = 'Executive Summary Report', # TODO: add title for shiny app here? + lead = 'Shiny App', + date = as.character(Sys.Date()), + outputLocation = tempdir(), + outputName = 'presentation.html' + ) + showDownload(T) + shiny::incProgress(1, detail = "Done") + }) + }) + + output$download <- shiny::downloadHandler( + filename = function() { + paste("presentation-", Sys.Date(), ".html", sep="") + }, + content = function(file){ + if(file.exists(file.path(tempdir(), 'presentation.html'))){ + file.copy( + from = file.path(tempdir(), 'presentation.html'), + to = file + ) + } + } + ) + + + + } + ) +} + + + +getTandOs <- function( + connectionHandler, + resultDatabaseSettings, + includeCharacterization = T, + includeCohortIncidence = T, + includeCohortMethod = T, + includePrediction = T, + includeSccs = T +){ + + # get cohorts + sql <- 'select distinct * from @schema.@cg_table_prefixcohort_definition order by cohort_name;' + cg <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + cg_table_prefix = resultDatabaseSettings$cgTablePrefix + ) + + if(includeCharacterization){ + characterization <- tryCatch( + {nrow(connectionHandler$queryDb( + 'select * from @schema.@c_table_prefixcohort_details limit 1;', + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix + ))>=0}, + error = function(e){return(F)} + ) + } else{ + characterization <- F + } + + if(includeCohortIncidence){ + cohortIncidence <- tryCatch( + {nrow(connectionHandler$queryDb( + 'select * from @schema.@ci_table_prefixincidence_summary limit 1;', + schema = resultDatabaseSettings$schema, + ci_table_prefix = resultDatabaseSettings$incidenceTablePrefix + ))>=0}, + error = function(e){return(F)} + ) + } else{ + cohortIncidence <- F + } + + if(includeCohortMethod){ + cohortMethod <- tryCatch( + {nrow(connectionHandler$queryDb( + 'select * from @schema.@cm_table_prefixtarget_comparator_outcome limit 1;', + schema = resultDatabaseSettings$schema, + cm_table_prefix = resultDatabaseSettings$cmTablePrefix + ))>=0}, + error = function(e){return(F)} + ) + } else{ + cohortMethod <- F + } + + if(includePrediction){ + prediction <- tryCatch( + {nrow(connectionHandler$queryDb( + 'select * from @schema.@plp_table_prefixmodel_designs limit 1;', + schema = resultDatabaseSettings$schema, + plp_table_prefix = resultDatabaseSettings$plpTablePrefix + ))>=0}, + error = function(e){return(F)} + )} else{ + prediction <- F + } + + if(includeSccs){ + sccs <- tryCatch( + {nrow(connectionHandler$queryDb( + 'select * from @schema.@sccs_table_prefixexposures_outcome_set limit 1;', + schema = resultDatabaseSettings$schema, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix + ))>=0}, + error = function(e){return(F)} + )} else{ + sccs <- F + } + + # get T and O pairs + sql <- "select distinct tid, oid from + + ( + + {@characterization} ? { + select distinct TARGET_COHORT_ID as tid, OUTCOME_COHORT_ID as oid + from @schema.@c_table_prefixcohort_details where + TARGET_COHORT_ID != 0 and OUTCOME_COHORT_ID != 0 + + } + + {@cohort_incidence} ? { + {@characterization}?{union} + select distinct TARGET_COHORT_DEFINITION_ID as tid, OUTCOME_COHORT_DEFINITION_ID as oid + from @schema.@ci_table_prefixincidence_summary + + } + + {@cohort_method} ? { + {@cohort_incidence | @characterization}?{union} + select distinct TARGET_ID as tid, OUTCOME_ID as oid + from @schema.@cm_table_prefixtarget_comparator_outcome + where OUTCOME_OF_INTEREST = 1 + + } + + {@prediction} ? { + {@cohort_method | @cohort_incidence | @characterization}?{union} + + select distinct c1.cohort_definition_id as tid, c2.cohort_definition_id as oid + from @schema.@plp_table_prefixmodel_designs md + inner join @schema.@plp_table_prefixcohorts c1 + on c1.cohort_id = md.target_id + inner join @schema.@plp_table_prefixcohorts c2 + on c2.cohort_id = md.outcome_id + } + + {@sccs} ? { + {@cohort_method | @cohort_incidence | @characterization | @prediction}?{union} + + SELECT distinct + cov.era_id as tid, + eos.outcome_id as oid + + FROM @schema.@sccs_table_prefixdiagnostics_summary ds + + inner join + @schema.@sccs_table_prefixexposures_outcome_set eos + on ds.exposures_outcome_set_id = eos.exposures_outcome_set_id + + INNER JOIN + @schema.@sccs_table_prefixcovariate cov + on cov.covariate_id = ds.covariate_id and + cov.exposures_outcome_set_id = ds.exposures_outcome_set_id and + cov.analysis_id = ds.analysis_id and + cov.database_id = ds.database_id + + -- adding code to remove the negative controls + INNER JOIN + @schema.@sccs_table_prefixexposure e + ON e.exposures_outcome_set_id = ds.exposures_outcome_set_id + AND e.era_id = cov.era_id + where e.true_effect_size is NULL + + } + + ) temp_t_o + + ;" + res <- connectionHandler$queryDb( + sql = sql, + schema = resultDatabaseSettings$schema, + c_table_prefix = resultDatabaseSettings$cTablePrefix, + ci_table_prefix = resultDatabaseSettings$incidenceTablePrefix, + cm_table_prefix = resultDatabaseSettings$cmTablePrefix, + plp_table_prefix = resultDatabaseSettings$plpTablePrefix, + sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, + characterization = characterization, + cohort_incidence = cohortIncidence, + cohort_method = cohortMethod, + prediction = prediction, + sccs = sccs + ) + + # add cohort names + res <- merge( + x = res, + y = cg[,c('cohortDefinitionId','cohortName')], + by.x = 'tid', + by.y = 'cohortDefinitionId' + ) %>% + dplyr::rename( + targetName = "cohortName" + ) + + res <- merge( + x = res, + y = cg[,c('cohortDefinitionId','cohortName')], + by.x = 'oid', + by.y = 'cohortDefinitionId' + ) %>% + dplyr::rename( + outcomeName = "cohortName" + ) %>% + dplyr::arrange( + .data$targetName, + .data$outcomeName + ) + + tos <- lapply(unique(res$tid), function(tid){ + data.frame( + outcomeId = res$oid[res$tid == tid], + outcomeName = res$outcomeName[res$tid == tid] + ) + }) + names(tos) <- unique(res$tid) + + # get target heirarchy + groupedCohorts <- lapply(unique(res$tid), function(tid){ + list( + cohortId = tid, + cohortName = unique(res$targetName[res$tid == tid]), + subsets = data.frame( + targetId = tid, + targetName = unique(res$targetName[res$tid == tid]), + subsetId = 0 + ) + ) + }) + names(groupedCohorts) <- unique(res$targetName) + + # if using subsets then do this using the isSubset + if('isSubset' %in% colnames(cg)){ + cg$isSubset[is.na(cg$isSubset)] <- 0 + cg$subsetParent[is.na(cg$subsetParent)] <- cg$cohortDefinitionId + cg$subsetDefinitionId[is.na(cg$subsetDefinitionId)] <- 0 + + if(sum(cg$isSubset == 0) > 0 ){ + # + parentChild <- unique( + merge( + x = cg[, c('cohortDefinitionId','subsetParent')], + y = res, + by.x = 'cohortDefinitionId', + by.y = 'tid' + ) + ) %>% dplyr::arrange( # adding order to make options orders + .data$targetName + ) + parents <- unique(parentChild$subsetParent) + groupedCohorts <- lapply(1:length(parents), function(i){ + x <- parents[i]; + list( + cohortId = x, + cohortName = cg$cohortName[cg$cohortDefinitionId == x], + subsets = data.frame( + targetId = cg$cohortDefinitionId[cg$subsetParent == x], + targetName = cg$cohortName[cg$subsetParent == x], + subsetId = cg$subsetDefinitionId[cg$subsetParent == x] + ) + ); + }) + names(groupedCohorts) <- unlist(lapply(groupedCohorts, function(x){x$cohortName})) + }} + + # get comparators + cs <- NULL + if(cohortMethod){ + comps <- connectionHandler$queryDb( + 'select distinct target_id, comparator_id from + @schema.@cm_table_prefixtarget_comparator_outcome + where outcome_of_interest = 1;', + schema = resultDatabaseSettings$schema, + cm_table_prefix = resultDatabaseSettings$cmTablePrefix + ) + + comps <- merge( + comps,cg[,c('cohortDefinitionId','cohortName')], + by.x = 'comparatorId', + by.y = 'cohortDefinitionId' + ) %>% + dplyr::rename(comparatorName = "cohortName") + + cs <- lapply(unique(comps$targetId), function(tid){ + data.frame( + comparatorName = unique(comps$comparatorName[comps$targetId == tid]), + comparatorId = unique(comps$comparatorId[comps$targetId == tid]) + ) + } + ) + names(cs) <- unique(comps$targetId) + } + + return( + list( + cg = cg, + groupedTs = groupedCohorts, + tos = tos, + cs = cs, + characterization = characterization, + cohortIncidence = cohortIncidence, + cohortMethod = cohortMethod, + prediction = prediction, + sccs = sccs + ) + ) + +} diff --git a/R/sccs-diagnosticsSummary.R b/R/sccs-diagnosticsSummary.R deleted file mode 100644 index ccfe77a3..00000000 --- a/R/sccs-diagnosticsSummary.R +++ /dev/null @@ -1,465 +0,0 @@ -# @file sccs-diagnosticsSummary -# -# Copyright 2024 Observational Health Data Sciences and Informatics -# -# This file is part of OhdsiShinyModules -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -sccsDiagnosticsSummaryViewer <- function(id) { - ns <- shiny::NS(id) - - #shinydashboard::box( - # status = 'info', - # width = '100%', - # title = shiny::span('Diagnostic Results'), - # solidHeader = TRUE, - shiny::div( - shiny::tabsetPanel( - type = 'pills', - id = ns('diagnosticsTablePanel'), - shiny::tabPanel( - title = 'Summary', - resultTableViewer(ns("diagnosticsSummaryTable")) - ), - shiny::tabPanel( - title = 'Full', - resultTableViewer(ns("diagnosticsTable")) - ) - ) - ) - -} - -sccsDiagnosticsSummaryServer <- function( - id, - connectionHandler, - resultDatabaseSettings, - inputSelected -) { - - shiny::moduleServer( - id, - function(input, output, session) { - - - data <- shiny::reactive({ - exposure <- inputSelected()$exposure - - if (is.character(exposure)) { - exposureGroup <- strsplit(exposure, " ")[[1]] - targetId <- exposureGroup[[1]] - indidcationId <- exposureGroup[[2]] - } else { - targetId <- -1 - indidcationId <- -1 - } - - getSccsAllDiagnosticsSummary( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - targetIds = targetId, - outcomeIds = inputSelected()$outcome, - analysisIds = inputSelected()$analysis, - indicationIds = indidcationId - ) - }) - - data2 <- shiny::reactive({ # use CM diag function - diagnosticSummaryFormat( - data = data, - idCols = c('databaseName','target','covariateName'), - namesFrom = c('outcome','analysis') - ) - }) - - customColDefs <- list( - databaseName = reactable::colDef( - header = withTooltip( - "Database", - "The database name" - ) - ), - target = reactable::colDef( - header = withTooltip( - "Target", - "The target cohort of interest " - ) - ), - outcome = reactable::colDef( - header = withTooltip( - "Outcome", - "The outcome of interest " - ) - ), - analysis = reactable::colDef( - header = withTooltip( - "Analysis", - "The analysis name " - ) - ), - covariateName = reactable::colDef( - header = withTooltip( - "Time Period", - "The time period of interest" - ) - ), - mdrr = reactable::colDef( - header = withTooltip( - "mdrr", - "The minimum detectible relative risk" - ) - ), - ease = reactable::colDef( - header = withTooltip( - "ease", - "The ..." - ) - ), - timeTrendP = reactable::colDef( - header = withTooltip( - "timeTrendP", - "The ..." - ) - ), - preExposureP = reactable::colDef( - header = withTooltip( - "preExposureP", - "The ..." - ) - ), - mdrrDiagnostic = reactable::colDef( - header = withTooltip( - "mdrrDiagnostic", - "The ..." - ) - ), - easeDiagnostic = reactable::colDef( - header = withTooltip( - "easeDiagnostic", - "The ..." - ) - ), - timeTrendDiagnostic = reactable::colDef( - header = withTooltip( - "timeTrendDiagnostic", - "The ..." - ) - ), - preExposureDiagnostic = reactable::colDef( - header = withTooltip( - "preExposureDiagnostic", - "The ..." - ) - ), - - unblind = reactable::colDef( - header = withTooltip( - "unblind", - "If the value is 1 then the diagnostics passed and results can be unblinded" - ) - ) - - ) - - resultTableServer( - id = "diagnosticsTable", - df = data, - colDefsInput = customColDefs - ) - - - resultTableServer( - id = "diagnosticsSummaryTable", - df = data2, - colDefsInput = getColDefsSccsDiag( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - ) - - } - ) -} - - - -getSccsDiagAnalyses <- function( - connectionHandler, - resultDatabaseSettings -){ - - sql <- " - SELECT distinct - a.analysis_id, - a.description as analysis - - FROM - @schema.@sccs_table_prefixanalysis a - ; - " - result <- connectionHandler$queryDb( - sql, - schema = resultDatabaseSettings$schema, - sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, - snakeCaseToCamelCase = TRUE - ) - - res <- result$analysisId - names(res) <- result$analysis - - return(res) -} - - -getSccsDiagOutcomes <- function( - connectionHandler, - resultDatabaseSettings -){ - - sql <- " - SELECT distinct - c.cohort_name as outcome, - c.cohort_definition_id - - FROM @schema.@sccs_table_prefixdiagnostics_summary ds - inner join - @schema.@sccs_table_prefixexposures_outcome_set eos - on ds.exposures_outcome_set_id = eos.exposures_outcome_set_id - inner join - @schema.@cg_table_prefixcohort_definition as c - on c.cohort_definition_id = eos.outcome_id - ; - " - result <- connectionHandler$queryDb( - sql, - schema = resultDatabaseSettings$schema, - sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - snakeCaseToCamelCase = TRUE - ) - - res <- result$cohortDefinitionId - names(res) <- result$outcome - - return(res) -} - -getSccsDiagTargets <- function( - connectionHandler, - resultDatabaseSettings -){ - - sql <- " - SELECT distinct - c2.cohort_name as target, - c2.cohort_definition_id - - FROM @schema.@sccs_table_prefixdiagnostics_summary ds - - INNER JOIN - @schema.@sccs_table_prefixcovariate cov - on cov.covariate_id = ds.covariate_id and - cov.exposures_outcome_set_id = ds.exposures_outcome_set_id and - cov.analysis_id = ds.analysis_id and - cov.database_id = ds.database_id - - inner join - @schema.@cg_table_prefixcohort_definition as c2 - on cov.era_id = c2.cohort_definition_id - ; - " - result <- connectionHandler$queryDb( - sql, - schema = resultDatabaseSettings$schema, - sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - snakeCaseToCamelCase = TRUE - ) - - res <- result$cohortDefinitionId - names(res) <- result$target - - return(res) -} - - -getSccsAllDiagnosticsSummary <- function( - connectionHandler, - resultDatabaseSettings, - targetIds, - outcomeIds, - analysisIds = NULL, - indicationIds = NULL -) { - - if(is.null(targetIds) || is.null(outcomeIds)){ - return(NULL) - } - - if (any(indicationIds == -1)) { - indicationIds <- NULL - } - - sql <- " - SELECT - d.cdm_source_abbreviation as database_name, - c.cohort_name as outcome, - c2.cohort_name as target, - a.description as analysis, - cov.covariate_name, - ds.* - FROM @schema.@sccs_table_prefixdiagnostics_summary ds - inner join - @schema.@sccs_table_prefixexposures_outcome_set eos - on ds.exposures_outcome_set_id = eos.exposures_outcome_set_id - inner join - @schema.@cg_table_prefixcohort_definition as c - on c.cohort_definition_id = eos.outcome_id - - INNER JOIN - @schema.@database_table_prefix@database_table d - on d.database_id = ds.database_id - - INNER JOIN - @schema.@sccs_table_prefixanalysis a - on a.analysis_id = ds.analysis_id - - INNER JOIN - @schema.@sccs_table_prefixcovariate cov - on cov.covariate_id = ds.covariate_id and - cov.exposures_outcome_set_id = ds.exposures_outcome_set_id and - cov.analysis_id = ds.analysis_id and - cov.database_id = ds.database_id - - inner join - @schema.@cg_table_prefixcohort_definition as c2 - on cov.era_id = c2.cohort_definition_id - - - where - - c2.cohort_definition_id in (@target_ids) - and c.cohort_definition_id in (@outcome_ids) - {@use_analysis}?{and a.analysis_id in (@analysis_ids)} - {@use_indications} ? {and eos.nesting_cohort_id IN (@indication_ids)} : {and eos.nesting_cohort_id IS NULL} - ; - " - result <- connectionHandler$queryDb( - sql, - schema = resultDatabaseSettings$schema, - cg_table_prefix = resultDatabaseSettings$cgTablePrefix, - sccs_table_prefix = resultDatabaseSettings$sccsTablePrefix, - database_table_prefix = resultDatabaseSettings$databaseTablePrefix, - database_table = resultDatabaseSettings$databaseTable, - - target_ids = paste0(targetIds, collapse = ','), - outcome_ids = paste0(outcomeIds, collapse = ','), - analysis_ids = paste0(analysisIds, collapse = ','), - indication_ids = paste0(indicationIds, collapse = ','), - use_analysis = !is.null(analysisIds), - use_indications = !is.null(indicationIds), - snakeCaseToCamelCase = TRUE - ) - - result <- result %>% - dplyr::select(-c("analysisId","exposuresOutcomeSetId","databaseId","covariateId")) - - result$summaryValue <- apply( - X = result[, grep('Diagnostic', colnames(result))], - MARGIN = 1, - FUN = function(x){ - - if(sum(x %in% c('FAIL'))>0){ - return('Fail') - } else if(sum(x %in% c('WARNING')) >0){ - return(sum(x %in% c('WARNING'), na.rm = T)) - } else{ - return('Pass') - } - } - ) - return(result) - -} - - -getColDefsSccsDiag <- function( - connectionHandler, - resultDatabaseSettings -){ - - fixedColumns = list( - databaseName = reactable::colDef( - header = withTooltip( - "Database", - "The database name" - ), - sticky = "left" - ), - target = reactable::colDef( - header = withTooltip( - "Target", - "The target cohort of interest " - ), - sticky = "left" - ), - covariateName = reactable::colDef( - header = withTooltip( - "Time Period", - "The time period of interest" - ), - sticky = "left" - ) - ) - - outcomes <- getSccsDiagOutcomes( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - analyses <- getSccsDiagAnalyses( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - colnameFormat <- merge(unique(names(outcomes)), unique(names(analyses))) - colnameFormat <- apply(colnameFormat, 1, function(x){paste(x, collapse = '_', sep = '_')}) - - styleList <- lapply( - colnameFormat, - FUN = function(x){ - reactable::colDef( - header = withTooltip( - substring(x,1,40), - x - ), - style = function(value) { - color <- 'orange' - if(is.na(value)){ - color <- 'black' - }else if(value == 'Pass'){ - color <- '#AFE1AF' - }else if(value == 'Fail'){ - color <- '#E97451' - } - list(background = color) - } - ) - } - ) - names(styleList) <- colnameFormat - result <- append(fixedColumns, styleList) - - return(result) -} diff --git a/R/sccs-main.R b/R/sccs-main.R deleted file mode 100644 index 8aab8110..00000000 --- a/R/sccs-main.R +++ /dev/null @@ -1,216 +0,0 @@ -#' SCCS shiny module UI code -#' @description -#' Load the ui for the sccs module -#' @param id id for module -#' @export -sccsView <- function(id = "sccs-module") { - ns <- shiny::NS(id) - tags <- shiny::tags - - shinydashboard::box( - status = 'info', - width = 12, - title = shiny::span( shiny::icon("people-arrows"), 'Self Controlled Case Series'), - solidHeader = TRUE, - - infoHelperViewer( - id = "helper", - helpLocation= system.file("sccs-www", "sccs.html", package = utils::packageName()) - ), - - inputSelectionViewer(ns("input-selection-sccs")), - - shiny::conditionalPanel( - condition = 'input.generate != 0', - ns = shiny::NS(ns("input-selection-sccs")), - - shiny::tabsetPanel( - type = 'pills', - id = ns("mainTabsetPanel"), - - shiny::tabPanel( - title = "Diagnostics", - sccsDiagnosticsSummaryViewer(ns("sccsDiganostics")) - ), - shiny::tabPanel( - title = 'Results', - sccsResultsViewer(ns("sccsResults")), - ) - ) - - ) # end condition - ) -} - -#' Gets input selection box for use with SCCS exposure indication selection -#' @noRd -.getSccsExposureIndicationSelection <- function(connectionHandler, - resultDatabaseSettings) { - migrations <- getMigrations(connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - tablePrefix = resultDatabaseSettings$sccsTablePrefix) - - # Migration_2-v5_1_0.sql - useNestingIndications <- migrations %>% migrationPresent(2) - - if (useNestingIndications) { - # Requires migration in 5.1.0 of cohort generator - expIndicationsTbl <- sccsGetExposureIndications( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - } else { - # Backwards compatability - expIndicationsTbl <- sccsGetExposures( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - } - - expIndicationsTbl <- expIndicationsTbl %>% - dplyr::mutate(exposureIndicationId = paste(.data$exposureId, - .data$indicationId)) - - exposureChoices <- expIndicationsTbl %>% - shinyWidgets::prepare_choices(label = .data$indicationName, - value = .data$exposureIndicationId, - group_by = .data$exposureName, - alias = .data$exposureName) - - namesCallback <- function(inputSelected) { - if (is.null(inputSelected)) - return("") - - vars <- strsplit(inputSelected, " ")[[1]] - - res <- expIndicationsTbl %>% - dplyr::filter(.data$exposureId == vars[[1]], - .data$indicationId == vars[[2]]) %>% - dplyr::select("exposureName", - "indicationName") - - paste(res$exposureName, "\n\t-", res$indicationName) - } - - return( - createInputSetting( - rowNumber = 1, - columnWidth = 12, - varName = 'exposure', - uiFunction = 'shinyWidgets::virtualSelectInput', - updateFunction = "shinyWidgets::updateVirtualSelectInput", - uiInputs = list( - label = 'Target/Indication: ', - choices = exposureChoices, - multiple = FALSE, - search = TRUE, - searchGroup = TRUE, - hasOptionDescription = TRUE, - keepAlwaysOpen = FALSE - ), - namesCallback = namesCallback - ) - ) -} - - -#' The module server for exploring SCCS -#' -#' @details -#' The user specifies the id for the module -#' -#' @param id the unique reference id for the module -#' @param connectionHandler a connection to the database with the results -#' @param resultDatabaseSettings a list containing the prediction result schema and connection details -#' -#' @return -#' The server for the PatientLevelPrediction module -#' -#' @export -sccsServer <- function( - id, - connectionHandler, - resultDatabaseSettings = list(port = 1) -) { - ns <- shiny::NS(id) - - # create functions to result list - outcomes <- sccsGetOutcomes( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - analyses <- sccsGetAnalyses( - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings - ) - - shiny::moduleServer(id, function(input, output, session) { - - inputSettings <- list( - .getSccsExposureIndicationSelection(connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings), - createInputSetting( - rowNumber = 2, - columnWidth = 6, - varName = 'outcome', - uiFunction = 'shinyWidgets::virtualSelectInput', - updateFunction = "shinyWidgets::updateVirtualSelectInput", - uiInputs = list( - label = 'Outcome: ', - choices = outcomes, - selected = outcomes[1], - multiple = F, - search = TRUE - ) - ), - createInputSetting( - rowNumber = 2, - columnWidth = 6, - varName = 'analysis', - uiFunction = 'shinyWidgets::virtualSelectInput', - updateFunction = "shinyWidgets::updateVirtualSelectInput", - uiInputs = list( - label = 'Analysis: ', - choices = analyses, - selected = analyses, - multiple = T - ) - ) - ) - - inputSelected <- inputSelectionServer( - id = "input-selection-sccs", - inputSettingList = inputSettings - ) - - sccsDiagnosticsSummaryServer( - id = "sccsDiganostics", - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - inputSelected = inputSelected - ) - - sccsResultsServer( - id = "sccsResults", - connectionHandler = connectionHandler, - resultDatabaseSettings = resultDatabaseSettings, - inputSelected = inputSelected - ) - }) -} - -#' The location of the description module helper file -#' -#' @details -#' Returns the location of the description helper file -#' -#' @return -#' string location of the description helper file -#' -#' @export -sccsHelperFile <- function() { - fileLoc <- system.file('sccs-www', "sccs.html", package = utils::packageName()) - return(fileLoc) -} diff --git a/README.md b/README.md index 44958a89..62c2dbf2 100644 --- a/README.md +++ b/README.md @@ -12,8 +12,12 @@ The OHDSI tools often provide shiny interfaces for viewing and exploring results Current Modules ======== -- about module: this contains information about the shiny viewer and the types of OHDSI analyses -- prediction module: a module for exploring patient-level prediction results that were developed usign the OHDSI PatientLevelPrediction package +- about module: this contains information about the shiny viewer and the types of OHDSI analyses. +- cohort diagnostics module: a module for exploring CohortDiagnostics results. +- characterization module: a module for exploring Characterization and CohortIncidence results. +- estimation module: a module for exploring CohortMethod, SelfControlledCaseSeries and EvidenceSynthesis results. +- prediction module: a module for exploring patient-level prediction results that were developed usign the OHDSI PatientLevelPrediction package. +- report module: a module that uses ReportGenerator to create a report based on user specified inputs. Technology @@ -40,10 +44,6 @@ User Documentation ================== Documentation can be found on the [package website](https://ohdsi.github.io/OhdsiShinyModules/). -PDF versions of the documentation are also available: -Vignette: [AddingShinyModules.pdf](https://github.com/OHDSI/OhdsiShinyModules/blob/main/inst/doc/AddingShinyModules.pdf) -* Package manual: [OhdsiShinyModules manual](https://raw.githubusercontent.com/OHDSI/OhdsiShinyModules/main/extras/OhdsiShinyModules.pdf) - Support ======= * Developer questions/comments/feedback: OHDSI Forum diff --git a/_pkgdown.yml b/_pkgdown.yml index 67d66644..0521de2f 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -46,133 +46,37 @@ reference: - title: "Prediction module" desc: > Modules for prediction results. - contents: - - patientLevelPredictionHelperFile - - patientLevelPredictionViewer - - patientLevelPredictionServer - - patientLevelPredictionDiagnosticsServer - - patientLevelPredictionDiagnosticsViewer - - patientLevelPredictionDesignSummaryViewer - - patientLevelPredictionDesignSummaryServer - - patientLevelPredictionModelSummaryViewer - - patientLevelPredictionModelSummaryServer - - patientLevelPredictionSettingsViewer - - patientLevelPredictionSettingsServer - - patientLevelPredictionCovariateSummaryViewer - - patientLevelPredictionCovariateSummaryServer - - patientLevelPredictionCutoffViewer - - patientLevelPredictionCutoffServer - - patientLevelPredictionDiscriminationViewer - - patientLevelPredictionDiscriminationServer - - patientLevelPredictionCalibrationViewer - - patientLevelPredictionCalibrationServer - - patientLevelPredictionNbViewer - - patientLevelPredictionNbServer - - patientLevelPredictionValidationViewer - - patientLevelPredictionValidationServer - - title: "Decription module" + contents: has_concept("PatientLevelPrediction") + - title: "Characterization module" desc: > - Modules for the description analyses. - contents: - - characterizationHelperFile - - characterizationViewer - - characterizationServer - - characterizationAggregateFeaturesViewer - - characterizationAggregateFeaturesServer - - characterizationDechallengeRechallengeViewer - - characterizationDechallengeRechallengeServer - - characterizationIncidenceViewer - - characterizationIncidenceServer - - characterizationTableViewer - - characterizationTableServer - - characterizationTimeToEventViewer - - characterizationTimeToEventServer + Modules for the characterization analyses. + contents: has_concept("Characterization") - title: "About module" desc: > Modules for the information page. - contents: - - aboutHelperFile - - aboutViewer - - aboutServer + contents: has_concept("About") - title: "Cohort Generator module" desc: > Modules for the cohort generator package. - contents: - - cohortGeneratorHelperFile - - cohortGeneratorViewer - - cohortGeneratorServer + contents: has_concept("CohortGenerator") - title: "Estimator module" desc: > - Modules for the CohortMethod package. - contents: - - cohortMethodHelperFile - - cohortMethodViewer - - cohortMethodServer - - cohortMethodAttritionViewer - - cohortMethodAttritionServer - - cohortMethodResultSummaryViewer - - cohortMethodResultSummaryServer - - cohortMethodCovariateBalanceViewer - - cohortMethodCovariateBalanceServer - - cohortMethodDiagnosticsSummaryViewer - - cohortMethodDiagnosticsSummaryServer - - cohortMethodKaplanMeierViewer - - cohortMethodKaplanMeierServer - - cohortMethodPopulationCharacteristicsViewer - - cohortMethodPopulationCharacteristicsServer - - cohortMethodPowerViewer - - cohortMethodPowerServer - - cohortMethodPropensityModelViewer - - cohortMethodPropensityModelServer - - cohortMethodPropensityScoreDistViewer - - cohortMethodPropensityScoreDistServer - - cohortMethodSystematicErrorViewer - - cohortMethodSystematicErrorServer - - evidenceSynthesisHelperFile - - evidenceSynthesisServer - - evidenceSynthesisViewer - - sccsHelperFile - - sccsServer - - sccsView + Modules for the CohortMethod, SCCS and Evidence Synthesis packages. + contents: has_concept("Estimation") - title: "Data diagnostics module" desc: > Modules for the DataDiagnostics package. - contents: - - dataDiagnosticDrillServer - - dataDiagnosticDrillViewer - - dataDiagnosticHelperFile - - dataDiagnosticServer - - dataDiagnosticSummaryServer - - dataDiagnosticSummaryViewer - - dataDiagnosticViewer + contents: has_concept("DataDiagnostics") - title: "Cohort Diagnostic module" desc: > Modules for the CohortDiagnostics package. - contents: - - cohortDiagnosticsServer - - cohortDiagnosticsView - - cohortDiagnosticsHelperFile - - cohortDiagCharacterizationView - - cohortCountsModule - - cohortCountsView - - cohortDefinitionsModule - - cohortDefinitionsView - - cohortOverlapView - - compareCohortCharacterizationView - - conceptsInDataSourceView - - createCdDatabaseDataSource - - databaseInformationView - - getCirceRenderedExpression - - getEnabledCdReports - - incidenceRatesView - - indexEventBreakdownView - - orpahanConceptsView - - timeDistributionsView - - visitContextView - - inclusionRulesView + contents: has_concept("CohortDiagnostics") - title: Cohort Diagnostics desc: "Run cohort diagnostics, deploy shiny" contents: has_concept("CohortDiagnostics") + - title: "Report module" + desc: "Report generating module" + contents: has_concept("Report") - title: "PheValuator module" desc: "PheValuator module" contents: has_concept("PheValuator") @@ -181,4 +85,10 @@ reference: contents: has_concept("LargeTables") - title: Utils desc: "Shared usable utility functions" - contents: has_concept("Utils") \ No newline at end of file + contents: has_concept("Utils") + - title: Example + desc: "Connection to example results database" + contents: has_concept("Example") + - title: Home + desc: "Home module functions" + contents: has_concept("Home") \ No newline at end of file diff --git a/docs/404.html b/docs/404.html index 52ada86d..8fa38016 100644 --- a/docs/404.html +++ b/docs/404.html @@ -6,7 +6,7 @@ Page not found (404) • OhdsiShinyModules - + @@ -32,7 +32,7 @@ OhdsiShinyModules - 2.1.5 + 3.0.0 @@ -60,6 +60,30 @@
  • Adding Shiny Modules
    • +
  • + Characterization +
    • +
  • + Cohort Diagnostics +
    • +
  • + Cohort Method (Estimation) +
    • +
  • + Cohorts +
    • +
  • + Data Sources +
    • +
  • + Evidence Synthesis (Meta, Meta Analysis) +
    • +
  • + Prediction +
    • +
  • + Self-Controlled Case Series +
  • @@ -68,7 +92,7 @@
  • @@ -69,7 +93,7 @@
    • - hadesLogo +
    • @@ -94,7 +118,7 @@

      Adding Shiny Modules

      Jenna Reps, Jamie Gilbert, Josh Ide

      -

      2024-07-15

      +

      2024-08-21

      @@ -356,12 +380,12 @@

      Dependencies

      -

      Developed by Jenna Reps.

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

      -

      Site built with pkgdown 2.0.9.

      +

      Site built with pkgdown 2.0.7.

      diff --git a/docs/articles/Characterization.html b/docs/articles/Characterization.html new file mode 100644 index 00000000..6bd0bdc7 --- /dev/null +++ b/docs/articles/Characterization.html @@ -0,0 +1,241 @@ + + + + + + + +Characterization • OhdsiShinyModules + + + + + + + + + + + + +
      +
      + + + + +
      +
      +
      +

      Characterization

      +

      Nathan +Hall

      + +

      2024-08-21

      + + + + +
      + + + + +
      +

      Introduction +

      +

      Characterization, a fundamental aspect of observational health data +research, serves as a cornerstone for understanding and analyzing +populations based on a myriad of characteristics. The methodologies of +characterization play a pivotal role in generating hypotheses about the +determinants of health and disease by providing descriptive insights +into population demographics, medical history, treatment patterns, and +incidence rates of outcomes. There are various methods for +characterization, including database-level characterization, cohort +characterization, treatment pathways analysis, and incidence +measurement. Each of these methods aims to describe populations relative +to an event known as the index date, which anchors the analysis of +baseline, pre-index, and post-index time periods. Through the lens of +use-cases such as disease natural history, treatment utilization, and +quality improvement, characterizing cohorts of patients empowers +researchers to glean actionable insights from observational healthcare +databases.

      +
      +
      +

      Features and Functionalities +

      +

      The Characterization module is dedicated to investigating these +factors within and between cohorts, and it contains several useful +features that allow for this exploration, including:

      +
        +
      1. +Target Viewer: Computes detailed results for target +cohorts, offering a granular view of the cohort’s demographics, +conditions, drug exposures, and more. This enables a deeper +understanding of the cohort’s characteristics at various time points at +or relative to the index date.
        2. +
      2. +Outcome Stratified: For both target and outcome +cohorts, the package calculates binary features during the designated +time at risk. This analysis helps in identifying specific attributes or +exposures that are present or absent in the cohort members, aiding in +the differentiation/comparability between cohorts.
        3. +
      3. +Incidence Rate: Utilizing the CohortIncidence R package, this set of +analyses computes incidence rates for both target and outcome cohorts +during the time at risk selected. This feature is essential for +assessing the frequency of outcomes or conditions within the specified +timeframe, providing a quantitative measure of risk or occurrence. +Incidence measures are provided in both tabular and graphical form, and +can be stratified across calendar year, age, and sex.
        4. +
      4. +Time to Event: Generates plots for the number of +events across different time periods (1, 30, or 365 days) for the +selected target and outcome cohorts. These plots visualize the temporal +distribution of events, allowing researchers to observe patterns over +time and make temporal comparisons between cohorts.
        5. +
      5. +Dechallenge Rechallenge: Offers the ability to +compute dechallenge (withdrawal of a drug or treatment) and rechallenge +(reintroduction) results. This analysis is critical for understanding +the causality between exposures and outcomes, especially in +pharmacovigilance studies and when adverse events following exposure to +a drug may occur.
        6. +
      +
      +
      +

      Utility and Application +

      +

      Characterization serves as a powerful tool for researchers aiming to +dissect and understand the nuances of patient cohorts in observational +health data. Its capabilities allow for the detailed examination of +cohort attributes, the incidence of health outcomes, and the effects of +treatment exposures over time. By facilitating a comprehensive analysis +of target and comparator cohorts, Characterization enables researchers +to draw meaningful conclusions about patient care, treatment efficacy, +and health outcomes, thereby contributing to the advancement of +evidence-based medicine. For more information on the Characterization R +package, please see here.

      +
      +
      + + + +
      + + + +
      +

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

      +
      + +
      +

      +

      Site built with pkgdown 2.0.7.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/articles/CohortDiagnostics.html b/docs/articles/CohortDiagnostics.html new file mode 100644 index 00000000..2eb81c51 --- /dev/null +++ b/docs/articles/CohortDiagnostics.html @@ -0,0 +1,297 @@ + + + + + + + +Cohort Diagnostics • OhdsiShinyModules + + + + + + + + + + + + +
      +
      + + + + +
      +
      +
      +

      Cohort Diagnostics

      +

      Nathan +Hall

      + +

      2024-08-21

      + + + + +
      + + + + +
      +

      Introduction +

      +

      In the realm of observational research, where data heterogeneity and +complexity are common, assessing and diagnosing the characteristics of +cohorts is fundamental to ensuring the reliability and credibility of +research findings. This is also an essential step in phenotype +development. The OHDSI community has developed an R package, +CohortDiagnostics, which provides researchers with a systematic approach +to examine various facets of cohorts, enabling them to identify +potential biases, assess data completeness, and validate the suitability +of cohorts for analysis. This tool is crucial for researchers working +within the Observational Health Data Sciences and Informatics (OHDSI) +ecosystem, enabling them to ensure the accuracy and reliability of +cohort definitions through a detailed examination of incidence rates, +cohort characteristics, and the specific codes triggering cohort +inclusion criteria. CohortDiagnostics streamlines the process of cohort +evaluation by:

      +
        +
      1. Generating a broad spectrum of diagnostics against a CDM database - +see more details here: Features +and Functionalities +
        2. +
      2. Providing an interactive R Shiny application within the package for +an intuitive exploration and visualization of these diagnostics. For +more information on R Shiny, see here.
        3. +
      +
      +
      +

      Features and Functionalities +

      +

      CohortDiagnostics offers a suite of features designed to deepen the +understanding of cohort dynamics and the intricacies of cohort +definitions, including:

      +
        +
      1. +Cohort Definition: Facilitates the examination and +validation of the logic behind cohort definitions, ensuring they +accurately capture the intended population.
        2. +
      2. +Concepts in Data Source: Identifies the specific +concepts present within the data source that are relevant to the cohort +definitions, enabling a deeper understanding of data coverage and +content.
        3. +
      3. +Orphan Concepts: Highlights concepts that, despite +their relevance, are not captured within a cohort’s definition. This +helps in refining concept sets and cohort criteria to ensure +comprehensiveness and relevance.
        4. +
      4. +Cohort Counts: Provides counts of individuals and +records within cohorts, offering a basic measure of cohort size and +scope.
        5. +
      5. +Incidence Rate: Calculates the incidence rate of +cohorts, stratified by various demographic and temporal factors such as +age, sex, and calendar year, to assess the frequency of patients/records +in the cohort and potential patterns over these strata.
        6. +
      6. +Time Distributions: Examines the distribution of +time-related variables within cohorts, such as observation time before +and after cohort index date as well as cohort duration, offering +insights into cohort dynamics over time and available observation +time.
        7. +
      7. +Index Event Breakdown: Breaks down the specific +events that qualify individuals for cohort inclusion, providing clarity +on how inclusion criteria are met.
        8. +
      8. +Visit Context: Analyzes the healthcare context +(e.g., inpatient, outpatient) of the index events, offering insights +into where and how cohort members are identified within the healthcare +system.
        9. +
      9. +Cohort Overlap: Assesses the degree of overlap +between cohorts, which can inform on potential biases, errors, or +redundancies in cohort construction, as well as shared characteristics +between cohorts of patients.
        10. +
      10. +Cohort Characterization: Characterizes cohorts by +detailing prevalent conditions, medication use, procedures, and more, to +understand the clinical profile of cohort members over various time +periods relative to index.
        11. +
      11. +Compare Cohort Characterization: Enables the direct +comparison of characteristics between cohorts, facilitating the +identification of unique or shared features across different cohorts and +across time points.
        12. +
      12. +Meta Data: Provides meta-information about the data +and analyses conducted, ensuring transparency and reproducibility of the +cohort diagnostics process.
        13. +
      +

      Together, these features equip researchers with the tools necessary +for a thorough examination of cohort definitions, enhancing the quality +and reliability of observational health research.

      +
      +
      +

      Utility and Application +

      +

      CohortDiagnostics significantly contributes to the field of +observational health research by providing a robust framework for the +evaluation and validation of cohort definitions. Its utility spans +several critical areas:

      +
        +
      1. +Enhancing Cohort Definition Confidence: By offering +detailed diagnostics, CohortDiagnostics helps researchers refine their +cohort definitions, ensuring they accurately capture the intended +population. This is a critical step in phenotype development, which is a +cornerstone of modern observational health data research.
        2. +
      2. +Identifying Data Quality Issues: Through the +identification of orphan concepts and the detailed breakdown of index +events, researchers can pinpoint data quality issues or gaps in cohort +definitions. Iterating over multiple potential cohort definitions after +analyzing these diagnostics is an encouraged and common practice.
        3. +
      3. +Facilitating the Ideas Behind Comparative Analyses: +The package’s capabilities to characterize and compare cohorts, as well +as to analyze cohort overlaps, are invaluable for researchers looking to +understand the nuances and dynamics of their study populations. These +diagnostics can help inform comparative studies in the future, after the +cohorts and phenotypes are refined and finalized.
        4. +
      4. +Supporting Transparent Research: By enabling the +listing of source codes, data source information, and providing a +platform for detailed diagnostics exploration, CohortDiagnostics fosters +a culture of transparency and reproducibility in observational +research.
        5. +
      +
      +
      + + + +
      + + + +
      +

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

      +
      + +
      +

      +

      Site built with pkgdown 2.0.7.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/articles/CohortMethod.html b/docs/articles/CohortMethod.html new file mode 100644 index 00000000..198d88f6 --- /dev/null +++ b/docs/articles/CohortMethod.html @@ -0,0 +1,274 @@ + + + + + + + +Cohort Method (Estimation) • OhdsiShinyModules + + + + + + + + + + + + +
      +
      + + + + +
      +
      +
      +

      Cohort Method (Estimation)

      +

      Nathan +Hall

      + +

      2024-08-21

      + + + + +
      + + + + +
      +

      Introduction +

      +

      Observational healthcare data, comprising administrative claims and +electronic health records, present a rich source for generating +real-world evidence pertinent to treatment effects that directly impact +patient well-being. Within this realm, population-level effect +estimation assumes a pivotal role, focusing on elucidating the average +causal effects of exposures—such as medical interventions like drug +exposures or procedures—on specific health outcomes of interest. +Population-level effect estimation delves into two primary realms: +direct effect estimation and comparative effect estimation. In direct +effect estimation, the focus lies on discerning the effect of an +exposure on the risk of an outcome compared to no exposure, while +comparative effect estimation aims to delineate the effect of a target +exposure against a comparator exposure. By contrasting factual outcomes +with counterfactual scenarios—what happened versus what would have +occurred under different circumstances—these estimation tasks offer +critical insights into treatment selection, safety surveillance, and +comparative effectiveness. Whether probing individual hypotheses or +exploring multiple hypotheses concurrently, the overarching goal remains +consistent: to derive high-quality estimates of causal effects from the +intricate fabric of observational healthcare data.

      +
      +
      +

      Features and Functionalities +

      +

      The CohortMethod R package, a cornerstone of +population-level estimation within the OHDSI framework, offers a robust +methodology for conducting comparative effectiveness research and +pharmacoepidemiology studies. Some of the features offered by conducting +population-level effect estimation using the CohortMethod module +are:

      +
        +
      1. +Data Extraction: Extracts necessary data from +databases structured in the OMOP Common Data Model (CDM) format, +ensuring uniformity and compatibility across diverse healthcare +settings.
        2. +
      2. +Covariate Selection: Utilizing a comprehensive set +of covariates, including drugs, diagnoses, procedures, age, and +comorbidity indexes, CohortMethod constructs propensity and outcome +models tailored to specific research questions.
        3. +
      3. +Large-Scale Regularized Regression: Employing +large-scale regularized regression techniques, CohortMethod fits +propensity and outcome models with precision and efficiency, +accommodating the complexities of real-world healthcare data.
        4. +
      4. +Propensity Score Adjustment: Facilitates propensity +score adjustment through trimming, stratification, matching, and +weighting, enabling researchers to address confounding and balance +covariate distributions across treatment groups. Results are viewable +both graphically and in tabular form to assess the model.
        5. +
      5. +Diagnostic Functions: Diagnostic functions within +CohortMethod offer insights into propensity score distributions and +covariate balance before and after matching or trimming, enhancing +transparency and robustness in estimation procedures.
        6. +
      6. +Supported Outcome Models: Supported outcome models +include (conditional) logistic regression, (conditional) Poisson +regression, and (conditional) Cox regression, providing flexibility in +modeling various types of outcomes in observational health data +research.
        7. +
      7. +Power: Incorporates power analysis techniques to +estimate the statistical power of the study design, aiding in sample +size determination and study planning, and provides a minimum-detectable +relative risk (MDRR) statistic.
        8. +
      8. +Attrition: Assesses attrition rates within cohorts, +providing insights into potential biases introduced by data loss during +the study period, and provides a visualization of attrition across +various cohort criteria.
        9. +
      9. +Population Characteristics: Analyzes population +characteristics to understand the demographic and clinical makeup of the +study cohorts, informing interpretation of estimation results both +before and after propensity score matching.
        10. +
      10. +Covariate Balance: Visually monitors covariate +balance before and after matching or trimming, ensuring that confounding +variables are adequately controlled for in the analysis.
        11. +
      11. +Systematic Error: Assesses effect size estimates +for negative controls (true hazard ratio = 1) and positive controls +(true hazard ratio > 1) both before and after calibration. Estimates +below the diagonal dashed lines are statistically significant (alpha = +0.05) different from the true effect size. A well-calibrated estimator +should have the true effect size within the 95 percent confidence +interval 95 percent of times, providing researchers with confidence in +the reliability of the estimation process and the accuracy of the +obtained results.
        12. +
      +
      +
      +

      Utility and Application +

      +

      Comparative Effectiveness Research: CohortMethod +empowers researchers to conduct comparative effectiveness studies by +estimating treatment effects while accounting for potential confounding +factors and bias inherent in observational data.

      +

      Pharmacoepidemiology and Drug Safety Studies: In +pharmacoepidemiology research, CohortMethod facilitates the evaluation +of drug safety and effectiveness by quantifying the association between +drug exposures and clinical outcomes in real-world populations.

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      + + + +
      +

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

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      + + + + + + + + diff --git a/docs/articles/Cohorts.html b/docs/articles/Cohorts.html new file mode 100644 index 00000000..459af9eb --- /dev/null +++ b/docs/articles/Cohorts.html @@ -0,0 +1,255 @@ + + + + + + + +Cohorts • OhdsiShinyModules + + + + + + + + + + + + +
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      + + + + +
      +
      +
      +

      Cohorts

      +

      Nathan +Hall

      + +

      2024-08-21

      + + + + +
      + + + + +
      +

      Introduction +

      +

      Cohort identification within real-world evidence (RWE) analyses of +data adhering to the Observational Medical Outcomes Partnership Common +Data Model (OMOP CDM) is a foundational step in understanding healthcare +outcomes and treatment effects. Observational health data, sourced from +diverse sources including Electronic Health Records (EHRs), health +insurance claims, registries, and patient-generated data, offer valuable +insights into patient health status and healthcare delivery. However, +these data were not originally collected for research purposes, leading +to the need for sophisticated methods to infer relevant clinical +information for research objectives. This summary explores the +importance of cohort identification, the methods endorsed by the +Observational Health Data Sciences and Informatics (OHDSI) community, +and the tools available for creating cohorts within OMOP CDM +datasets.

      +
      +
      +

      Importance of Cohort Identification +

      +

      Cohort identification is crucial for conducting meaningful analyses +in healthcare research. A cohort, defined as a set of individuals +meeting one or more inclusion criteria over a duration of time, serves +as the basis for studying healthcare outcomes, treatment effects, and +disease epidemiology. In the context of OHDSI research, cohorts are +fundamental building blocks used for executing research questions, +developing phenotypes, and conducting comparative effectiveness studies. +OHDSI’s approach emphasizes the independence and reusability of cohort +definitions, allowing researchers to define cohorts tailored to specific +research questions while ensuring consistency and reproducibility across +studies.

      +
      +
      +

      Methods for Cohort Identification +

      +

      OHDSI endorses standardized methods for cohort definition, ensuring +transparency, and reproducibility in research. Two main approaches are +employed for constructing cohorts:

      +
      +

      Rule-based Cohort Definitions +

      +

      Rule-based cohort definitions rely on explicitly stated inclusion +criteria to define cohort membership. These criteria are typically based +on domain expertise and clinical knowledge, allowing researchers to +specify cohort attributes such as clinical conditions, procedures, +medications, and temporal relationships. OHDSI provides standardized +components for assembling these criteria, including domains, concept +sets, domain-specific attributes, and temporal logic.

      +
      +
      +

      Probabilistic Cohort Definitions +

      +

      Probabilistic cohort definitions leverage machine learning techniques +to compute the probability of cohort inclusion based on patient +characteristics and clinical events. These models are trained on example +data to automatically identify relevant patient characteristics +predictive of cohort membership. The resulting probabilities can be used +to classify patients into cohorts or as inputs for certain study +designs.

      +
      +
      +
      +

      Tools for Cohort Creation & Identification +

      +

      OHDSI offers a suite of open-source tools to support cohort +identification within OMOP CDM datasets, such as:

      +
        +
      1. +ATLAS: A +web-based tool that enables researchers to define, execute, and share +cohort definitions using standardized terminologies and criteria. ATLAS +streamlines the cohort creation process by providing an intuitive +interface for specifying cohort criteria and visualizing cohort +characteristics.
        2. +
      2. +SQL: Structured Query Language (SQL) provides a +powerful means for defining cohorts through custom queries. OHDSI +encourages the use of SQL for advanced cohort definition tasks and +complex analyses.
        3. +
      3. +CohortGenerator: The CohortGenerator R +package is a tool within the Observational Health Data Sciences and +Informatics (OHDSI) ecosystem designed to facilitate the creation of +cohorts from observational healthcare data stored in databases adhering +to the OMOP Common Data Model (CDM). It is an R Package that streamlines +the cohort creation process, allowing for efficient and reproducible +cohort identification across different datasets.
        4. +
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      + + + +
      +

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

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      + + + + + + + + diff --git a/docs/articles/DataSources.html b/docs/articles/DataSources.html new file mode 100644 index 00000000..eefc7a4d --- /dev/null +++ b/docs/articles/DataSources.html @@ -0,0 +1,251 @@ + + + + + + + +Data Sources • OhdsiShinyModules + + + + + + + + + + + + +
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      +
      +
      +

      Data Sources

      +

      Nathan +Hall

      + +

      2024-08-21

      + + + + +
      + + + + +
      +

      Introduction +

      +

      Real-world data (RWD) encompasses a vast array of information +collected from a variety of sources outside traditional clinical +research settings. This data, when analyzed, generates real-world +evidence (RWE) that can inform healthcare decisions, policy-making, and +patient care.

      +

      The Observational Medical Outcomes Partnership (OMOP) was a +collaborative initiative chaired by the U.S. Food and Drug +Administration (FDA) aimed at improving the understanding of how +observational studies can contribute to evaluating the safety and +effectiveness of medications. It focused on developing methods, tools, +and standards to enhance the quality and reliability of real-world +evidence generated from healthcare data. The OMOP’s legacy continues to +influence current research methodologies, particularly through the +adoption and evolution of its Common Data Model (CDM) in projects like +those undertaken by the Observational Health Data Sciences and +Informatics (OHDSI) community.

      +

      The OMOP CDM standardizes observational healthcare data, enabling the +harmonization of disparate data sources into a consistent format. This +standardization encompasses structured data schemas, controlled +vocabularies for medical terminologies, and unique identifiers for +clinical concepts, ensuring that data from varied healthcare systems and +studies can be compared and analyzed collectively. By transforming +diverse datasets into a unified model, the OMOP CDM facilitates robust, +scalable, and reproducible research across the global healthcare +research community, enhancing the reliability and validity of findings +derived from real-world data.

      +

      The Extract-Transform-Load (ETL) process is a critical data +management procedure that prepares raw data for analysis by extracting +it from various sources, transforming it into a consistent format that +aligns with analysis requirements, and loading it into a destination +system for use. In the context of generating real-world evidence (RWE) +and ensuring adherence to a Common Data Model (CDM) like OMOP, the ETL +process is indispensable. It enables the standardization of +observational healthcare data—often fragmented across different systems +and formats—into a unified structure. This standardization is essential +not only for the interoperability of data across different healthcare +databases but also for ensuring that analyses conducted on these data +are reliable, scalable, and reproducible. For more information on the +ETL process and how OHDSI tools can be utilizied to perform it for a +particular data source, please visit this page.

      +
      +
      +

      Key Data Sources for RWE Analyses +

      +

      Real-world data sources that adhere to the OMOP CDM are varied and +rich, including, but not limited to:

      +
        +
      1. Electronic Health Records (EHRs): These are one of the primary +sources of RWD, containing detailed patient health information from +clinical encounters.
        2. +
      2. Claims and Billing Data: These data sources provide information on +healthcare utilization, including diagnoses, procedures, and medication +prescriptions, primarily for billing purposes.
        3. +
      3. Patient Registries: Disease or condition-specific registries offer +valuable insights into patient outcomes, treatment patterns, and disease +epidemiology.
        4. +
      4. Wearable Devices and Mobile Health Applications: An emerging source +of RWD, these technologies capture real-time data on patient health and +activity levels.
        5. +
      +
      +
      +

      Example OMOP-mapped Datasets +

      +

      The sensitivity of observational health data underscores the need for +robust privacy protection measures due to the potential identification +of individuals and the disclosure of sensitive health information. This +sensitivity arises from the richness of health data, including +diagnoses, treatments, and demographic details, which can reveal +personal health conditions and behaviors. Ensuring privacy in +observational health data is paramount to maintain trust and compliance +with regulations such as the Health Insurance Portability and +Accountability Act (HIPAA). Despite these challenges, several publicly +available data sources mapped to the OMOP CDM offer valuable insights +for research while safeguarding patient privacy. Some examples +include:

      +
        +
      1. HowOften
        2. +
      2. Public +Datasets from OHDSI
        3. +
      3. SynPUFs
        4. +
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      +

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

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      + + + + + + + + diff --git a/docs/articles/EvidenceSynthesis.html b/docs/articles/EvidenceSynthesis.html new file mode 100644 index 00000000..82680aa2 --- /dev/null +++ b/docs/articles/EvidenceSynthesis.html @@ -0,0 +1,238 @@ + + + + + + + +Evidence Synthesis (Meta, Meta Analysis) • OhdsiShinyModules + + + + + + + + + + + + +
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      +
      +
      +

      Evidence Synthesis (Meta, Meta Analysis)

      +

      Nathan +Hall

      + +

      2024-08-21

      + + + + +
      + + + + +
      +

      Introduction +

      +

      Meta-analysis plays a pivotal role in healthcare research by enabling +the synthesis of findings from multiple studies to draw more +generalizable conclusions. In the context of distributed health data +networks, where data are spread across various sites with diverse +populations and practices, synthesizing evidence becomes both a +challenge and a necessity. The EvidenceSynthesis R package addresses +these challenges head-on. It offers a suite of tools designed for +combining causal effect estimates and study diagnostics from multiple +data sites, all while adhering to stringent patient privacy requirements +and navigating the complexities inherent to observational data. This +approach enhances the robustness of meta-analytical conclusions and +extends the utility of distributed health data for research +purposes.

      +
      +
      +

      Features and Functionalities +

      +

      The Meta module which utilizes the EvidenceSynthesis R package makes +use of the following features to summarize the results of a study:

      +
        +
      1. +Meta-Analysis Methods: Facilitates both traditional +fixed-effects and random-effects meta-analyses, accommodating studies +with different degrees of between-site or between-database +variability.
        2. +
      2. +Forest Plot Generation: Provides capabilities for +creating forest plots, visual summaries that illustrate the effects +estimated by individual studies, their confidence intervals, and the +synthesized overall effect.
        3. +
      3. +Non-Normal Likelihood Approximations: Utilizes +non-normal approximations for the per-data-site likelihood function to +reduce bias in scenarios with small or zero counts, a frequent issue in +distributed research environments.
        4. +
      +

      The syntheses are generated for both Cohort Method and +Self-Controlled Case Series estimation results from the study, providing +both information on the diagnostic results within each database and the +visualized and tabular results of the meta analysis.

      +
      +
      +

      Utility and Application +

      +

      The EvidenceSynthesis package is instrumental in synthesizing +evidence from observational studies across multiple healthcare +databases. Its significance is underscored in scenarios characterized +by:

      +

      Comparative Effectiveness Research: Synthesizing +evidence from disparate sources allows for stronger, more reliable +comparisons of treatment outcomes, enriching the foundation for clinical +decision-making.

      +

      Safety Surveillance: Aggregated safety data across +databases enhance the detection and understanding of adverse drug +reactions, contributing to safer patient care.

      +

      Policy and Clinical Guidelines Development: +Meta-analytical findings informed by comprehensive, real-world data can +guide policy formulation and the updating of clinical guidelines, +ensuring they are grounded in broad-based evidence.

      +

      Addressing Challenges of Small Sample Sizes: The +EvidenceSynthesis package notably advances the field by tackling the +issue of small sample sizes and zero event counts, which traditional +meta-analytical methods often handle poorly. Its innovative use of +non-normal likelihood approximations enables more precise effect size +estimation under such conditions, ensuring that the insights derived +from meta-analyses are both accurate and meaningful. This attribute is +particularly beneficial in distributed health data networks, where +individual site/database data may be limited but collectively hold +significant informational value.

      +
      +
      + + + +
      + + + +
      +

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

      +
      + +
      +

      +

      Site built with pkgdown 2.0.7.

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      +
      + + + + + + + + diff --git a/docs/articles/Prediction.html b/docs/articles/Prediction.html new file mode 100644 index 00000000..dfae24ff --- /dev/null +++ b/docs/articles/Prediction.html @@ -0,0 +1,241 @@ + + + + + + + +Prediction • OhdsiShinyModules + + + + + + + + + + + + +
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      +
      +
      +

      Prediction

      +

      Nathan +Hall

      + +

      2024-08-21

      + + + + +
      + + + + +
      +

      Introduction +

      +

      Patient-level prediction stands as a pivotal component in clinical +decision-making, providing clinicians with tools to anticipate +diagnostic or prognostic outcomes based on individual patient +characteristics. In the complex landscape of modern medicine, where +patients generate extensive digital footprints through Electronic Health +Records (EHRs), the ability to harness this wealth of data for +predictive modeling holds immense potential. However, despite the +growing interest in predictive modeling, challenges such as model +reproducibility, validation, and transparency persist. The Observational +Health Data Science and Informatics (OHDSI) framework, with its Common +Data Model (CDM) and standardized methodologies, addresses these +challenges by enabling the development and validation of predictive +models at scale, facilitating external validation across diverse +healthcare settings globally. The OHDSI PatientLevelPrediction R package +encapsulates established best practices for model development and +validation.

      +
      +
      +

      Features and Functionalities +

      +

      The Prediction module is dedicated to investigating prediction models +tailored to specific prediction problems through a combination of +machine learning algorithms and feature engineering techniques. In this +module, users are able to explore model design summaries, detailed +information about the models fitted, model diagnostics, and a detailed +report including performance characteristic results, model +discrimination results, and calibration results.

      +

      The full list of features which are explorable in the Prediction +module are as follows:

      +
        +

      • Takes one or more target cohorts (Ts) and one or more outcome +cohorts (Os) and develops and validates models for all T and O +combinations.

        • +

      • Allows for multiple prediction design options.

        • +

      • Extracts the necessary data from a database in OMOP Common Data +Model format for multiple covariate settings.

        • +

      • Uses a large set of covariates including for example all drugs, +diagnoses, procedures, as well as age, comorbidity indexes, and custom +covariates.

        • +

      • Allows you to add custom covariates or cohort +covariates.

        • +

      • Includes a large number of state-of-the-art machine learning +algorithms that can be used to develop predictive models, including +Regularized logistic regression, Random forest, Gradient boosting +machines, Decision tree, Naive Bayes, K-nearest neighbours, Neural +network, AdaBoost and Support vector machines.

        • +

      • Allows you to add custom algorithms.

        • +

      • Allows you to add custom feature engineering

        • +

      • Allows you to add custom under/over sampling (or any other +sampling) [note: based on existing research this is not +recommended]

        • +

      • Contains functionality to externally validate models.

        • +

      • Includes functions to plot and explore model performance (ROC + +Calibration).

        • +

      • Build ensemble models using +EnsemblePatientLevelPrediction.

        • +

      • Build Deep Learning models using +DeepPatientLevelPrediction.

        • +

      • Generates learning curves.

        • +
      +
      +
      +

      Utility and Application +

      +

      Patient-level prediction within the OHDSI framework represents a +paradigm shift in clinical decision support, offering the potential to +transform patient care through personalized medicine. By leveraging +standardized data structures, rigorous methodologies, and transparent +reporting practices, patient-level prediction endeavors in OHDSI strive +to bridge the gap between predictive modeling research and clinical +practice, ultimately enhancing patient outcomes and advancing +evidence-based healthcare.

      +
      +
      + + + +
      + + + +
      +

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

      +
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      +

      +

      Site built with pkgdown 2.0.7.

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      + + + + + + + + diff --git a/docs/articles/SelfControlledCaseSeries.html b/docs/articles/SelfControlledCaseSeries.html new file mode 100644 index 00000000..af9cbe2a --- /dev/null +++ b/docs/articles/SelfControlledCaseSeries.html @@ -0,0 +1,323 @@ + + + + + + + +Self-Controlled Case Series • OhdsiShinyModules + + + + + + + + + + + + +
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      +
      +
      +

      Self-Controlled Case Series

      +

      Nathan +Hall

      + +

      2024-08-21

      + + + + +
      + + + + +
      +

      Introduction +

      +

      The Self-Controlled Case Series (SCCS) method offers a nuanced +approach to investigating the relationship between exposures and +outcomes within individual patients over time. SCCS designs are +particularly adept at comparing the rate of outcomes during times of +exposure to rates during periods of non-exposure, including before, +between, and after exposure episodes. By leveraging a Poisson regression +that is conditioned on the individual, the SCCS design inherently +addresses the question: “Given that a patient has the outcome, is the +outcome more likely to occur during exposed time compared to non-exposed +time?” The design choices outlined in the method are pivotal for +defining an SCCS question, with each choice playing a critical role in +the study’s design and outcomes:

      +

      Target Cohort: This represents the treatment under +investigation. Outcome Cohort: This cohort signifies +the outcome of interest. Time-at-Risk: Identifies the +specific times when the risk of the outcome is considered, often +relative to the start and end dates of the target cohort. +Model: Defines the statistical model used to estimate +the effect, including adjustments for time-varying confounders if +necessary.

      +

      One of the SCCS design’s strengths is its robustness to confounding +by factors that differ between individuals, as each participant serves +as their own control. However, it remains sensitive to time-varying +confounding factors. To mitigate this, adjustments can be made for +factors such as age, seasonality, and calendar time, enhancing the +model’s accuracy.

      +

      An advanced variant of the SCCS also considers all other drug +exposures recorded in the database, significantly expanding the model’s +variables. This approach employs L1-regularization, with +cross-validation used to select the regularization hyperparameter for +all exposures except the one of interest.

      +

      An important assumption of the SCCS is that the observation period’s +end is independent of the outcome date. This may not hold true for +outcomes that can be fatal, such as stroke. To address this, extensions +to the SCCS model have been developed that correct for any dependency +between the observation period end and the outcome.

      +
      +
      +

      Features and Functionalities +

      +

      The SelfControlledCaseSeries R package allows the user to +perform SCCS analyses in an observational database in the OMOP Common +Data Model. Some of the features offered by the SCCS module include:

      +
        +
      1. **Data Extraction: Extracts necessary data from databases structured +in the OMOP Common Data Model (CDM) format, ensuring uniformity and +compatibility across diverse healthcare settings.
        2. +
      2. +Seasonality Adjustment: Offers the option to adjust +for seasonality effects using a spline function, enhancing the model’s +accuracy by accounting for seasonal variation in exposure and outcome +rates.
        3. +
      3. +Age Adjustment: Provides the option to incorporate +age adjustments using a spline function, allowing for more nuanced +analyses that consider the impact of age on the exposure-outcome +relationship.
        4. +
      4. +Calendar Time Adjustment: Enables the inclusion of +calendar time adjustments using a spline function, addressing potential +temporal trends in the data that could confound the exposure-outcome +relationship.
        5. +
      5. +Event-dependent Censoring Correction: Features the +ability to correct for event-dependent censoring of the observation +period, ensuring that the end of the observation period is appropriately +handled, especially in cases where it might be related to the +outcome.
        6. +
      6. +Comprehensive Covariate Inclusion: Allows for the +addition of a wide array of covariates in one analysis, such as all +recorded drug exposures, facilitating a thorough examination of +potential confounders and effect modifiers.
        7. +
      7. +Risk Window Customization: Supports the +construction of various types of covariates and risk windows, including +pre-exposure windows, to capture contra-indications and other relevant +temporal patterns related to exposure and outcome.
        8. +
      8. +Regularization of Covariates: Applies +regularization to all covariates except the outcome of interest, +employing techniques like L1-regularization with cross-validation for +selecting the regularization hyperparameter, thereby preventing +overfitting and enhancing model reliability.
        9. +
      9. +Self-Controlled Risk Interval Design: Incorporates +the self-controlled risk interval design as a specific application of +the SCCS method, offering additional methodological flexibility for +studying short-term effects of exposures.
        10. +
      10. +Power: Incorporates power analysis techniques to +estimate the statistical power of the study design, aiding in sample +size determination and study planning, and provides a minimum-detectable +relative risk (MDRR) statistic.
        11. +
      11. +Attrition: Assesses attrition rates within cohorts, +providing insights into potential biases introduced by data loss during +the study period, and provides a visualization of attrition across +various cohort criteria.
        12. +
      12. +Spanning: Analyzes the number of subjects observed +for 3 consecutive months, providing insights into the cohort’s +consistency and stability over time.
        13. +
      13. +Time Trend: Assesses the ratio of observed to +expected outcomes per month, with adjustments for calendar time, +seasonality, and/or age as specified in the model, to examine time +trends in the data.
        14. +
      14. +Time to Event: Evaluates the number of events and +subjects observed per week relative to the start of the first exposure, +offering critical insights into the temporal relationship between +exposure and outcome.
        15. +
      15. +Event-dependent Observation: Provides histograms +for the time between the first occurrence of the outcome and the end of +observation, stratified by censored and uncensored ends of observation, +to assess the impact of event-dependent observation periods.
        16. +
      16. +Systematic Error: Assesses effect size estimates +for negative controls (true hazard ratio = 1) and positive controls +(true hazard ratio > 1) both before and after calibration. Estimates +below the diagonal dashed lines are statistically significant (alpha = +0.05) different from the true effect size. A well-calibrated estimator +should have the true effect size within the 95 percent confidence +interval 95 percent of times, providing researchers with confidence in +the reliability of the estimation process and the accuracy of the +obtained results.
        17. +
      +
      +
      +

      Utility and Application +

      +

      The SCCS method is particularly applicable in several key areas of +epidemiological research and pharmacovigilance:

      +

      Drug Safety Surveillance: The SCCS method is widely +used in drug safety surveillance to identify adverse effects of +medications post-marketing. It is well-suited to detect short-term risks +associated with drug exposures, especially where the onset of the +adverse event is expected to be temporally close to the exposure.

      +

      Vaccine Safety Evaluation: The SCCS design is ideal +for assessing the safety of vaccines, especially in evaluating the risk +of adverse events following immunization. Its self-controlled nature +helps to address concerns about confounding by indication and other +biases that can affect observational studies in vaccine safety.

      +

      Comparative Effectiveness Research: While primarily +designed for evaluating the safety of medical interventions, the SCCS +method can also be adapted to compare the effectiveness of different +treatments or interventions within the same individual over time, +particularly for acute conditions.

      +

      Epidemiological Research: More broadly, the SCCS +method is used in epidemiological research to study the temporal +relationships between exposures and outcomes, offering insights into the +causality and mechanisms underlying health conditions and diseases.

      +
      +
      + + + +
      + + + +
      +

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

      +
      + +
      +

      +

      Site built with pkgdown 2.0.7.

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      +
      + + + + + + + + diff --git a/docs/articles/index.html b/docs/articles/index.html index aa4101af..27331065 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -1,5 +1,5 @@ -Articles • OhdsiShinyModulesArticles • OhdsiShinyModules @@ -17,7 +17,7 @@ OhdsiShinyModules - 2.1.5 + 3.0.0 @@ -43,12 +43,36 @@

    • Changelog
  • @@ -69,7 +93,7 @@
    • - hadesLogo +
    • @@ -102,8 +126,12 @@

      Current Modules

        -
      • about module: this contains information about the shiny viewer and the types of OHDSI analyses
        • -
      • prediction module: a module for exploring patient-level prediction results that were developed usign the OHDSI PatientLevelPrediction package
        • +
      • about module: this contains information about the shiny viewer and the types of OHDSI analyses.
        • +
      • cohort diagnostics module: a module for exploring CohortDiagnostics results.
        • +
      • characterization module: a module for exploring Characterization and CohortIncidence results.
        • +
      • estimation module: a module for exploring CohortMethod, SelfControlledCaseSeries and EvidenceSynthesis results.
        • +
      • prediction module: a module for exploring patient-level prediction results that were developed usign the OHDSI PatientLevelPrediction package.
        • +
      • report module: a module that uses ReportGenerator to create a report based on user specified inputs.
      @@ -130,7 +158,6 @@

      InstallationUser Documentation

      Documentation can be found on the package website.

      -

      PDF versions of the documentation are also available: Vignette: AddingShinyModules.pdf * Package manual: OhdsiShinyModules manual

      Support @@ -196,7 +223,9 @@

      Citation

      Developers

        -
      • Jenna Reps
        Maintainer
        • +
      • Jenna Reps
        Author, maintainer
        • +
      • Nathan Hall
        Author
        • +
      • Jamie Gibert
        Author
      @@ -208,12 +237,12 @@

      Developers

      -

      Developed by Jenna Reps.

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

      -

      Site built with pkgdown 2.0.9.

      +

      Site built with pkgdown 2.0.7.

      diff --git a/docs/news/index.html b/docs/news/index.html index b2ad7c14..96e40983 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -1,5 +1,5 @@ -Changelog • OhdsiShinyModulesChangelog • OhdsiShinyModules @@ -17,7 +17,7 @@ OhdsiShinyModules - 2.1.5 + 3.0.0
      @@ -43,12 +43,36 @@
    • Changelog
    • - hadesLogo +
    • @@ -69,9 +93,20 @@

      Changelog

      +
      +

      OhdsiShinyModules v2.2.1

      +
      • Fixed issue in DatabaseConnector check for pooled connections of sqlite databases on cohort diagnotiscs load (from main hotfix)
        • +
      +
      +

      OhdsiShinyModules v2.2.0

      +
      • Combined cohort method, sccs and evidence synthesis into one estimation module with shared target and outcome ids
        • +
      • Characterizations now share the target id
        • +
      • Updated tests to get them all working
        • +
      • Cleaned R check (but cohort incidence still has many notes)
        • +

      OhdsiShinyModules v2.1.5

      -

      Fixed issue in DatabaseConnector check for pooled connections of sqlite databases on cohort diagnotiscs load

      +

      Fixed bug of orphan concepts report not displaying Fixed bug in orphan concepts where negative database ids were causing dynamic sql queries to crash

      OhdsiShinyModules v2.1.4

      @@ -157,11 +192,11 @@

      OhdsiS
      -

      Developed by Jenna Reps.

      +

      Developed by Jenna Reps, Nathan Hall, Jamie Gibert.

      -

      Site built with pkgdown 2.0.9.

      +

      Site built with pkgdown 2.0.7.

      diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index a8323449..f32fb1e2 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,7 +1,15 @@ -pandoc: '3.2' -pkgdown: 2.0.9 +pandoc: 3.1.11 +pkgdown: 2.0.7 pkgdown_sha: ~ articles: AddingShinyModules: AddingShinyModules.html -last_built: 2024-07-15T14:49Z + Characterization: Characterization.html + CohortDiagnostics: CohortDiagnostics.html + CohortMethod: CohortMethod.html + Cohorts: Cohorts.html + DataSources: DataSources.html + EvidenceSynthesis: EvidenceSynthesis.html + Prediction: Prediction.html + SelfControlledCaseSeries: SelfControlledCaseSeries.html +last_built: 2024-08-22T00:49Z diff --git a/docs/reference/LargeDataTable.html b/docs/reference/LargeDataTable.html index bcea8f5c..b2dcf1db 100644 --- a/docs/reference/LargeDataTable.html +++ b/docs/reference/LargeDataTable.html @@ -1,5 +1,5 @@ -Large Data Table — LargeDataTable • OhdsiShinyModulesLarge Data Table — LargeDataTable • OhdsiShinyModules OhdsiShinyModules - 2.1.5 + 3.0.0 @@ -50,12 +50,36 @@
    • Changelog
    \ No newline at end of file + + +

    For more information, please visit https://github.com/OHDSI/CohortGenerator

    \ No newline at end of file diff --git a/inst/cohort-method-www/cohort-method.html b/inst/cohort-method-www/cohort-method.html deleted file mode 100644 index 72e455ca..00000000 --- a/inst/cohort-method-www/cohort-method.html +++ /dev/null @@ -1,3 +0,0 @@ -

    Description

    -

    Information about the study and links to the code used to run the study

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Group,The age group category +genderId,Gender ID,The gender ID +genderName,Gender,The gender category +startYear,Start Year,The year of Time At Risk start +personsAtRiskPe,Persons At Risk PE,The distinct persons at risk before removing excluded time +personsAtRisk,Persons At Risk,The distinct persons at risk +personDaysPe,Person Days PE,The total time at risk before removing excluded time (days) +personDays,Person Days,The total time at risk (days) +personOutcomesPe,Person Outcomes PE,The distinct persons with outcomes before removing excluded time +personOutcomes,Person Outcomes,The distinct persons with outcomes +outcomesPe,Outcomes PE,The number of outcomes before removing excluded time +outcomes,Outcomes,The number of ouctomes +incidenceProportionP100p,IP per 100P,The incidence proportion (per 100 people) = 100 * personOutcomes/personsAtRisk +incidenceRateP100py,IR per 100P,The incidence rate (per 100 person years) = 100 * outcomes/personDays/365.25 +tar,Time-At-Risk,The time-at-risk window (in days) +targetNameShort,Target Short Name,The short name of the target +outcomeNameShort,Outcome Short Name,The short name of the outcome diff --git a/inst/components-columnInformation/characterization-incidence-colDefs.json b/inst/components-columnInformation/characterization-incidence-colDefs.json deleted file mode 100644 index a47fde20..00000000 --- a/inst/components-columnInformation/characterization-incidence-colDefs.json +++ /dev/null @@ -1,1649 +0,0 @@ -{ - "cdmSourceAbbreviation": { - "name": "cdmSourceAbbreviation", - "header": { - "name": "div", - "attribs": { - "style": "text-decoration: underline; text-decoration-style: dotted; cursor: help" - }, - "children": [ - { - "x": { - "opts": { - "content": "The name of the database" - }, - "text": "Database" - }, - "width": null, - "height": null, - "sizingPolicy": { - "defaultWidth": null, - "defaultHeight": null, - "padding": null, - "fill": null, - "viewer": { - "defaultWidth": null, - "defaultHeight": 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"children": [ + { + "x": { + "opts": { + "content": "Absolute Value of the Standardized Mean Difference After Matching" + }, + "text": "Abs Val StdDiff After Matching" + }, + "width": null, + "height": null, + "sizingPolicy": { + "defaultWidth": null, + "defaultHeight": null, + "padding": null, + "fill": null, + "viewer": { + "defaultWidth": null, + "defaultHeight": null, + "padding": null, + "fill": true, + "suppress": false, + "paneHeight": null + }, + "browser": { + "defaultWidth": null, + "defaultHeight": null, + "padding": null, + "fill": false, + "external": false + }, + "knitr": { + "defaultWidth": null, + "defaultHeight": null, + "figure": true + } + }, + "dependencies": null, + "elementId": null, + "preRenderHook": null, + "jsHooks": [], + "attr_class": ["tippy", "htmlwidget"], + "attr_package": "tippy" + } + ], + "attr_class": "shiny.tag" + }, + "attr_class": "colDef" + } +} diff --git a/inst/datasources-www/datasources.html b/inst/datasources-www/datasources.html index 9dcb6d6d..a3e63a73 100644 --- a/inst/datasources-www/datasources.html +++ b/inst/datasources-www/datasources.html @@ -1,15 +1 @@ -

    Below are the descriptions of each column in the Data Sources Module:

    -
      -
    • cdmSourceName
      • -
    • cdmSourceAbbreviation
      • -
    • cdmHolder
      • -
    • sourceDescription
      • -
    • sourceDocumentationReference
      • -
    • cdmEtlReference
      • -
    • sourceReleaseDate
      • -
    • cdmReleaseDate
      • -
    • cdmVersion
      • -
    • vocabularyVersion
      • -
    • databaseId
      • -
    • maxObsPeriodEndDate
      • -
    \ No newline at end of file +

    This module gives an overview of the data sources used in this study.

    diff --git a/inst/estimation-www/estimation.html b/inst/estimation-www/estimation.html new file mode 100644 index 00000000..819bfae2 --- /dev/null +++ b/inst/estimation-www/estimation.html @@ -0,0 +1,28 @@ +

    Description

    +

    CohortMethod is an R package for performing new-user cohort studies in an observational database in the OMOP Common Data Model. This module: + +

      +
    • Uses a large set of covariates for both the propensity and outcome model, including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc.
      • +
    • Performs large scale regularized regression to fit the propensity and outcome models.
      • +
    • Includes function for trimming, stratifying, matching, and weighting on propensity scores.
      • +
    • Includes diagnostic functions, including propensity score distribution plots and plots showing covariate balance before and after matching and/or trimming.
      • +
    • Supports the following outcome models: (conditional) logistic regression, (conditional) Poisson regression, and (conditional) Cox regression.
      • +
    + +

    For more information, please visit https://github.com/OHDSI/CohortMethod

    + + +

    Self Controlled Case Series

    +

    SelfControlledCaseSeries is an R package for performing Self-Controlled Case Series (SCCS) analyses in an observational database in the OMOP Common Data Model. This module:

    + +
      +
    • Extracts the necessary data from a database in OMOP Common Data Model format.
      • +
    • Optionally adds seasonality, age, and/or calendar time using spline functions.
      • +
    • Optionally corrects for event-dependent censoring of the observation period.
      • +
    • Optionally add many covariates in one analysis (e.g. all drugs).
      • +
    • Has options for constructing different types of covariates and risk windows, including pre-exposure windows (to capture contra-indications).
      • +
    • Optionally uses regularization on all covariates except the outcome of interest.
      • +
    • Provides the self-controlled risk interval design as a special case of the SCCS.
      • +
    + +

    For more information, please visit https://github.com/OHDSI/SelfControlledCaseSeries

    \ No newline at end of file diff --git a/inst/evidence-synthesis-www/evidence-synthesis.html b/inst/evidence-synthesis-www/evidence-synthesis.html index db8e9289..15a91285 100644 --- a/inst/evidence-synthesis-www/evidence-synthesis.html +++ b/inst/evidence-synthesis-www/evidence-synthesis.html @@ -1,4 +1,6 @@

    Evidence Synthesis

    - Shows all the estimates and meta analysis results -

    \ No newline at end of file + This R package contains routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study. This includes functions for performing meta-analysis and forest plots. +

    + +

    For more information, please visit https://github.com/OHDSI/EvidenceSynthesis

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zN`(I;`urjlFmcr&z;Jx}B}L?@;(q!L6Q&SXp#+r=i-0PxG+M2+)TSQ|dHun;@wZtR5s&a&1^_ZW>E~*dPJ#dqw`w%_clb>m0M33-! zJ~bD6ImipbRPg~N(F1&nLlqwrme?N6ZP~KV!5r1cb_cF;BnGmq-DHmHNgKomnb=s_OJsCCgcoDcleTG;LhKJ}~ojOdiY`>ZBy+Zj{&0ny19dAlS;Ynm`baJ&^8 z%BMfn5|a5YN5ut-)o93(m?$B!!E-qB$X>cIL_S8emq!z10wO5%HAN7?oV0Qee1Az4 zN@Zsufcn=5kp~%BMW`PUDOcrDv_Xnh5g%$2g-oV`qi<21p`1b{R9`4K?iA$}$}zOP zWa4Kj+iiM0Sw>Oe`Bh(~cs1&MglL8*iY zD)3F4*~j5=g@^*zUcw}59h7Z|cJujn5|fCRw-G$C*e?FSlZ$BExrp2Lh -

    add info here

    \ No newline at end of file +

    PatientLevelPrediction is an R package for building and validating patient-level predictive models using data in the OMOP Common Data Model format.

    +

    For more information, please visit https://github.com/OHDSI/PatientLevelPrediction

    \ No newline at end of file diff --git a/inst/phevaluator-www/phevaluator.html b/inst/phevaluator-www/phevaluator.html index e1425a92..42b07a4f 100644 --- a/inst/phevaluator-www/phevaluator.html +++ b/inst/phevaluator-www/phevaluator.html @@ -1,4 +1,6 @@ -

    Below are the descriptions of each tab in the PheValuator Module, which will appear after clicking the "Generate Results" button below:

    +

    The goal of PheValuator is to produce a large cohort of subjects each with a predicted probability for a specified health outcome of interest (HOI). This is achieved by developing a diagnostic predictive model for the HOI using the PatientLevelPrediction (PLP) R package and applying the model to a large, randomly selected population. These subjects can be used to test one or more phenotype algorithms.

    + +

    Below are the descriptions of relevant fields in the PheValuator Module.

    • Phenotypes – phenotypes, with SQL and JSON code, examined using PheValuator.
    • Phenotype Performance Characteristics – estimates for sensitivity, specificity, and positive and negative predictive value for phenotype algorithms tested by PheValuator.
      • @@ -10,4 +12,6 @@
    • Evaluation Cohort Parameters - R function parameters used in PheValuator to develop the evaluation cohort used to determine phenotype algorithm performance.
    • Test Subjects – examples of test subjects evaluated to be false positive or negative and true positive or negative.
    • Test Subjects Covariates – examples of the associated covariates included in the diagnostic predictive model for test subjects evaluated to be false positive or negative and true positive or negative.
      • -
    \ No newline at end of file + + +

    For more information, please visit https://github.com/OHDSI/PheValuator

    \ No newline at end of file diff --git a/inst/report-www/report.html b/inst/report-www/report.html new file mode 100644 index 00000000..c2f5883a --- /dev/null +++ b/inst/report-www/report.html @@ -0,0 +1 @@ +

    Use this module to create an anlysis summary presentation for one target cohort, multiple comparators and multiple outcomes.

    \ No newline at end of file diff --git a/inst/sccs-www/sccs.html b/inst/sccs-www/sccs.html deleted file mode 100644 index 44373c91..00000000 --- a/inst/sccs-www/sccs.html +++ /dev/null @@ -1,2 +0,0 @@ -

    Self Controlled Case Series

    -

    Information

    diff --git a/man/OhdsiShinyModules.Rd b/man/OhdsiShinyModules.Rd index fe505b2c..21722b2a 100644 --- a/man/OhdsiShinyModules.Rd +++ b/man/OhdsiShinyModules.Rd @@ -8,3 +8,14 @@ \description{ A selection of shiny modules for exploring standardized OHDSI results } +\author{ +\strong{Maintainer}: Jenna Reps \email{jreps@its.jnj.com} + +Authors: +\itemize{ + \item Nathan Hall + \item Jamie Gibert +} + +} +\keyword{internal} diff --git a/man/aboutHelperFile.Rd b/man/aboutHelperFile.Rd index 38ef9865..8d4af628 100644 --- a/man/aboutHelperFile.Rd +++ b/man/aboutHelperFile.Rd @@ -15,3 +15,9 @@ The location of the about module helper file \details{ Returns the location of the about helper file } +\seealso{ +Other {About}: +\code{\link{aboutServer}()}, +\code{\link{aboutViewer}()} +} +\concept{{About}} diff --git a/man/aboutServer.Rd b/man/aboutServer.Rd index 6f767497..b891648b 100644 --- a/man/aboutServer.Rd +++ b/man/aboutServer.Rd @@ -7,7 +7,8 @@ aboutServer( id = "homepage", connectionHandler = NULL, - resultDatabaseSettings = NULL + resultDatabaseSettings = NULL, + config ) } \arguments{ @@ -16,6 +17,8 @@ aboutServer( \item{connectionHandler}{a connection to the database with the results} \item{resultDatabaseSettings}{a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix} + +\item{config}{the config from the app.R file that contains a list of which modules to include} } \value{ The server for the shiny app home @@ -26,3 +29,9 @@ The module server for the shiny app home \details{ The user specifies the id for the module } +\seealso{ +Other {About}: +\code{\link{aboutHelperFile}()}, +\code{\link{aboutViewer}()} +} +\concept{{About}} diff --git a/man/aboutViewer.Rd b/man/aboutViewer.Rd index 7fd25085..7a1cd8eb 100644 --- a/man/aboutViewer.Rd +++ b/man/aboutViewer.Rd @@ -18,3 +18,9 @@ The module viewer for the shiny app home \details{ The user specifies the id for the module } +\seealso{ +Other {About}: +\code{\link{aboutHelperFile}()}, +\code{\link{aboutServer}()} +} +\concept{{About}} diff --git a/man/characterizationAggregateFeaturesServer.Rd b/man/characterizationAggregateFeaturesServer.Rd deleted file mode 100644 index 37c27cbd..00000000 --- a/man/characterizationAggregateFeaturesServer.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/characterization-aggregateFeatures.R -\name{characterizationAggregateFeaturesServer} -\alias{characterizationAggregateFeaturesServer} -\title{The module server for exploring aggregate features results} -\usage{ -characterizationAggregateFeaturesServer( - id, - connectionHandler, - resultDatabaseSettings -) -} -\arguments{ -\item{id}{the unique reference id for the module} - -\item{connectionHandler}{the connection to the prediction result database} - -\item{resultDatabaseSettings}{a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix} -} -\value{ -The server to the description aggregate features module -} -\description{ -The module server for exploring aggregate features results -} -\details{ -The user specifies the id for the module -} diff --git a/man/characterizationAggregateFeaturesViewer.Rd b/man/characterizationAggregateFeaturesViewer.Rd deleted file mode 100644 index 75b82348..00000000 --- a/man/characterizationAggregateFeaturesViewer.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/characterization-aggregateFeatures.R -\name{characterizationAggregateFeaturesViewer} -\alias{characterizationAggregateFeaturesViewer} -\title{The module viewer for exploring aggregate feature results} -\usage{ -characterizationAggregateFeaturesViewer(id) -} -\arguments{ -\item{id}{the unique reference id for the module} -} -\value{ -The user interface to the description aggregate feature module -} -\description{ -The module viewer for exploring aggregate feature results -} -\details{ -The user specifies the id for the module -} diff --git a/man/characterizationDechallengeRechallengeServer.Rd b/man/characterizationDechallengeRechallengeServer.Rd deleted file mode 100644 index 0dc4a02f..00000000 --- a/man/characterizationDechallengeRechallengeServer.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/characterization-dechallengeRechallenge.R -\name{characterizationDechallengeRechallengeServer} -\alias{characterizationDechallengeRechallengeServer} -\title{The module server for exploring Dechallenge Rechallenge results} -\usage{ -characterizationDechallengeRechallengeServer( - id, - connectionHandler, - resultDatabaseSettings -) -} -\arguments{ -\item{id}{the unique reference id for the module} - -\item{connectionHandler}{the connection to the prediction result database} - -\item{resultDatabaseSettings}{a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix} -} -\value{ -The server to the Dechallenge Rechallenge module -} -\description{ -The module server for exploring Dechallenge Rechallenge results -} -\details{ -The user specifies the id for the module -} diff --git a/man/characterizationDechallengeRechallengeViewer.Rd b/man/characterizationDechallengeRechallengeViewer.Rd deleted file mode 100644 index f937ae7e..00000000 --- a/man/characterizationDechallengeRechallengeViewer.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/characterization-dechallengeRechallenge.R -\name{characterizationDechallengeRechallengeViewer} -\alias{characterizationDechallengeRechallengeViewer} -\title{The module viewer for exploring Dechallenge Rechallenge results} -\usage{ -characterizationDechallengeRechallengeViewer(id) -} -\arguments{ -\item{id}{the unique reference id for the module} -} -\value{ -The user interface to the description Dechallenge Rechallenge module -} -\description{ -The module viewer for exploring Dechallenge Rechallenge results -} -\details{ -The user specifies the id for the module -} diff --git a/man/characterizationHelperFile.Rd b/man/characterizationHelperFile.Rd index 67cd8f45..912d8343 100644 --- a/man/characterizationHelperFile.Rd +++ b/man/characterizationHelperFile.Rd @@ -15,3 +15,11 @@ The location of the characterization module helper file \details{ Returns the location of the characterization helper file } +\seealso{ +Other {Characterization}: +\code{\link{characterizationIncidenceServer}()}, +\code{\link{characterizationIncidenceViewer}()}, +\code{\link{characterizationServer}()}, +\code{\link{characterizationViewer}()} +} +\concept{{Characterization}} diff --git a/man/characterizationIncidenceServer.Rd b/man/characterizationIncidenceServer.Rd index 42dd31cc..713ffc5a 100644 --- a/man/characterizationIncidenceServer.Rd +++ b/man/characterizationIncidenceServer.Rd @@ -4,7 +4,15 @@ \alias{characterizationIncidenceServer} \title{The module server for exploring incidence results} \usage{ -characterizationIncidenceServer(id, connectionHandler, resultDatabaseSettings) +characterizationIncidenceServer( + id, + connectionHandler, + resultDatabaseSettings, + parents, + parentIndex, + outcomes, + targetIds +) } \arguments{ \item{id}{the unique reference id for the module} @@ -12,6 +20,14 @@ characterizationIncidenceServer(id, connectionHandler, resultDatabaseSettings) \item{connectionHandler}{the connection to the prediction result database} \item{resultDatabaseSettings}{a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix} + +\item{parents}{a list of parent cohorts} + +\item{parentIndex}{an integer specifying the parent index of interest} + +\item{outcomes}{a reactive object specifying the outcomes of interest} + +\item{targetIds}{a reactive vector of integer specifying the targetIds of interest} } \value{ The server to the prediction incidence module @@ -22,3 +38,11 @@ The module server for exploring incidence results \details{ The user specifies the id for the module } +\seealso{ +Other {Characterization}: +\code{\link{characterizationHelperFile}()}, +\code{\link{characterizationIncidenceViewer}()}, +\code{\link{characterizationServer}()}, +\code{\link{characterizationViewer}()} +} +\concept{{Characterization}} diff --git a/man/characterizationIncidenceViewer.Rd b/man/characterizationIncidenceViewer.Rd index 8bb718f7..84d0253f 100644 --- a/man/characterizationIncidenceViewer.Rd +++ b/man/characterizationIncidenceViewer.Rd @@ -18,3 +18,11 @@ The module viewer for exploring incidence results \details{ The user specifies the id for the module } +\seealso{ +Other {Characterization}: +\code{\link{characterizationHelperFile}()}, +\code{\link{characterizationIncidenceServer}()}, +\code{\link{characterizationServer}()}, +\code{\link{characterizationViewer}()} +} +\concept{{Characterization}} diff --git a/man/characterizationServer.Rd b/man/characterizationServer.Rd index b85af663..a21b66b5 100644 --- a/man/characterizationServer.Rd +++ b/man/characterizationServer.Rd @@ -26,3 +26,11 @@ The module server for exploring characterization studies \details{ The user specifies the id for the module } +\seealso{ +Other {Characterization}: +\code{\link{characterizationHelperFile}()}, +\code{\link{characterizationIncidenceServer}()}, +\code{\link{characterizationIncidenceViewer}()}, +\code{\link{characterizationViewer}()} +} +\concept{{Characterization}} diff --git a/man/characterizationTableServer.Rd b/man/characterizationTableServer.Rd deleted file mode 100644 index d92445bb..00000000 --- a/man/characterizationTableServer.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/characterization-cohorts.R -\name{characterizationTableServer} -\alias{characterizationTableServer} -\title{The module server for exploring 1 or more cohorts features} -\usage{ -characterizationTableServer(id, connectionHandler, resultDatabaseSettings) -} -\arguments{ -\item{id}{the unique reference id for the module} - -\item{connectionHandler}{the connection to the prediction result database} - -\item{resultDatabaseSettings}{a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix} -} -\value{ -The server to the cohorts features server -} -\description{ -The module server for exploring 1 or more cohorts features -} -\details{ -The user specifies the id for the module -} diff --git a/man/characterizationTableViewer.Rd b/man/characterizationTableViewer.Rd deleted file mode 100644 index b82f445c..00000000 --- a/man/characterizationTableViewer.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/characterization-cohorts.R -\name{characterizationTableViewer} -\alias{characterizationTableViewer} -\title{The module viewer for exploring 1 or more cohorts features} -\usage{ -characterizationTableViewer(id) -} -\arguments{ -\item{id}{the unique reference id for the module} -} -\value{ -The user interface to the description cohorts features -} -\description{ -The module viewer for exploring 1 or more cohorts features -} -\details{ -The user specifies the id for the module -} diff --git a/man/characterizationTimeToEventServer.Rd b/man/characterizationTimeToEventServer.Rd deleted file mode 100644 index 638499bd..00000000 --- a/man/characterizationTimeToEventServer.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/characterization-timeToEvent.R -\name{characterizationTimeToEventServer} -\alias{characterizationTimeToEventServer} -\title{The module server for exploring time to event results} -\usage{ -characterizationTimeToEventServer( - id, - connectionHandler, - resultDatabaseSettings -) -} -\arguments{ -\item{id}{the unique reference id for the module} - -\item{connectionHandler}{the connection to the prediction result database} - -\item{resultDatabaseSettings}{a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix} -} -\value{ -The server to the prediction time to event module -} -\description{ -The module server for exploring time to event results -} -\details{ -The user specifies the id for the module -} diff --git a/man/characterizationTimeToEventViewer.Rd b/man/characterizationTimeToEventViewer.Rd deleted file mode 100644 index f5626ffc..00000000 --- a/man/characterizationTimeToEventViewer.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/characterization-timeToEvent.R -\name{characterizationTimeToEventViewer} -\alias{characterizationTimeToEventViewer} -\title{The module viewer for exploring time to event results} -\usage{ -characterizationTimeToEventViewer(id) -} -\arguments{ -\item{id}{the unique reference id for the module} -} -\value{ -The user interface to the characterization time to event module -} -\description{ -The module viewer for exploring time to event results -} -\details{ -The user specifies the id for the module -} diff --git a/man/characterizationViewer.Rd b/man/characterizationViewer.Rd index 641ee31c..460de01a 100644 --- a/man/characterizationViewer.Rd +++ b/man/characterizationViewer.Rd @@ -18,3 +18,11 @@ The module viewer for exploring characterization studies \details{ The user specifies the id for the module } +\seealso{ +Other {Characterization}: +\code{\link{characterizationHelperFile}()}, +\code{\link{characterizationIncidenceServer}()}, +\code{\link{characterizationIncidenceViewer}()}, +\code{\link{characterizationServer}()} +} +\concept{{Characterization}} diff --git a/man/cohortCountsModule.Rd b/man/cohortCountsModule.Rd index 8c4cce1f..84eca181 100644 --- a/man/cohortCountsModule.Rd +++ b/man/cohortCountsModule.Rd @@ -32,3 +32,27 @@ cohortCountsModule( \description{ Shiny module for cohort counts. Displays reactable table of cohort counts } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/cohortCountsView.Rd b/man/cohortCountsView.Rd index 5d3a0977..d1245326 100644 --- a/man/cohortCountsView.Rd +++ b/man/cohortCountsView.Rd @@ -12,3 +12,27 @@ cohortCountsView(id) \description{ Shiny view for cohort counts module } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/cohortDefinitionsModule.Rd b/man/cohortDefinitionsModule.Rd index 87a60709..d9da5ce8 100644 --- a/man/cohortDefinitionsModule.Rd +++ b/man/cohortDefinitionsModule.Rd @@ -29,3 +29,27 @@ cohortDefinitionsModule( \description{ cohort defintion conceptsets, json etc } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/cohortDefinitionsView.Rd b/man/cohortDefinitionsView.Rd index 44c6eb84..387e60c0 100644 --- a/man/cohortDefinitionsView.Rd +++ b/man/cohortDefinitionsView.Rd @@ -12,3 +12,27 @@ cohortDefinitionsView(id) \description{ Outputs cohort definitions } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/cohortDiagCharacterizationView.Rd b/man/cohortDiagCharacterizationView.Rd index 171c47b1..f83473c0 100644 --- a/man/cohortDiagCharacterizationView.Rd +++ b/man/cohortDiagCharacterizationView.Rd @@ -12,3 +12,27 @@ cohortDiagCharacterizationView(id) \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/cohortDiagnosticsHelperFile.Rd b/man/cohortDiagnosticsHelperFile.Rd index 5a4eda49..5ac9f07f 100644 --- a/man/cohortDiagnosticsHelperFile.Rd +++ b/man/cohortDiagnosticsHelperFile.Rd @@ -17,7 +17,25 @@ Returns the location of the description helper file } \seealso{ Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, \code{\link{cohortDiagnosticsServer}()}, -\code{\link{orpahanConceptsView}()} +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} } \concept{{CohortDiagnostics}} diff --git a/man/cohortDiagnosticsServer.Rd b/man/cohortDiagnosticsServer.Rd index 73e1512e..ac6eb4ec 100644 --- a/man/cohortDiagnosticsServer.Rd +++ b/man/cohortDiagnosticsServer.Rd @@ -25,7 +25,25 @@ Cohort Diagnostics Explorer main module } \seealso{ Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, \code{\link{cohortDiagnosticsHelperFile}()}, -\code{\link{orpahanConceptsView}()} +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} } \concept{{CohortDiagnostics}} diff --git a/man/cohortDiagnosticsView.Rd b/man/cohortDiagnosticsView.Rd index ef34d0bc..9dff57de 100644 --- a/man/cohortDiagnosticsView.Rd +++ b/man/cohortDiagnosticsView.Rd @@ -18,3 +18,27 @@ View for cohort diagnostics module \details{ The user specifies the id for the module } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/cohortGeneratorHelperFile.Rd b/man/cohortGeneratorHelperFile.Rd index cfb9d71f..5b4ee1fe 100644 --- a/man/cohortGeneratorHelperFile.Rd +++ b/man/cohortGeneratorHelperFile.Rd @@ -15,3 +15,9 @@ The location of the cohort-generator module helper file \details{ Returns the location of the cohort-generator helper file } +\seealso{ +Other {CohortGenerator}: +\code{\link{cohortGeneratorServer}()}, +\code{\link{cohortGeneratorViewer}()} +} +\concept{{CohortGenerator}} diff --git a/man/cohortGeneratorServer.Rd b/man/cohortGeneratorServer.Rd index 792a984f..022c3d6f 100644 --- a/man/cohortGeneratorServer.Rd +++ b/man/cohortGeneratorServer.Rd @@ -19,3 +19,9 @@ the cohort generator results viewer main module server \description{ The module server for the main cohort generator module } +\seealso{ +Other {CohortGenerator}: +\code{\link{cohortGeneratorHelperFile}()}, +\code{\link{cohortGeneratorViewer}()} +} +\concept{{CohortGenerator}} diff --git a/man/cohortGeneratorViewer.Rd b/man/cohortGeneratorViewer.Rd index ed2776df..ee251fb4 100644 --- a/man/cohortGeneratorViewer.Rd +++ b/man/cohortGeneratorViewer.Rd @@ -15,3 +15,9 @@ The user interface to the cohort generator results viewer \description{ The viewer of the main cohort generator module } +\seealso{ +Other {CohortGenerator}: +\code{\link{cohortGeneratorHelperFile}()}, +\code{\link{cohortGeneratorServer}()} +} +\concept{{CohortGenerator}} diff --git a/man/cohortMethodAttritionServer.Rd b/man/cohortMethodAttritionServer.Rd index 6e3444cc..a1f96a9c 100644 --- a/man/cohortMethodAttritionServer.Rd +++ b/man/cohortMethodAttritionServer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-attrition.R +% Please edit documentation in R/estimation-cohort-method-attrition.R \name{cohortMethodAttritionServer} \alias{cohortMethodAttritionServer} \title{The module server for rendering the PLE attrition results} @@ -26,3 +26,25 @@ the PLE attrition results content server \description{ The module server for rendering the PLE attrition results } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodAttritionViewer.Rd b/man/cohortMethodAttritionViewer.Rd index 9b16769e..d2eb00eb 100644 --- a/man/cohortMethodAttritionViewer.Rd +++ b/man/cohortMethodAttritionViewer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-attrition.R +% Please edit documentation in R/estimation-cohort-method-attrition.R \name{cohortMethodAttritionViewer} \alias{cohortMethodAttritionViewer} \title{The module viewer for rendering the PLE attrition results} @@ -15,3 +15,25 @@ The user interface to the cohort method attrition \description{ The module viewer for rendering the PLE attrition results } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodCovariateBalanceServer.Rd b/man/cohortMethodCovariateBalanceServer.Rd index 41a874bc..83137c6d 100644 --- a/man/cohortMethodCovariateBalanceServer.Rd +++ b/man/cohortMethodCovariateBalanceServer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-covariateBalance.R +% Please edit documentation in R/estimation-cohort-method-covariateBalance.R \name{cohortMethodCovariateBalanceServer} \alias{cohortMethodCovariateBalanceServer} \title{The module server for rendering the covariate balance plot} @@ -29,3 +29,25 @@ the PLE covariate balance content server \description{ The module server for rendering the covariate balance plot } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodCovariateBalanceViewer.Rd b/man/cohortMethodCovariateBalanceViewer.Rd index 5e9011c5..020c3683 100644 --- a/man/cohortMethodCovariateBalanceViewer.Rd +++ b/man/cohortMethodCovariateBalanceViewer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-covariateBalance.R +% Please edit documentation in R/estimation-cohort-method-covariateBalance.R \name{cohortMethodCovariateBalanceViewer} \alias{cohortMethodCovariateBalanceViewer} \title{The module viewer for rendering the PLE covariate balance analysis} @@ -15,3 +15,25 @@ The user interface to the cohort method covariate balance results \description{ The module viewer for rendering the PLE covariate balance analysis } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodDiagnosticsSummaryServer.Rd b/man/cohortMethodDiagnosticsSummaryServer.Rd deleted file mode 100644 index b9660fed..00000000 --- a/man/cohortMethodDiagnosticsSummaryServer.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-diagnosticsSummary.R -\name{cohortMethodDiagnosticsSummaryServer} -\alias{cohortMethodDiagnosticsSummaryServer} -\title{The module server for rendering the PLE diagnostics summary} -\usage{ -cohortMethodDiagnosticsSummaryServer( - id, - connectionHandler, - resultDatabaseSettings, - inputSelected -) -} -\arguments{ -\item{id}{the unique reference id for the module} - -\item{connectionHandler}{the connection to the PLE results database} - -\item{resultDatabaseSettings}{a list containing the result schema and prefixes} - -\item{inputSelected}{The target id, comparator id, outcome id and analysis id selected by the user} -} -\value{ -the PLE diagnostics summary results -} -\description{ -The module server for rendering the PLE diagnostics summary -} diff --git a/man/cohortMethodDiagnosticsSummaryViewer.Rd b/man/cohortMethodDiagnosticsSummaryViewer.Rd deleted file mode 100644 index c14dcde7..00000000 --- a/man/cohortMethodDiagnosticsSummaryViewer.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-diagnosticsSummary.R -\name{cohortMethodDiagnosticsSummaryViewer} -\alias{cohortMethodDiagnosticsSummaryViewer} -\title{The module viewer for rendering the PLE diagnostics results} -\usage{ -cohortMethodDiagnosticsSummaryViewer(id) -} -\arguments{ -\item{id}{the unique reference id for the module} -} -\value{ -The user interface to the cohort method diagnostics viewer -} -\description{ -The module viewer for rendering the PLE diagnostics results -} diff --git a/man/cohortMethodHelperFile.Rd b/man/cohortMethodHelperFile.Rd deleted file mode 100644 index 01ba84ca..00000000 --- a/man/cohortMethodHelperFile.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-main.R -\name{cohortMethodHelperFile} -\alias{cohortMethodHelperFile} -\title{The location of the cohort method module helper file} -\usage{ -cohortMethodHelperFile() -} -\value{ -string location of the cohort method helper file -} -\description{ -The location of the cohort method module helper file -} -\details{ -Returns the location of the cohort method helper file -} diff --git a/man/cohortMethodKaplanMeierServer.Rd b/man/cohortMethodKaplanMeierServer.Rd index 0f3f3c4b..fdd6c12c 100644 --- a/man/cohortMethodKaplanMeierServer.Rd +++ b/man/cohortMethodKaplanMeierServer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-kaplainMeier.R +% Please edit documentation in R/estimation-cohort-method-kaplainMeier.R \name{cohortMethodKaplanMeierServer} \alias{cohortMethodKaplanMeierServer} \title{The module server for rendering the Kaplan Meier curve} @@ -26,3 +26,25 @@ the PLE Kaplain Meier content server \description{ The module server for rendering the Kaplan Meier curve } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodKaplanMeierViewer.Rd b/man/cohortMethodKaplanMeierViewer.Rd index a896e9b1..78201b40 100644 --- a/man/cohortMethodKaplanMeierViewer.Rd +++ b/man/cohortMethodKaplanMeierViewer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-kaplainMeier.R +% Please edit documentation in R/estimation-cohort-method-kaplainMeier.R \name{cohortMethodKaplanMeierViewer} \alias{cohortMethodKaplanMeierViewer} \title{The module viewer for rendering the PLE Kaplan Meier curve} @@ -15,3 +15,25 @@ The module viewer for Kaplan Meier objects \description{ The module viewer for rendering the PLE Kaplan Meier curve } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodPopulationCharacteristicsServer.Rd b/man/cohortMethodPopulationCharacteristicsServer.Rd index 34119408..e603a0ab 100644 --- a/man/cohortMethodPopulationCharacteristicsServer.Rd +++ b/man/cohortMethodPopulationCharacteristicsServer.Rd @@ -1,5 +1,6 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-populationCharacteristics.R +% Please edit documentation in +% R/estimation-cohort-method-populationCharacteristics.R \name{cohortMethodPopulationCharacteristicsServer} \alias{cohortMethodPopulationCharacteristicsServer} \title{The module server for rendering the population characteristics} @@ -26,3 +27,25 @@ the PLE population characteristics content server \description{ The module server for rendering the population characteristics } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodPopulationCharacteristicsViewer.Rd b/man/cohortMethodPopulationCharacteristicsViewer.Rd index 9262f9e3..c6191bb7 100644 --- a/man/cohortMethodPopulationCharacteristicsViewer.Rd +++ b/man/cohortMethodPopulationCharacteristicsViewer.Rd @@ -1,5 +1,6 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-populationCharacteristics.R +% Please edit documentation in +% R/estimation-cohort-method-populationCharacteristics.R \name{cohortMethodPopulationCharacteristicsViewer} \alias{cohortMethodPopulationCharacteristicsViewer} \title{The module viewer for rendering the PLE population characteristics} @@ -15,3 +16,25 @@ The user interface to the cohort method population characteristics objects \description{ The module viewer for rendering the PLE population characteristics } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodPowerServer.Rd b/man/cohortMethodPowerServer.Rd index d9b9b201..b52c51b8 100644 --- a/man/cohortMethodPowerServer.Rd +++ b/man/cohortMethodPowerServer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-power.R +% Please edit documentation in R/estimation-cohort-method-power.R \name{cohortMethodPowerServer} \alias{cohortMethodPowerServer} \title{The module server for rendering the PLE power analysis results} @@ -26,3 +26,25 @@ the PLE systematic error power server \description{ The module server for rendering the PLE power analysis results } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodPowerViewer.Rd b/man/cohortMethodPowerViewer.Rd index cea46b77..f93983e5 100644 --- a/man/cohortMethodPowerViewer.Rd +++ b/man/cohortMethodPowerViewer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-power.R +% Please edit documentation in R/estimation-cohort-method-power.R \name{cohortMethodPowerViewer} \alias{cohortMethodPowerViewer} \title{The module viewer for rendering the PLE power analysis} @@ -15,3 +15,25 @@ The user interface to the cohort method power calculation results \description{ The module viewer for rendering the PLE power analysis } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodPropensityModelServer.Rd b/man/cohortMethodPropensityModelServer.Rd index 1c0ab242..167115d9 100644 --- a/man/cohortMethodPropensityModelServer.Rd +++ b/man/cohortMethodPropensityModelServer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-propensityModel.R +% Please edit documentation in R/estimation-cohort-method-propensityModel.R \name{cohortMethodPropensityModelServer} \alias{cohortMethodPropensityModelServer} \title{The module server for rendering the propensity score model} @@ -26,3 +26,25 @@ the PLE propensity score model \description{ The module server for rendering the propensity score model } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodPropensityModelViewer.Rd b/man/cohortMethodPropensityModelViewer.Rd index d05b543a..f26809fe 100644 --- a/man/cohortMethodPropensityModelViewer.Rd +++ b/man/cohortMethodPropensityModelViewer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-propensityModel.R +% Please edit documentation in R/estimation-cohort-method-propensityModel.R \name{cohortMethodPropensityModelViewer} \alias{cohortMethodPropensityModelViewer} \title{The module viewer for rendering the PLE propensity score model covariates/coefficients} @@ -15,3 +15,25 @@ The user interface to the cohort method propensity score model covariates/coeffi \description{ The module viewer for rendering the PLE propensity score model covariates/coefficients } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodPropensityScoreDistServer.Rd b/man/cohortMethodPropensityScoreDistServer.Rd index a5b57e16..f8e84375 100644 --- a/man/cohortMethodPropensityScoreDistServer.Rd +++ b/man/cohortMethodPropensityScoreDistServer.Rd @@ -1,5 +1,6 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-propensityScoreDistribution.R +% Please edit documentation in +% R/estimation-cohort-method-propensityScoreDistribution.R \name{cohortMethodPropensityScoreDistServer} \alias{cohortMethodPropensityScoreDistServer} \title{The module server for rendering a PLE propensity score distribution} @@ -29,3 +30,25 @@ the PLE propensity score distribution content server \description{ The module server for rendering a PLE propensity score distribution } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodPropensityScoreDistViewer.Rd b/man/cohortMethodPropensityScoreDistViewer.Rd index ba8e780a..872e7705 100644 --- a/man/cohortMethodPropensityScoreDistViewer.Rd +++ b/man/cohortMethodPropensityScoreDistViewer.Rd @@ -1,5 +1,6 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-propensityScoreDistribution.R +% Please edit documentation in +% R/estimation-cohort-method-propensityScoreDistribution.R \name{cohortMethodPropensityScoreDistViewer} \alias{cohortMethodPropensityScoreDistViewer} \title{The module viewer for rendering the propensity score distribution} @@ -15,3 +16,25 @@ The user interface to the cohort method propensity score distribution \description{ The module viewer for rendering the propensity score distribution } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodResultSummaryServer.Rd b/man/cohortMethodResultSummaryServer.Rd deleted file mode 100644 index b32b7b21..00000000 --- a/man/cohortMethodResultSummaryServer.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-resultSummary.R -\name{cohortMethodResultSummaryServer} -\alias{cohortMethodResultSummaryServer} -\title{The module server for rendering the PLE diagnostics summary} -\usage{ -cohortMethodResultSummaryServer( - id, - connectionHandler, - resultDatabaseSettings, - inputSelected -) -} -\arguments{ -\item{id}{the unique reference id for the module} - -\item{connectionHandler}{the connection to the PLE results database} - -\item{resultDatabaseSettings}{a list containing the result schema and prefixes} - -\item{inputSelected}{The target id, comparator id, outcome id and analysis id selected by the user} -} -\value{ -the PLE diagnostics summary results -} -\description{ -The module server for rendering the PLE diagnostics summary -} diff --git a/man/cohortMethodResultSummaryViewer.Rd b/man/cohortMethodResultSummaryViewer.Rd deleted file mode 100644 index d1545136..00000000 --- a/man/cohortMethodResultSummaryViewer.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-resultSummary.R -\name{cohortMethodResultSummaryViewer} -\alias{cohortMethodResultSummaryViewer} -\title{The module viewer for rendering the cohort method results} -\usage{ -cohortMethodResultSummaryViewer(id) -} -\arguments{ -\item{id}{the unique reference id for the module} -} -\value{ -The user interface to the cohort method diagnostics viewer -} -\description{ -The module viewer for rendering the cohort method results -} diff --git a/man/cohortMethodServer.Rd b/man/cohortMethodServer.Rd deleted file mode 100644 index 83f734dd..00000000 --- a/man/cohortMethodServer.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-main.R -\name{cohortMethodServer} -\alias{cohortMethodServer} -\title{The module server for the main cohort method module} -\usage{ -cohortMethodServer(id, connectionHandler, resultDatabaseSettings) -} -\arguments{ -\item{id}{the unique reference id for the module} - -\item{connectionHandler}{a connection to the database with the results} - -\item{resultDatabaseSettings}{a named list containing the PLE results database connection details} -} -\value{ -the PLE results viewer main module server -} -\description{ -The module server for the main cohort method module -} diff --git a/man/cohortMethodSystematicErrorServer.Rd b/man/cohortMethodSystematicErrorServer.Rd index 3f44490e..d8410d6e 100644 --- a/man/cohortMethodSystematicErrorServer.Rd +++ b/man/cohortMethodSystematicErrorServer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-systematicError.R +% Please edit documentation in R/estimation-cohort-method-systematicError.R \name{cohortMethodSystematicErrorServer} \alias{cohortMethodSystematicErrorServer} \title{The module server for rendering the systematic error objects} @@ -26,3 +26,25 @@ the PLE systematic error content server \description{ The module server for rendering the systematic error objects } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodSystematicErrorViewer.Rd b/man/cohortMethodSystematicErrorViewer.Rd index c4dea713..c3187437 100644 --- a/man/cohortMethodSystematicErrorViewer.Rd +++ b/man/cohortMethodSystematicErrorViewer.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-systematicError.R +% Please edit documentation in R/estimation-cohort-method-systematicError.R \name{cohortMethodSystematicErrorViewer} \alias{cohortMethodSystematicErrorViewer} \title{The module viewer for rendering the PLE systematic error objects} @@ -15,3 +15,25 @@ The user interface to the cohort method systematic error module \description{ The module viewer for rendering the PLE systematic error objects } +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/cohortMethodViewer.Rd b/man/cohortMethodViewer.Rd deleted file mode 100644 index ae650d1a..00000000 --- a/man/cohortMethodViewer.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/cohort-method-main.R -\name{cohortMethodViewer} -\alias{cohortMethodViewer} -\title{The viewer of the main cohort method module} -\usage{ -cohortMethodViewer(id) -} -\arguments{ -\item{id}{the unique reference id for the module} -} -\value{ -The user interface to the cohort method results viewer -} -\description{ -The viewer of the main cohort method module -} diff --git a/man/cohortOverlapView.Rd b/man/cohortOverlapView.Rd index 11d57f55..ecf3b61b 100644 --- a/man/cohortOverlapView.Rd +++ b/man/cohortOverlapView.Rd @@ -12,3 +12,27 @@ cohortOverlapView(id) \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/compareCohortCharacterizationView.Rd b/man/compareCohortCharacterizationView.Rd index eba1e958..ca43605c 100644 --- a/man/compareCohortCharacterizationView.Rd +++ b/man/compareCohortCharacterizationView.Rd @@ -17,3 +17,27 @@ compareCohortCharacterizationView( \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/conceptsInDataSourceView.Rd b/man/conceptsInDataSourceView.Rd index 72f44a10..c5400057 100644 --- a/man/conceptsInDataSourceView.Rd +++ b/man/conceptsInDataSourceView.Rd @@ -12,3 +12,27 @@ conceptsInDataSourceView(id) \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/createCdDatabaseDataSource.Rd b/man/createCdDatabaseDataSource.Rd index 60bab3d9..2bd8b7e5 100644 --- a/man/createCdDatabaseDataSource.Rd +++ b/man/createCdDatabaseDataSource.Rd @@ -33,3 +33,27 @@ use this to create an interface to cohort diagnostics results data NOTE: I think this would make a good R6 class for other objects in this package so you could query them outside of a shiny app. E.g. if you wanted to make a custom R markdown template } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/createCustomColDefList.Rd b/man/createCustomColDefList.Rd index 505da8f1..a1f74a23 100644 --- a/man/createCustomColDefList.Rd +++ b/man/createCustomColDefList.Rd @@ -35,6 +35,14 @@ A named list of reactable::colDef objects Creating a list of custom column definitions for use in reactables } \seealso{ +Other {Utils}: +\code{\link{datasourcesHelperFile}()}, +\code{\link{datasourcesServer}()}, +\code{\link{datasourcesViewer}()}, +\code{\link{makeButtonLabel}()}, +\code{\link{resultTableServer}()}, +\code{\link{resultTableViewer}()} + Other {Utils}: \code{\link{datasourcesHelperFile}()}, \code{\link{datasourcesServer}()}, diff --git a/man/dataDiagnosticDrillServer.Rd b/man/dataDiagnosticDrillServer.Rd index cff580fb..907f4d6f 100644 --- a/man/dataDiagnosticDrillServer.Rd +++ b/man/dataDiagnosticDrillServer.Rd @@ -22,3 +22,13 @@ The module server for exploring prediction summary results \details{ The user specifies the id for the module } +\seealso{ +Other {DataDiagnostics}: +\code{\link{dataDiagnosticDrillViewer}()}, +\code{\link{dataDiagnosticHelperFile}()}, +\code{\link{dataDiagnosticServer}()}, +\code{\link{dataDiagnosticSummaryServer}()}, +\code{\link{dataDiagnosticSummaryViewer}()}, +\code{\link{dataDiagnosticViewer}()} +} +\concept{{DataDiagnostics}} diff --git a/man/dataDiagnosticDrillViewer.Rd b/man/dataDiagnosticDrillViewer.Rd index ba386ef5..7f92bbef 100644 --- a/man/dataDiagnosticDrillViewer.Rd +++ b/man/dataDiagnosticDrillViewer.Rd @@ -18,3 +18,13 @@ The module viewer for exploring data-diagnostic results in more detail \details{ The user specifies the id for the module } +\seealso{ +Other {DataDiagnostics}: +\code{\link{dataDiagnosticDrillServer}()}, +\code{\link{dataDiagnosticHelperFile}()}, +\code{\link{dataDiagnosticServer}()}, +\code{\link{dataDiagnosticSummaryServer}()}, +\code{\link{dataDiagnosticSummaryViewer}()}, +\code{\link{dataDiagnosticViewer}()} +} +\concept{{DataDiagnostics}} diff --git a/man/dataDiagnosticHelperFile.Rd b/man/dataDiagnosticHelperFile.Rd index 907edd2f..887231d9 100644 --- a/man/dataDiagnosticHelperFile.Rd +++ b/man/dataDiagnosticHelperFile.Rd @@ -15,3 +15,13 @@ The location of the data-diagnostic module helper file \details{ Returns the location of the data-diagnostic helper file } +\seealso{ +Other {DataDiagnostics}: +\code{\link{dataDiagnosticDrillServer}()}, +\code{\link{dataDiagnosticDrillViewer}()}, +\code{\link{dataDiagnosticServer}()}, +\code{\link{dataDiagnosticSummaryServer}()}, +\code{\link{dataDiagnosticSummaryViewer}()}, +\code{\link{dataDiagnosticViewer}()} +} +\concept{{DataDiagnostics}} diff --git a/man/dataDiagnosticServer.Rd b/man/dataDiagnosticServer.Rd index 63ec90db..372daa36 100644 --- a/man/dataDiagnosticServer.Rd +++ b/man/dataDiagnosticServer.Rd @@ -26,3 +26,13 @@ The module server for exploring data-diagnostic \details{ The user specifies the id for the module } +\seealso{ +Other {DataDiagnostics}: +\code{\link{dataDiagnosticDrillServer}()}, +\code{\link{dataDiagnosticDrillViewer}()}, +\code{\link{dataDiagnosticHelperFile}()}, +\code{\link{dataDiagnosticSummaryServer}()}, +\code{\link{dataDiagnosticSummaryViewer}()}, +\code{\link{dataDiagnosticViewer}()} +} +\concept{{DataDiagnostics}} diff --git a/man/dataDiagnosticSummaryServer.Rd b/man/dataDiagnosticSummaryServer.Rd index a5eea92d..28639530 100644 --- a/man/dataDiagnosticSummaryServer.Rd +++ b/man/dataDiagnosticSummaryServer.Rd @@ -22,3 +22,13 @@ The module server for exploring prediction summary results \details{ The user specifies the id for the module } +\seealso{ +Other {DataDiagnostics}: +\code{\link{dataDiagnosticDrillServer}()}, +\code{\link{dataDiagnosticDrillViewer}()}, +\code{\link{dataDiagnosticHelperFile}()}, +\code{\link{dataDiagnosticServer}()}, +\code{\link{dataDiagnosticSummaryViewer}()}, +\code{\link{dataDiagnosticViewer}()} +} +\concept{{DataDiagnostics}} diff --git a/man/dataDiagnosticSummaryViewer.Rd b/man/dataDiagnosticSummaryViewer.Rd index a868a131..3b9e5986 100644 --- a/man/dataDiagnosticSummaryViewer.Rd +++ b/man/dataDiagnosticSummaryViewer.Rd @@ -18,3 +18,13 @@ The module viewer for exploring data-diagnostic summary results \details{ The user specifies the id for the module } +\seealso{ +Other {DataDiagnostics}: +\code{\link{dataDiagnosticDrillServer}()}, +\code{\link{dataDiagnosticDrillViewer}()}, +\code{\link{dataDiagnosticHelperFile}()}, +\code{\link{dataDiagnosticServer}()}, +\code{\link{dataDiagnosticSummaryServer}()}, +\code{\link{dataDiagnosticViewer}()} +} +\concept{{DataDiagnostics}} diff --git a/man/dataDiagnosticViewer.Rd b/man/dataDiagnosticViewer.Rd index d2c840cc..291960f7 100644 --- a/man/dataDiagnosticViewer.Rd +++ b/man/dataDiagnosticViewer.Rd @@ -18,3 +18,13 @@ The module viewer for exploring data-diagnostic \details{ The user specifies the id for the module } +\seealso{ +Other {DataDiagnostics}: +\code{\link{dataDiagnosticDrillServer}()}, +\code{\link{dataDiagnosticDrillViewer}()}, +\code{\link{dataDiagnosticHelperFile}()}, +\code{\link{dataDiagnosticServer}()}, +\code{\link{dataDiagnosticSummaryServer}()}, +\code{\link{dataDiagnosticSummaryViewer}()} +} +\concept{{DataDiagnostics}} diff --git a/man/databaseInformationView.Rd b/man/databaseInformationView.Rd index 63492980..7341b9c8 100644 --- a/man/databaseInformationView.Rd +++ b/man/databaseInformationView.Rd @@ -12,3 +12,27 @@ databaseInformationView(id) \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/datasourcesHelperFile.Rd b/man/datasourcesHelperFile.Rd index 4c1526a8..bcd60638 100644 --- a/man/datasourcesHelperFile.Rd +++ b/man/datasourcesHelperFile.Rd @@ -13,6 +13,14 @@ The helper html file for the datasources module Define the helper file for the module } \seealso{ +Other {Utils}: +\code{\link{createCustomColDefList}()}, +\code{\link{datasourcesServer}()}, +\code{\link{datasourcesViewer}()}, +\code{\link{makeButtonLabel}()}, +\code{\link{resultTableServer}()}, +\code{\link{resultTableViewer}()} + Other {Utils}: \code{\link{createCustomColDefList}()}, \code{\link{datasourcesServer}()}, diff --git a/man/datasourcesServer.Rd b/man/datasourcesServer.Rd index 10a2f784..e70923c1 100644 --- a/man/datasourcesServer.Rd +++ b/man/datasourcesServer.Rd @@ -20,6 +20,14 @@ The server for the datasources module The server function for the datasources module } \seealso{ +Other {Utils}: +\code{\link{createCustomColDefList}()}, +\code{\link{datasourcesHelperFile}()}, +\code{\link{datasourcesViewer}()}, +\code{\link{makeButtonLabel}()}, +\code{\link{resultTableServer}()}, +\code{\link{resultTableViewer}()} + Other {Utils}: \code{\link{createCustomColDefList}()}, \code{\link{datasourcesHelperFile}()}, diff --git a/man/datasourcesViewer.Rd b/man/datasourcesViewer.Rd index c5fc5fe3..e986b45b 100644 --- a/man/datasourcesViewer.Rd +++ b/man/datasourcesViewer.Rd @@ -16,6 +16,14 @@ The UI for the datasources module The viewer function for hte datasources module } \seealso{ +Other {Utils}: +\code{\link{createCustomColDefList}()}, +\code{\link{datasourcesHelperFile}()}, +\code{\link{datasourcesServer}()}, +\code{\link{makeButtonLabel}()}, +\code{\link{resultTableServer}()}, +\code{\link{resultTableViewer}()} + Other {Utils}: \code{\link{createCustomColDefList}()}, \code{\link{datasourcesHelperFile}()}, diff --git a/man/estimationHelperFile.Rd b/man/estimationHelperFile.Rd new file mode 100644 index 00000000..392a657d --- /dev/null +++ b/man/estimationHelperFile.Rd @@ -0,0 +1,39 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/estimation-main.R +\name{estimationHelperFile} +\alias{estimationHelperFile} +\title{The location of the estimation module helper file} +\usage{ +estimationHelperFile() +} +\value{ +string location of the characterization helper file +} +\description{ +The location of the estimation module helper file +} +\details{ +Returns the location of the characterization helper file +} +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationServer}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/estimationServer.Rd b/man/estimationServer.Rd new file mode 100644 index 00000000..a0416244 --- /dev/null +++ b/man/estimationServer.Rd @@ -0,0 +1,50 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/estimation-main.R +\name{estimationServer} +\alias{estimationServer} +\title{The module server for exploring estimation studies} +\usage{ +estimationServer( + id, + connectionHandler, + resultDatabaseSettings = list(port = 1) +) +} +\arguments{ +\item{id}{the unique reference id for the module} + +\item{connectionHandler}{a connection to the database with the results} + +\item{resultDatabaseSettings}{a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix} +} +\value{ +The server for the estimation module +} +\description{ +The module server for exploring estimation studies +} +\details{ +The user specifies the id for the module +} +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationViewer}()} +} +\concept{{Estimation}} diff --git a/man/estimationViewer.Rd b/man/estimationViewer.Rd new file mode 100644 index 00000000..a873b1d6 --- /dev/null +++ b/man/estimationViewer.Rd @@ -0,0 +1,42 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/estimation-main.R +\name{estimationViewer} +\alias{estimationViewer} +\title{The module viewer for exploring characterization studies} +\usage{ +estimationViewer(id = 1) +} +\arguments{ +\item{id}{the unique reference id for the module} +} +\value{ +The user interface to the characterization viewer module +} +\description{ +The module viewer for exploring characterization studies +} +\details{ +The user specifies the id for the module +} +\seealso{ +Other {Estimation}: +\code{\link{cohortMethodAttritionServer}()}, +\code{\link{cohortMethodAttritionViewer}()}, +\code{\link{cohortMethodCovariateBalanceServer}()}, +\code{\link{cohortMethodCovariateBalanceViewer}()}, +\code{\link{cohortMethodKaplanMeierServer}()}, +\code{\link{cohortMethodKaplanMeierViewer}()}, +\code{\link{cohortMethodPopulationCharacteristicsServer}()}, +\code{\link{cohortMethodPopulationCharacteristicsViewer}()}, +\code{\link{cohortMethodPowerServer}()}, +\code{\link{cohortMethodPowerViewer}()}, +\code{\link{cohortMethodPropensityModelServer}()}, +\code{\link{cohortMethodPropensityModelViewer}()}, +\code{\link{cohortMethodPropensityScoreDistServer}()}, +\code{\link{cohortMethodPropensityScoreDistViewer}()}, +\code{\link{cohortMethodSystematicErrorServer}()}, +\code{\link{cohortMethodSystematicErrorViewer}()}, +\code{\link{estimationHelperFile}()}, +\code{\link{estimationServer}()} +} +\concept{{Estimation}} diff --git a/man/evidenceSynthesisHelperFile.Rd b/man/evidenceSynthesisHelperFile.Rd deleted file mode 100644 index aa65874e..00000000 --- a/man/evidenceSynthesisHelperFile.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/evidence-synth-main.R -\name{evidenceSynthesisHelperFile} -\alias{evidenceSynthesisHelperFile} -\title{The location of the evidence synthesis module helper file} -\usage{ -evidenceSynthesisHelperFile() -} -\value{ -string location of the evidence synthesis helper file -} -\description{ -The location of the evidence synthesis module helper file -} -\details{ -Returns the location of the evidence synthesis helper file -} diff --git a/man/evidenceSynthesisServer.Rd b/man/evidenceSynthesisServer.Rd deleted file mode 100644 index cf33e7d6..00000000 --- a/man/evidenceSynthesisServer.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/evidence-synth-main.R -\name{evidenceSynthesisServer} -\alias{evidenceSynthesisServer} -\title{The module server for exploring PatientLevelPrediction} -\usage{ -evidenceSynthesisServer( - id, - connectionHandler, - resultDatabaseSettings = list(port = 1) -) -} -\arguments{ -\item{id}{the unique reference id for the module} - -\item{connectionHandler}{a connection to the database with the results} - -\item{resultDatabaseSettings}{a list containing the result schema and prefixes} -} -\value{ -The server for the PatientLevelPrediction module -} -\description{ -The module server for exploring PatientLevelPrediction -} -\details{ -The user specifies the id for the module -} diff --git a/man/evidenceSynthesisViewer.Rd b/man/evidenceSynthesisViewer.Rd deleted file mode 100644 index 6e4b215e..00000000 --- a/man/evidenceSynthesisViewer.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/evidence-synth-main.R -\name{evidenceSynthesisViewer} -\alias{evidenceSynthesisViewer} -\title{The module viewer for exploring evidence-synthesis} -\usage{ -evidenceSynthesisViewer(id = 1) -} -\arguments{ -\item{id}{the unique reference id for the module} -} -\value{ -The user interface to the evidence-synthesis viewer module -} -\description{ -The module viewer for exploring evidence-synthesis -} -\details{ -The user specifies the id for the module -} diff --git a/man/getCirceRenderedExpression.Rd b/man/getCirceRenderedExpression.Rd index 6be115c4..f4b46023 100644 --- a/man/getCirceRenderedExpression.Rd +++ b/man/getCirceRenderedExpression.Rd @@ -25,3 +25,27 @@ list object \description{ Returns list with circe generated documentation } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/getEnabledCdReports.Rd b/man/getEnabledCdReports.Rd index b48eee5d..681367f8 100644 --- a/man/getEnabledCdReports.Rd +++ b/man/getEnabledCdReports.Rd @@ -12,3 +12,27 @@ getEnabledCdReports(dataSource) \description{ Get enable cd reports from available data } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/getExampleConnectionDetails.Rd b/man/getExampleConnectionDetails.Rd new file mode 100644 index 00000000..5e708ca2 --- /dev/null +++ b/man/getExampleConnectionDetails.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/helpers-example.R +\name{getExampleConnectionDetails} +\alias{getExampleConnectionDetails} +\title{A connection details to an example result database} +\usage{ +getExampleConnectionDetails() +} +\value{ +The connection details to an example result database +} +\description{ +A connection details to an example result database +} +\details{ +Finds the location within the package of an sqlite database with example results for 1) CohortGenerator, +2) Characterization, 3) PatientLevelPrediction, 4) CohortMethod, 5) SelfControlledCaseSeries and 6) CohortIncidence +} +\concept{{Example}} diff --git a/man/homeHelperFile.Rd b/man/homeHelperFile.Rd new file mode 100644 index 00000000..72c2c77b --- /dev/null +++ b/man/homeHelperFile.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/home-main.R +\name{homeHelperFile} +\alias{homeHelperFile} +\title{The location of the home module helper file} +\usage{ +homeHelperFile() +} +\value{ +string location of the home helper file +} +\description{ +The location of the home module helper file +} +\details{ +Returns the location of the home helper file +} +\seealso{ +Other {Home}: +\code{\link{homeServer}()}, +\code{\link{homeViewer}()} +} +\concept{{Home}} diff --git a/man/sccsServer.Rd b/man/homeServer.Rd similarity index 51% rename from man/sccsServer.Rd rename to man/homeServer.Rd index d1679ce0..45bbdcc7 100644 --- a/man/sccsServer.Rd +++ b/man/homeServer.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sccs-main.R -\name{sccsServer} -\alias{sccsServer} -\title{The module server for exploring SCCS} +% Please edit documentation in R/home-main.R +\name{homeServer} +\alias{homeServer} +\title{The module server for exploring home} \usage{ -sccsServer(id, connectionHandler, resultDatabaseSettings = list(port = 1)) +homeServer(id, connectionHandler, resultDatabaseSettings = list(port = 1)) } \arguments{ \item{id}{the unique reference id for the module} @@ -14,11 +14,17 @@ sccsServer(id, connectionHandler, resultDatabaseSettings = list(port = 1)) \item{resultDatabaseSettings}{a list containing the prediction result schema and connection details} } \value{ -The server for the PatientLevelPrediction module +The server for the home module } \description{ -The module server for exploring SCCS +The module server for exploring home } \details{ The user specifies the id for the module } +\seealso{ +Other {Home}: +\code{\link{homeHelperFile}()}, +\code{\link{homeViewer}()} +} +\concept{{Home}} diff --git a/man/homeViewer.Rd b/man/homeViewer.Rd new file mode 100644 index 00000000..ffc34871 --- /dev/null +++ b/man/homeViewer.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/home-main.R +\name{homeViewer} +\alias{homeViewer} +\title{The module viewer for exploring home} +\usage{ +homeViewer(id = 1) +} +\arguments{ +\item{id}{the unique reference id for the module} +} +\value{ +The user interface to the home viewer module +} +\description{ +The module viewer for exploring home +} +\details{ +The user specifies the id for the module +} +\seealso{ +Other {Home}: +\code{\link{homeHelperFile}()}, +\code{\link{homeServer}()} +} +\concept{{Home}} diff --git a/man/incidenceRatesView.Rd b/man/incidenceRatesView.Rd index 6cd4c74e..a8ae79b7 100644 --- a/man/incidenceRatesView.Rd +++ b/man/incidenceRatesView.Rd @@ -12,3 +12,27 @@ incidenceRatesView(id) \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/inclusionRulesView.Rd b/man/inclusionRulesView.Rd index 5c164c9b..3dac0b49 100644 --- a/man/inclusionRulesView.Rd +++ b/man/inclusionRulesView.Rd @@ -12,3 +12,27 @@ inclusionRulesView(id) \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/indexEventBreakdownView.Rd b/man/indexEventBreakdownView.Rd index 59090e51..48e238be 100644 --- a/man/indexEventBreakdownView.Rd +++ b/man/indexEventBreakdownView.Rd @@ -12,3 +12,27 @@ indexEventBreakdownView(id) \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/makeButtonLabel.Rd b/man/makeButtonLabel.Rd index 7a8f9c2a..6d32375a 100644 --- a/man/makeButtonLabel.Rd +++ b/man/makeButtonLabel.Rd @@ -16,6 +16,14 @@ html code to make a button label Make a label for an html button } \seealso{ +Other {Utils}: +\code{\link{createCustomColDefList}()}, +\code{\link{datasourcesHelperFile}()}, +\code{\link{datasourcesServer}()}, +\code{\link{datasourcesViewer}()}, +\code{\link{resultTableServer}()}, +\code{\link{resultTableViewer}()} + Other {Utils}: \code{\link{createCustomColDefList}()}, \code{\link{datasourcesHelperFile}()}, diff --git a/man/orpahanConceptsView.Rd b/man/orpahanConceptsView.Rd index 292dc446..c77aa9f6 100644 --- a/man/orpahanConceptsView.Rd +++ b/man/orpahanConceptsView.Rd @@ -14,7 +14,47 @@ Use for customizing UI } \seealso{ Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, \code{\link{cohortDiagnosticsHelperFile}()}, -\code{\link{cohortDiagnosticsServer}()} +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} + +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{timeDistributionsView}()}, +\code{\link{visitContextView}()} } \concept{{CohortDiagnostics}} diff --git a/man/patientLevelPredictionCalibrationServer.Rd b/man/patientLevelPredictionCalibrationServer.Rd index b06dbf22..f3b88bff 100644 --- a/man/patientLevelPredictionCalibrationServer.Rd +++ b/man/patientLevelPredictionCalibrationServer.Rd @@ -32,3 +32,29 @@ The module server for exploring prediction validation results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionCalibrationViewer.Rd b/man/patientLevelPredictionCalibrationViewer.Rd index be958e62..d3f3b180 100644 --- a/man/patientLevelPredictionCalibrationViewer.Rd +++ b/man/patientLevelPredictionCalibrationViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction model calibration results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionCovariateSummaryServer.Rd b/man/patientLevelPredictionCovariateSummaryServer.Rd index 32ed5c59..3d99221e 100644 --- a/man/patientLevelPredictionCovariateSummaryServer.Rd +++ b/man/patientLevelPredictionCovariateSummaryServer.Rd @@ -38,3 +38,29 @@ The module server for exploring prediction covariate summary results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionCovariateSummaryViewer.Rd b/man/patientLevelPredictionCovariateSummaryViewer.Rd index fd4fb28d..7ad174a1 100644 --- a/man/patientLevelPredictionCovariateSummaryViewer.Rd +++ b/man/patientLevelPredictionCovariateSummaryViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction covariate summary results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionCutoffServer.Rd b/man/patientLevelPredictionCutoffServer.Rd index 5f2916d8..8886720e 100644 --- a/man/patientLevelPredictionCutoffServer.Rd +++ b/man/patientLevelPredictionCutoffServer.Rd @@ -32,3 +32,29 @@ The module server for exploring prediction cut-off results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionCutoffViewer.Rd b/man/patientLevelPredictionCutoffViewer.Rd index c4dcd60f..b91f0de8 100644 --- a/man/patientLevelPredictionCutoffViewer.Rd +++ b/man/patientLevelPredictionCutoffViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction cut-off results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionDesignSummaryServer.Rd b/man/patientLevelPredictionDesignSummaryServer.Rd index 238528ae..13d992ce 100644 --- a/man/patientLevelPredictionDesignSummaryServer.Rd +++ b/man/patientLevelPredictionDesignSummaryServer.Rd @@ -26,3 +26,29 @@ The module server for exploring prediction designs in the results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionDesignSummaryViewer.Rd b/man/patientLevelPredictionDesignSummaryViewer.Rd index 311bcd6e..f51d6cdc 100644 --- a/man/patientLevelPredictionDesignSummaryViewer.Rd +++ b/man/patientLevelPredictionDesignSummaryViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction designs that have been run \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionDiagnosticsServer.Rd b/man/patientLevelPredictionDiagnosticsServer.Rd index 8b2db8f0..8c262d88 100644 --- a/man/patientLevelPredictionDiagnosticsServer.Rd +++ b/man/patientLevelPredictionDiagnosticsServer.Rd @@ -29,3 +29,29 @@ The module server for exploring prediction diagnostic results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionDiagnosticsViewer.Rd b/man/patientLevelPredictionDiagnosticsViewer.Rd index c2eccf8b..67a03a93 100644 --- a/man/patientLevelPredictionDiagnosticsViewer.Rd +++ b/man/patientLevelPredictionDiagnosticsViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction diagnostic results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionDiscriminationServer.Rd b/man/patientLevelPredictionDiscriminationServer.Rd index 1cafe353..fec85533 100644 --- a/man/patientLevelPredictionDiscriminationServer.Rd +++ b/man/patientLevelPredictionDiscriminationServer.Rd @@ -32,3 +32,29 @@ The module server for exploring prediction model discrimination results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionDiscriminationViewer.Rd b/man/patientLevelPredictionDiscriminationViewer.Rd index 0c2c43f1..bb48f5a2 100644 --- a/man/patientLevelPredictionDiscriminationViewer.Rd +++ b/man/patientLevelPredictionDiscriminationViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction model discrimination results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionHelperFile.Rd b/man/patientLevelPredictionHelperFile.Rd index c200cbc6..3693e75b 100644 --- a/man/patientLevelPredictionHelperFile.Rd +++ b/man/patientLevelPredictionHelperFile.Rd @@ -15,3 +15,29 @@ The location of the prediction module helper file \details{ Returns the location of the prediction helper file } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionModelSummaryServer.Rd b/man/patientLevelPredictionModelSummaryServer.Rd index 0de376f4..e89e3679 100644 --- a/man/patientLevelPredictionModelSummaryServer.Rd +++ b/man/patientLevelPredictionModelSummaryServer.Rd @@ -29,3 +29,29 @@ The module server for exploring prediction summary results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionModelSummaryViewer.Rd b/man/patientLevelPredictionModelSummaryViewer.Rd index 92aa79f3..6baac054 100644 --- a/man/patientLevelPredictionModelSummaryViewer.Rd +++ b/man/patientLevelPredictionModelSummaryViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction summary results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionNbServer.Rd b/man/patientLevelPredictionNbServer.Rd index e0c3a360..c1d54c5b 100644 --- a/man/patientLevelPredictionNbServer.Rd +++ b/man/patientLevelPredictionNbServer.Rd @@ -32,3 +32,29 @@ The module server for exploring prediction net-benefit results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionNbViewer.Rd b/man/patientLevelPredictionNbViewer.Rd index 93badc2b..2cebf1c6 100644 --- a/man/patientLevelPredictionNbViewer.Rd +++ b/man/patientLevelPredictionNbViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction net-benefit results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionServer.Rd b/man/patientLevelPredictionServer.Rd index 7c725ebd..4d5faa63 100644 --- a/man/patientLevelPredictionServer.Rd +++ b/man/patientLevelPredictionServer.Rd @@ -26,3 +26,29 @@ The module server for exploring PatientLevelPrediction \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionSettingsServer.Rd b/man/patientLevelPredictionSettingsServer.Rd index b987945c..9762a2ce 100644 --- a/man/patientLevelPredictionSettingsServer.Rd +++ b/man/patientLevelPredictionSettingsServer.Rd @@ -38,3 +38,29 @@ The module server for exploring prediction settings \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionSettingsViewer.Rd b/man/patientLevelPredictionSettingsViewer.Rd index 63ee8176..616b7a74 100644 --- a/man/patientLevelPredictionSettingsViewer.Rd +++ b/man/patientLevelPredictionSettingsViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction settings \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionValidationServer.Rd b/man/patientLevelPredictionValidationServer.Rd index 2e36700a..b1379ccb 100644 --- a/man/patientLevelPredictionValidationServer.Rd +++ b/man/patientLevelPredictionValidationServer.Rd @@ -38,3 +38,29 @@ The module server for exploring prediction validation results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationViewer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionValidationViewer.Rd b/man/patientLevelPredictionValidationViewer.Rd index abab5da5..f950fe00 100644 --- a/man/patientLevelPredictionValidationViewer.Rd +++ b/man/patientLevelPredictionValidationViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring prediction validation results \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/patientLevelPredictionViewer.Rd b/man/patientLevelPredictionViewer.Rd index 010e47c3..61ef9117 100644 --- a/man/patientLevelPredictionViewer.Rd +++ b/man/patientLevelPredictionViewer.Rd @@ -18,3 +18,29 @@ The module viewer for exploring PatientLevelPrediction \details{ The user specifies the id for the module } +\seealso{ +Other {PatientLevelPrediction}: +\code{\link{patientLevelPredictionCalibrationServer}()}, +\code{\link{patientLevelPredictionCalibrationViewer}()}, +\code{\link{patientLevelPredictionCovariateSummaryServer}()}, +\code{\link{patientLevelPredictionCovariateSummaryViewer}()}, +\code{\link{patientLevelPredictionCutoffServer}()}, +\code{\link{patientLevelPredictionCutoffViewer}()}, +\code{\link{patientLevelPredictionDesignSummaryServer}()}, +\code{\link{patientLevelPredictionDesignSummaryViewer}()}, +\code{\link{patientLevelPredictionDiagnosticsServer}()}, +\code{\link{patientLevelPredictionDiagnosticsViewer}()}, +\code{\link{patientLevelPredictionDiscriminationServer}()}, +\code{\link{patientLevelPredictionDiscriminationViewer}()}, +\code{\link{patientLevelPredictionHelperFile}()}, +\code{\link{patientLevelPredictionModelSummaryServer}()}, +\code{\link{patientLevelPredictionModelSummaryViewer}()}, +\code{\link{patientLevelPredictionNbServer}()}, +\code{\link{patientLevelPredictionNbViewer}()}, +\code{\link{patientLevelPredictionServer}()}, +\code{\link{patientLevelPredictionSettingsServer}()}, +\code{\link{patientLevelPredictionSettingsViewer}()}, +\code{\link{patientLevelPredictionValidationServer}()}, +\code{\link{patientLevelPredictionValidationViewer}()} +} +\concept{{PatientLevelPrediction}} diff --git a/man/phevaluatorHelperFile.Rd b/man/phevaluatorHelperFile.Rd index 6c80ebc0..9bbe7459 100644 --- a/man/phevaluatorHelperFile.Rd +++ b/man/phevaluatorHelperFile.Rd @@ -16,6 +16,10 @@ The location of the phevaluator module helper file Returns the location of the cohort-generator helper file } \seealso{ +Other {PheValuator}: +\code{\link{phevaluatorServer}()}, +\code{\link{phevaluatorViewer}()} + Other {PheValuator}: \code{\link{phevaluatorServer}()}, \code{\link{phevaluatorViewer}()} diff --git a/man/phevaluatorServer.Rd b/man/phevaluatorServer.Rd index f8e32d9f..2cf30409 100644 --- a/man/phevaluatorServer.Rd +++ b/man/phevaluatorServer.Rd @@ -20,6 +20,10 @@ The phevaluator main module server The module server for the main phevaluator module } \seealso{ +Other {PheValuator}: +\code{\link{phevaluatorHelperFile}()}, +\code{\link{phevaluatorViewer}()} + Other {PheValuator}: \code{\link{phevaluatorHelperFile}()}, \code{\link{phevaluatorViewer}()} diff --git a/man/phevaluatorViewer.Rd b/man/phevaluatorViewer.Rd index 6dee97cb..1537318f 100644 --- a/man/phevaluatorViewer.Rd +++ b/man/phevaluatorViewer.Rd @@ -16,6 +16,10 @@ The user interface to the phevaluator results viewer The viewer of the phevaluator module } \seealso{ +Other {PheValuator}: +\code{\link{phevaluatorHelperFile}()}, +\code{\link{phevaluatorServer}()} + Other {PheValuator}: \code{\link{phevaluatorHelperFile}()}, \code{\link{phevaluatorServer}()} diff --git a/man/reportHelperFile.Rd b/man/reportHelperFile.Rd new file mode 100644 index 00000000..4bdb64f3 --- /dev/null +++ b/man/reportHelperFile.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/report-main.R +\name{reportHelperFile} +\alias{reportHelperFile} +\title{The location of the report module helper file} +\usage{ +reportHelperFile() +} +\value{ +string location of the report helper file +} +\description{ +The location of the report module helper file +} +\details{ +Returns the location of the report helper file +} +\seealso{ +Other {Report}: +\code{\link{reportServer}()}, +\code{\link{reportViewer}()} +} +\concept{{Report}} diff --git a/man/reportServer.Rd b/man/reportServer.Rd new file mode 100644 index 00000000..7fab7c76 --- /dev/null +++ b/man/reportServer.Rd @@ -0,0 +1,46 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/report-main.R +\name{reportServer} +\alias{reportServer} +\title{The module server for the shiny app report module} +\usage{ +reportServer( + id = "reportModule", + connectionHandler = NULL, + resultDatabaseSettings = NULL, + server = Sys.getenv("RESULTS_SERVER"), + username = Sys.getenv("RESULTS_USER"), + password = Sys.getenv("RESULTS_PASSWORD"), + dbms = Sys.getenv("RESULTS_DBMS") +) +} +\arguments{ +\item{id}{the unique reference id for the module} + +\item{connectionHandler}{a connection to the database with the results} + +\item{resultDatabaseSettings}{a list containing the characterization result schema, dbms, tablePrefix, databaseTable and cgTablePrefix} + +\item{server}{server for the connection to the results for quarto} + +\item{username}{username for the connection to the results for quarto} + +\item{password}{password for the connection to the results for quarto} + +\item{dbms}{dbms for the connection to the results for quarto} +} +\value{ +The server for the shiny app home +} +\description{ +The module server for the shiny app report module +} +\details{ +The user specifies the id for the module +} +\seealso{ +Other {Report}: +\code{\link{reportHelperFile}()}, +\code{\link{reportViewer}()} +} +\concept{{Report}} diff --git a/man/reportViewer.Rd b/man/reportViewer.Rd new file mode 100644 index 00000000..e76e8aa7 --- /dev/null +++ b/man/reportViewer.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/report-main.R +\name{reportViewer} +\alias{reportViewer} +\title{The module viewer for the shiny app report module} +\usage{ +reportViewer(id = "reportModule") +} +\arguments{ +\item{id}{the unique reference id for the module} +} +\value{ +The user interface to the home page module +} +\description{ +The module viewer for the shiny app report module +} +\details{ +The user specifies the id for the module +} +\seealso{ +Other {Report}: +\code{\link{reportHelperFile}()}, +\code{\link{reportServer}()} +} +\concept{{Report}} diff --git a/man/resultTableServer.Rd b/man/resultTableServer.Rd index 4cccbb4f..46633384 100644 --- a/man/resultTableServer.Rd +++ b/man/resultTableServer.Rd @@ -8,6 +8,7 @@ resultTableServer( id, df, colDefsInput, + details = data.frame(), selectedCols = NULL, sortedCols = NULL, elementId = NULL, @@ -23,6 +24,8 @@ resultTableServer( \item{colDefsInput}{named list of reactable::colDefs} +\item{details}{The details of the results such as cohort names and database names} + \item{selectedCols}{string vector of columns the reactable should display to start by default. Defaults to ALL if not specified.} \item{sortedCols}{string vector of columns the reactable should sort by by default. Defaults to no sort if not specified.} @@ -43,6 +46,14 @@ shiny module server Result Table Server } \seealso{ +Other {Utils}: +\code{\link{createCustomColDefList}()}, +\code{\link{datasourcesHelperFile}()}, +\code{\link{datasourcesServer}()}, +\code{\link{datasourcesViewer}()}, +\code{\link{makeButtonLabel}()}, +\code{\link{resultTableViewer}()} + Other {Utils}: \code{\link{createCustomColDefList}()}, \code{\link{datasourcesHelperFile}()}, diff --git a/man/resultTableViewer.Rd b/man/resultTableViewer.Rd index 00ae4af9..97b3a3d4 100644 --- a/man/resultTableViewer.Rd +++ b/man/resultTableViewer.Rd @@ -4,12 +4,18 @@ \alias{resultTableViewer} \title{Result Table Viewer} \usage{ -resultTableViewer(id = "result-table", downloadedFileName = NULL) +resultTableViewer( + id = "result-table", + downloadedFileName = NULL, + boxTitle = "Table" +) } \arguments{ \item{id}{string} \item{downloadedFileName}{string, desired name of downloaded data file. can use the name from the module that is being used} + +\item{boxTitle}{the title added to the box} } \value{ shiny module UI @@ -18,6 +24,14 @@ shiny module UI Result Table Viewer } \seealso{ +Other {Utils}: +\code{\link{createCustomColDefList}()}, +\code{\link{datasourcesHelperFile}()}, +\code{\link{datasourcesServer}()}, +\code{\link{datasourcesViewer}()}, +\code{\link{makeButtonLabel}()}, +\code{\link{resultTableServer}()} + Other {Utils}: \code{\link{createCustomColDefList}()}, \code{\link{datasourcesHelperFile}()}, diff --git a/man/sccsHelperFile.Rd b/man/sccsHelperFile.Rd deleted file mode 100644 index f2bc86ab..00000000 --- a/man/sccsHelperFile.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sccs-main.R -\name{sccsHelperFile} -\alias{sccsHelperFile} -\title{The location of the description module helper file} -\usage{ -sccsHelperFile() -} -\value{ -string location of the description helper file -} -\description{ -The location of the description module helper file -} -\details{ -Returns the location of the description helper file -} diff --git a/man/sccsView.Rd b/man/sccsView.Rd deleted file mode 100644 index 5a01ddbd..00000000 --- a/man/sccsView.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sccs-main.R -\name{sccsView} -\alias{sccsView} -\title{SCCS shiny module UI code} -\usage{ -sccsView(id = "sccs-module") -} -\arguments{ -\item{id}{id for module} -} -\description{ -Load the ui for the sccs module -} diff --git a/man/timeDistributionsView.Rd b/man/timeDistributionsView.Rd index 0b1f586c..ea088476 100644 --- a/man/timeDistributionsView.Rd +++ b/man/timeDistributionsView.Rd @@ -12,3 +12,27 @@ timeDistributionsView(id) \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{visitContextView}()} +} +\concept{{CohortDiagnostics}} diff --git a/man/visitContextView.Rd b/man/visitContextView.Rd index 0e1cb542..8ae1c96f 100644 --- a/man/visitContextView.Rd +++ b/man/visitContextView.Rd @@ -12,3 +12,27 @@ visitContextView(id) \description{ Use for customizing UI } +\seealso{ +Other {CohortDiagnostics}: +\code{\link{cohortCountsModule}()}, +\code{\link{cohortCountsView}()}, +\code{\link{cohortDefinitionsModule}()}, +\code{\link{cohortDefinitionsView}()}, +\code{\link{cohortDiagCharacterizationView}()}, +\code{\link{cohortDiagnosticsHelperFile}()}, +\code{\link{cohortDiagnosticsServer}()}, +\code{\link{cohortDiagnosticsView}()}, +\code{\link{cohortOverlapView}()}, +\code{\link{compareCohortCharacterizationView}()}, +\code{\link{conceptsInDataSourceView}()}, +\code{\link{createCdDatabaseDataSource}()}, +\code{\link{databaseInformationView}()}, +\code{\link{getCirceRenderedExpression}()}, +\code{\link{getEnabledCdReports}()}, +\code{\link{incidenceRatesView}()}, +\code{\link{inclusionRulesView}()}, +\code{\link{indexEventBreakdownView}()}, +\code{\link{orpahanConceptsView}()}, +\code{\link{timeDistributionsView}()} +} +\concept{{CohortDiagnostics}} diff --git a/tests/resources/cDatabase/databaseFile.sqlite b/tests/resources/cDatabase/databaseFile.sqlite new file mode 100644 index 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