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m_BVM_p1.R
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# Model diagnostics tab - UI ("master") module ------------------------------ -----------------------------------------
MA_model_diagnostics_tab_UI <- function(id) {
ns <- NS(id)
uiOutput(ns("MA_model_diagnostics_tab_ui"))
}
MA_model_diagnostics_tab_renderUI_server <- function(id, SA_indicator) {
moduleServer(
id,
function(input, output, session) {
output$MA_model_diagnostics_tab_ui <- renderUI({
ns <- session$ns
SA_indicator <- SA_indicator$SA_indicator
# Main model ------------------------
tagList(
h4("Main model"),
h3("Sampler diagnostics"),
sampler_diagnostics_table_UI(id = "MA_model_id"),
br(),
h3("R-hat statistics"),
MA_rhat_table_UI(id = "MA_model_id"),
br(),
h3("Posterior density plots"),
dropdownButton(
model_posterior_density_plots_settings_UI(id = "MA_model_id"),
circle = TRUE, status = "danger",
icon = icon("gear"), width = "300px",
tooltip = tooltipOptions(title = "Click to customise plot")
),
model_posterior_density_plots_UI(id = "MA_model_id"),
br(),
h3("Trace plots"),
dropdownButton(
model_trace_plots_settings_UI(id = "MA_model_id"),
circle = TRUE, status = "danger",
icon = icon("gear"), width = "300px",
tooltip = tooltipOptions(title = "Click to customise plot")
),
model_trace_plots_UI(id = "MA_model_id"),
# SA ------------------------
if (SA_indicator == TRUE) {
tagList(
h4("Sensitivity analysis"),
sampler_diagnostics_table_UI(id = "SA_MA_model_id"),
br(),
h3("R-hat statistics"),
MA_rhat_table_UI(id = "SA_MA_model_id"),
br(),
h3("Posterior density plots"),
dropdownButton(
model_posterior_density_plots_settings_UI(id = "SA_MA_model_id"),
circle = TRUE, status = "danger",
icon = icon("gear"), width = "300px",
tooltip = tooltipOptions(title = "Click to customise plot")
),
model_posterior_density_plots_UI(id = "SA_MA_model_id"),
br(),
h3("Trace plots"),
dropdownButton(
model_trace_plots_settings_UI(id = "SA_MA_model_id"),
circle = TRUE, status = "danger",
icon = icon("gear"), width = "300px",
tooltip = tooltipOptions(title = "Click to customise plot")
),
model_trace_plots_UI(id = "SA_MA_model_id")
)
} # end of if
) # end of taglist
})
}
)
}
# Parameter estimates tab - UI ("master") module ---------------------------- -----------------------------------------------------------------------------------
MA_parameter_estimates_tab_UI <- function(id) {
ns <- NS(id)
uiOutput(ns("MA_parameter_estimates_tab_ui"))
}
MA_parameter_estimates_tab_renderUI_server <- function(id, SA_indicator) {
moduleServer(
id,
function(input, output, session) {
output$MA_parameter_estimates_tab_ui <- renderUI({
print(SA_indicator$SA_indicator)
ns <- session$ns
tagList(
dropdownButton(
MA_parameter_estimates_table_menu_UI(id = "MA_model_id"),
circle = TRUE, status = "danger",
icon = icon("gear"), width = "300px",
tooltip = tooltipOptions(title = "Click to customise plot")
),
MA_parameter_estimates_table_UI(id = "MA_model_id"),
br(),
br(),
# SA ------------------------
if (SA_indicator$SA_indicator == TRUE) {
tagList(
h4("Sensitivity analysis"),
dropdownButton(
MA_parameter_estimates_table_menu_UI(id = "SA_MA_model_id"),
circle = TRUE, status = "danger",
icon = icon("gear"), width = "300px",
tooltip = tooltipOptions(title = "Click to customise plot")
),
MA_parameter_estimates_table_UI(id = "SA_MA_model_id")
)
}
)
})
}
)
}
# BVM - Forest Plots ---------------------------------------------------------- -----------------------------
MA_forest_plots_UI <- function(id) {
ns <- NS(id)
tagList(
fluidRow(
splitLayout(cellWidths = c("50%", "50%"), #
plotOutput(ns("forest_plot_sens"))
,plotOutput(ns("forest_plot_spec")))
),
radioButtons(ns("filetype_forest"),
label="Select plot format",
choices=list("png", "PDF")),
downloadButton(ns("forest_plot_sens_download"), "Download Sensitivity Forest Plot"),
downloadButton(ns("forest_plot_spec_download"), "Download Specificity Forest Plot")
)
}
MA_forest_plots_server <- function(id, data) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
### forest plot for sens ----------------------------------
forest_plot_sens_obj <- function() {
X <- data()
D <- mada::madad(X, correction.control = "any")
plot <- forest(D,
type = "sens",
snames = X$author,
xlab = "Sensitivity",
main = "Forest plot of sensitivity")
plot
}
# Output plot object
output$forest_plot_sens <- renderPlot({
forest_plot_sens_obj()
})
# Download plot object
output$forest_plot_sens_download <- downloadHandler(
filename = function(){
paste("Sensitivity_forest_plot", input$filetype_forest, sep=".")
},
content = function(file) {
if (input$filetype_forest == "png") {
png(file)
forest_plot_sens_obj()
dev.off()
}
else {
pdf(file)
forest_plot_sens_obj()
dev.off()
}
}
)
### forest plot for spec ---- ------------------------------
forest_plot_spec_obj <- function() {
X <- data()
D <- madad(X, correction.control = "any")
plot <- forest(D,
type = "spec",
snames = X$author,
xlab = "Specificity",
main = "Forest plot of specificity")
plot
}
# Output plot object
output$forest_plot_spec <- renderPlot({
forest_plot_spec_obj()
})
# Download plot object
output$forest_plot_spec_download <- downloadHandler(
filename = function(){
paste("Specificity_forest_plot", input$filetype_forest, sep=".")
},
content = function(file) {
if (input$filetype_forest == "png") {
png(file)
forest_plot_spec_obj()
dev.off()
}
else {
pdf(file)
forest_plot_spec_obj()
dev.off()
}
}
)
}
)
}
# BVM - Table - Parameters for revman ----------------------------- ---------------------------------------------------------------------------------------------------
MA_revman_plots_UI <- function(id) {
ns <- NS(id)
tagList(
tableOutput(ns("revman")),
downloadButton(ns("revman_download"), "Download Table")
)
}
MA_revman_plots_server <- function(id,
data,
draws) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
revman_obj <- reactive({
mod <- draws()
X <- data()
N <- length(X$TP)
logit_Sens <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lSe"))$summary[5]
logit_Spec <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lSp"))$summary[5]
between_study_sd_se <- (summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma"))$summary[1,5])
between_study_sd_sp <- (summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma"))$summary[2,5])
between_study_var_se <- ( between_study_sd_se )^2
between_study_var_sp <- ( between_study_sd_sp )^2
se_logit_sens <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lSe"))$summary[2]
se_logit_spec <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lSp"))$summary[2]
correlation <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Omega"))$summary[2, 5]
covariance <- correlation*between_study_sd_se*between_study_sd_sp
Z = data.frame(estimate = c(logit_Sens, logit_Spec,
between_study_var_se, between_study_var_sp,
correlation,
se_logit_sens, se_logit_spec,
covariance
),
row.names=c("logit(sens)", "logit(spec)",
"Variance logit(sens)", "Variance logit(spec)",
"Correlation",
"SE(logit(sens))", "SE(logit(spec))",
"Covariance")
)
# Create a matrix to store the parameter estimates
s.matrix <- matrix(nrow=8, ncol=2)
s.matrix[1,1] <- "E(logitSe)"
s.matrix[2,1] <- "E(logitSp)"
s.matrix[3,1] <- "Var(logitSe)"
s.matrix[4,1] <- "Var(logitSp)"
s.matrix[5,1] <- "Corr(logits)"
s.matrix[6,1] <- "SE(E(logitSe))"
s.matrix[7,1] <- "SE(E(logitSp))"
s.matrix[8,1] <- "Cov(Es)"
s.matrix[1,2] <- sprintf('%4.6f', Z[1,])
s.matrix[2,2] <- sprintf('%4.6f', Z[2,])
s.matrix[3,2] <- sprintf('%4.6f', Z[3,])
s.matrix[4,2] <- sprintf('%4.6f', Z[4,])
s.matrix[5,2] <- sprintf('%4.6f', Z[8,])
s.matrix[6,2] <- sprintf('%4.6f', Z[6,])
s.matrix[7,2] <- sprintf('%4.6f', Z[7,])
s.matrix[8,2] <- sprintf('%4.6f', Z[5,])
#Name the columns of the matrix
colnames(s.matrix) <- c("Parameter", "Estimate (Posterior Median)")
#Only the rows of s.matrix where statticks=T will be displayed
s.matrix
})
output$revman <- renderTable({
req(draws(), cancelOutput = TRUE)
revman_obj()
}, sanitize.text.function = function(x) x)
output$revman_download <- downloadHandler(
filename = "parameters_for_revman.csv",
content = function(file){
write.csv(revman_obj(), file, sep=",", row.names=FALSE)
}
)
}
)
}
# BVM - Plot of prior distributions --------------- ---------------------------------------------------------------------------
MA_model_priors_plot_server <- function(id,
draws_PO) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
priors_plot_obj <- reactive({
validate(
need(!(is.null(draws_PO())), "Please run prior model to display plot")
)
req(draws_PO(), cancelOutput = TRUE)
params <- rstan::extract(draws_PO())
se <- params$Se
sp <- params$Sp
corr <- params$Omega[, 1, 2]
sd_se <- params$sigma[, 1]
sd_sp <- params$sigma[, 2]
n_samps <- (2000-200)*4
data <- tibble(
Samples = c(se, sp, corr, sd_se, sd_sp),
Parameter = c(rep("Sensitivity", n_samps),
rep("Specificity", n_samps),
rep("Between-study correlation", n_samps),
rep("Between-study SD for logit(sensitivity)", n_samps),
rep("Between-study SD for logit(specificity)", n_samps)))
g <- ggplot(data = data, aes(x=Samples)) +
geom_density(alpha = 0.50) +
theme_bw() +
theme(legend.position = "none") +
facet_wrap( ~ Parameter, scales = "free") +
xlab(" ") +
ylab(" ")
g
})
# output plot
observe({
output$model_priors_plot <- renderPlot({
validate(
need(!(is.null(draws_PO())), "Please run prior model to display plot")
)
req(draws_PO(), cancelOutput = TRUE)
priors_plot_obj()
},
height = input$model_priors_plot_dimension_slider_height,
width = input$model_priors_plot_dimension_slider_width)
})
# Download ggplot object
output$download_priors_plot <- downloadHandler(
filename = function(){
paste("priors_plot.png")
},
content = function(file) {
{ggsave(file,
priors_plot_obj(),
width = input$priors_plot_dl_width,
height = input$priors_plot_dl_height,
dpi = input$priors_plot_dl_dpi,
units = "in"
)}
}
)
}
)
}
# BVM - table of prior distributions -------------------------------- -----------------------------------------------------
# UI function
MA_model_priors_table_UI <- function(id) {
ns <- NS(id)
tagList(
tableOutput(outputId = ns("model_priors_table")),
p("NOTE: The default priors are:"),
p("For logit sensitivities and specificities - normal distribution with mean 0 and SD of 1, equivalent to a 95% prior interval of (0.05, 0.95) "),
p("For between-study SD's of logit sensitivities and specificities - truncated (at 0) normal distribution with mean 0 and SD of 1, equivalent to a 95% prior interval of (0.03, 2.25) "),
p("For between-study correlation between study-specific logit sensitivities and specificities - LKJ(2) prior, equivalent to 95% prior interval of (-0.8, 0.8)")
)
}
# server function
MA_model_priors_table_server <- function(id, draws_PO) {
moduleServer(
id,
function(input, output, session) {
output$model_priors_table <- renderTable({
validate(
need(!(is.null(draws_PO())), "Please run prior model to display plot")
)
req(draws_PO(), cancelOutput = TRUE)
model <- req(draws_PO())
Sens <- rstan::summary(model, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary
Spec <- rstan::summary(model, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary
correlation <- rstan::summary(model, probs = c(0.025, 0.5, 0.975), pars = c("Omega"))$summary
between_study_sd <- rstan::summary(model, probs = c(0.025, 0.5, 0.975), pars = c("sigma"))$summary
Z = data.frame(estimate = c(Sens[, "50%"], Spec[, "50%"],
1 - Spec[, "50%"],
correlation[, "50%"][3],
between_study_sd[, "50%"][1], between_study_sd[, "50%"][2]),
sd = c(Sens[, "sd"], Spec[, "sd"],
Spec[, "sd"],
correlation[, "sd"][3],
between_study_sd[, "sd"][1], between_study_sd[, "sd"][2]),
lci = c(Sens[, "2.5%"], Spec[, "2.5%"],
1 - Spec[, "97.5%"],
correlation[, "2.5%"][3],
between_study_sd[, "2.5%"][1], between_study_sd[, "2.5%"][2]),
uci = c(Sens[, "97.5%"], Spec[, "97.5%"],
1 - Spec[, "2.5%"],
correlation[, "97.5%"][3],
between_study_sd[, "97.5%"][1], between_study_sd[, "97.5%"][2]),
row.names=c("Sensitivity", "Specifcity", "FPR",
"Correlation",
"sd_sens", "sd_spec")
)
# Create a matrix to store the parameter estimates
nrow <- 7
s.matrix <- matrix(nrow=nrow, ncol=6)
s.matrix[1,1] <- paste0("Sensitivity ", "( ", paste0("logit", HTML("<sup>-1</sup>")), "(", HTML("μ<sub>1</sub>"), ")", ")")
s.matrix[2,1] <- paste0("Specificity ", "( ", paste0("logit", HTML("<sup>-1</sup>")), "(", HTML("μ<sub>0</sub>"), ")", ")")
s.matrix[3,1] <- "False Positive Rate (1 - Specificity)"
s.matrix[4,1] <- paste0("Between-study Correlation" , "( ", HTML("ρ"), " )")
s.matrix[5,1] <- paste0("Between-study SD for logit(Sensitivity) ", "( ", HTML("σ<sub>1</sub>"), " )")
s.matrix[6,1] <- paste0("Between-study SD for logit(Specificity) ", "( ", HTML("σ<sub>0</sub>"), " )")
for (i in 1:4) {
for (j in 1:(nrow)-1) {
s.matrix[j,i+1] <- sprintf('%4.3f', Z[j,i])
}
}
s.matrix[, 6] <- paste0("(", s.matrix[, 4], ", ", s.matrix[, 5], ")")
s.matrix[nrow, 1:6] <- ""
s.matrix <- s.matrix[, c(1, 2, 3, 6)]
#Name the columns of the matrix
colnames(s.matrix) <- c("Parameter", "Prior Median", "Standard Deviation", "95% Prior Interval")
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
return(s.matrix)
}, sanitize.text.function = function(x) x)
})
}
# BVM - Trace plots ------------------------------------------------------------ --------------------------------------------------------------
MA_model_trace_plots_server <- function(id, draws, data) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
trace_plots_obj <- reactive({
req(data(), draws(), cancelOutput = TRUE)
X <- data()
mod <- draws()
plot <- stan_trace(mod,
nrow = input$trace_plot_nrow,
ncol = input$trace_plot_ncol,
pars = c("Se", "Sp", "Sigma", "se", "sp"))
plot
})
observe({
output$trace_plots <- renderPlot({
trace_plots_obj()
}, height = input$trace_plot_dimension_slider_height,
width = input$trace_plot_dimension_slider_width)
})
# Download ggplot object
output$download_trace_plot <- downloadHandler(
filename = function(){
paste("trace_plot.png")
},
content = function(file) {
{ggsave(file, trace_plots_obj(),
width = input$trace_plot_dl_width,
height = input$trace_plot_dl_height,
dpi = input$trace_plot_dl_dpi,
units = "in"
)}
}
)
}
)
}
# BVM - Posterior density plots ---------------------------------------------- --------------------------------------------------------------
MA_model_posterior_density_plots_server <- function(id, draws, data) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
posterior_density_plots_obj <- reactive({
req(data(), draws(), cancelOutput = TRUE)
X <- data()
mod <- draws()
plot <- stan_dens(mod,
nrow = input$posterior_density_plot_nrow,
ncol = input$posterior_density_plot_ncol,
pars = c("Se", "Sp", "Sigma", "se", "sp"))
plot
})
observe({
output$posterior_density_plots <- renderPlot({
posterior_density_plots_obj()
}, height = input$posterior_density_plot_dimension_slider_height,
width = input$posterior_density_plot_dimension_slider_width)
})
# Download ggplot object
output$download_dens_plot <- downloadHandler(
filename = function(){
paste("posterior_density_plot.png")
},
content = function(file) {
{ggsave(file, posterior_density_plots_obj(),
width = input$dens_plot_dl_width,
height = input$dens_plot_dl_height,
dpi = input$dens_plot_dl_dpi,
units = "in"
)}
}
)
}
)
}
# BVM - table which displays observed data and study weights from model ("study-level outcomes") ----------- --------------------------------
MA_data_table_UI <- function(id) {
ns <- NS(id)
DT::dataTableOutput(outputId = ns("data_table"))
}
MA_data_table_settings_UI <- function(id) {
ns <- NS(id)
downloadButton(outputId = ns("data_table_download"), label = "Download Table")
}
MA_data_table_server <- function(id, data, draws) { # "mod" is the rstan file which is reactive.
moduleServer(
id,
function(input, output, session) {
data_table_obj <- reactive({
X <- data()
model <- draws()
b <- study_level_outcomes(X) # get sens, spec for each trial
bb <- data.frame(Author=X$author, Year=X$year, TP=X$TP, FN=X$FN, FP=X$FP,
TN=X$TN, N=b$N, Sens=b$Sens, Spec=b$Spec)
bb$Sens <- sprintf('%4.3f', bb$Sens) # restrict number of figures after decimal place for sens
bb$Spec <- sprintf('%4.3f', bb$Spec)
X <- MA_weights(X, model)
bb$Weight_Sens <- sprintf('%4.3f', X$pctse)
bb$Weight_Spec <- sprintf('%4.3f', X$pctsp)
bb <- data.frame(bb)
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
return(bb)
})
output$data_table <- DT::renderDataTable({
options(DT.options = list(pageLength = 30,
autoWidth = TRUE,
scrollX=T))
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
return(DT::datatable( data_table_obj() ))
})
output$data_table_download <- downloadHandler(
filename = function(){
paste("table.csv")
},
content = function(file) {
write.csv(data_table_obj(), file, sep=",", row.names=FALSE)
}
)
}
)
}
# BVM - Prior distribution options ------------------------------------------- --------------------------------------
# UI function to input prior distribution options
MA_priors_options_inputModule_UI <- function(id) {
ns <- NS(id)
uiOutput(ns("priors_options"))
}
# server-side function to input prior dist. options - outputs a list of inputs which can be called within the shiny program
MA_priors_options_inputModule_renderUI_server <- function(id) {
moduleServer(
id,
function(input, output, session) {
output$priors_options <- renderUI({
ns <- session$ns
div(id = ns("priors_options_div"),
if (input$p_scale_priors_indicator == FALSE) {
tagList(
h4("Prior Distributions:"),
numericInput(inputId = ns("MA_prior_mean_sens_mu"), label=h5("Prior mean of pooled logit(sensitivity) - mean"), value = 0),
numericInput(inputId = ns("MA_prior_mean_sens_sd"), label=h5("Prior mean of pooled logit(sensitivity) - SD"), value = 1.5, min = 0),
numericInput(inputId = ns("MA_prior_mean_spec_mu"), label=h5("Prior mean of pooled logit(specificity) - mean"), value = 0),
numericInput(inputId = ns("MA_prior_mean_spec_sd"), label=h5("Prior mean of pooled logit(specificity) - SD"), value = 1.5, min = 0),
numericInput(inputId = ns("MA_prior_SD_sens_sd"), label=h5("Prior for between-study SD of logit(sensitivity) - SD"), value = 1, min = 0),
numericInput(inputId = ns("MA_prior_SD_spec_sd"), label=h5("Prior for between-study SD of logit(specificity) - SD"), value = 1, min = 0)
)
}
else {
tagList(
h5("Sensitivity - 95% credible interval:"),
numericInputRow(inputId = ns("MA_prior_sens_lower95"), label=h5("Lower interval (2.5th percentile)"), value = 0.05, min = 0, max = 1),
numericInputRow(inputId = ns("MA_prior_sens_upper95"), label=h5("Upper interval (97.5th percentile)"), value = 0.95, min = 0, max = 1),
h5("Specificity - 95% credible interval:"),
numericInputRow(inputId = ns("MA_prior_spec_lower95"), label=h5("Lower interval (2.5th percentile)"), value = 0.05, min = 0, max = 1),
numericInputRow(inputId = ns("MA_prior_spec_upper95"), label=h5("Upper interval (97.5th percentile)"), value = 0.95, min = 0, max = 1),
h5("Standard deviation (SD) priors:"),
numericInput(inputId = ns("MA_prior_SD_sens_sd"), label=h5("Prior for between-study SD of logit(sensitivity) - SD"), value = 1, min = 0),
numericInput(inputId = ns("MA_prior_SD_spec_sd"), label=h5("Prior for between-study SD of logit(specificity) - SD"), value = 1, min = 0)
)
}
)
})
observeEvent(input$reset,{
shinyjs::reset("priors_options_div")
})
outputOptions(output, "priors_options", suspendWhenHidden = FALSE)
}
)
}
MA_priors_options_inputModule_server <- function(id) {
moduleServer(
id,
function(input, output, session) {
vals <- reactiveValues()
observe({
if (input$p_scale_priors_indicator == FALSE) {
observe({vals$MA_prior_mean_sens_mu <- input$MA_prior_mean_sens_mu})
observe({vals$MA_prior_mean_sens_sd <- input$MA_prior_mean_sens_sd})
observe({vals$MA_prior_mean_spec_mu <- input$MA_prior_mean_spec_mu})
observe({vals$MA_prior_mean_spec_sd <- input$MA_prior_mean_spec_sd})
observe({vals$MA_prior_SD_sens_sd <- input$MA_prior_SD_sens_sd})
observe({vals$MA_prior_SD_spec_sd <- input$MA_prior_SD_spec_sd})
}
else {
observe({vals$MA_prior_sens_lower95 <- input$MA_prior_sens_lower95})
observe({vals$MA_prior_sens_upper95 <- input$MA_prior_sens_upper95})
observe({vals$MA_prior_spec_lower95 <- input$MA_prior_spec_lower95})
observe({vals$MA_prior_spec_upper95 <- input$MA_prior_spec_upper95})
observe({vals$MA_prior_SD_sens_sd <- input$MA_prior_SD_sens_sd})
observe({vals$MA_prior_SD_spec_sd <- input$MA_prior_SD_spec_sd})
}
})
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
return(vals)
}
)
}
# BVM - Parameter estimates table --------------------------------------------------- ----------------------------------------
# UI function for parameter estimates table
MA_parameter_estimates_table_UI <- function(id) {
ns <- NS(id)
tagList(
tableOutput(ns("table")),
downloadButton(ns("download_table"), "Download Table")
)
}
# UI function to output the options for summary results/ parameter estimates table (to let the user hide specific rows of the table)
MA_parameter_estimates_table_menu_UI <- function(id) {
ns <- NS(id)
checkboxGroupInput(inputId= ns("statscheck"),
label=h4("Options"),
choices=list("Sensitivity"=1, "Specificity"=2, "False Positive Rate"=3, "Between-study Correlation"=4,
"Between-Study SD"=5, "HSROC parameters"=6, "Diagnostic Odds Ratio"=7, "Likelihood Ratios"=8),
selected=list(1,2,3,4,5,6,7,8))
}
# Server function for parameter estimates table
MA_parameter_estimates_table_server <- function(id,
data,
draws) {
moduleServer(
id,
function(input, output, session) {
table_obj <- reactive({
req(data(), draws(), cancelOutput = TRUE)
mod <- draws()
logit_Sens <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lSe"))$summary
logit_Spec <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lSp"))$summary
Sens <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary
Spec <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary
correlation <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Omega"))$summary
DOR <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("DOR"))$summary
LRp <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("LRp"))$summary
LRn <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("LRn"))$summary
Theta <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("theta"))$summary
Lambda <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("lambda"))$summary
beta <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("beta"))$summary
sigma2_theta <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma_sq_theta"))$summary
sigma2_alpha <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma_sq_alpha"))$summary
between_study_sd <- summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("sigma"))$summary
Z = data.frame(estimate = c(logit_Sens[, "50%"], logit_Spec[, "50%"],
Sens[, "50%"], Spec[, "50%"],
1 - Spec[, "50%"],
DOR[, "50%"], LRp[, "50%"] , LRn[, "50%"],
correlation[, "50%"][3], between_study_sd[, "50%"][1], between_study_sd[, "50%"][2],
Theta[, "50%"], Lambda[, "50%"], beta[, "50%"], sigma2_theta[, "50%"], sigma2_alpha[, "50%"]),
sd = c(logit_Sens[, "sd"], logit_Spec[, "sd"],
Sens[, "sd"], Spec[, "sd"],
Spec[, "sd"],
DOR[, "sd"], LRp[, "sd"] , LRn[, "sd"],
correlation[, "sd"][3], between_study_sd[, "sd"][1], between_study_sd[, "sd"][2],
Theta[, "sd"], Lambda[, "sd"], beta[, "sd"], sigma2_theta[, "sd"], sigma2_alpha[, "sd"]),
lci = c(logit_Sens[, "2.5%"], logit_Spec[, "2.5%"],
Sens[, "2.5%"], Spec[, "2.5%"],
1 - Spec[, "97.5%"],
DOR[, "2.5%"], LRp[, "2.5%"] , LRn[, "2.5%"],
correlation[, "2.5%"][3], between_study_sd[, "2.5%"][1], between_study_sd[, "2.5%"][2],
Theta[, "2.5%"], Lambda[, "2.5%"], beta[, "2.5%"], sigma2_theta[, "2.5%"], sigma2_alpha[, "2.5%"]),
uci = c(logit_Sens[, "97.5%"], logit_Spec[, "97.5%"],
Sens[, "97.5%"], Spec[, "97.5%"],
1 - Spec[, "2.5%"],
DOR[, "97.5%"], LRp[, "97.5%"] , LRn[, "97.5%"],
correlation[, "97.5%"][3], between_study_sd[, "97.5%"][1], between_study_sd[, "97.5%"][2],
Theta[, "97.5%"], Lambda[, "97.5%"], beta[, "97.5%"], sigma2_theta[, "97.5%"], sigma2_alpha[, "97.5%"]),
row.names=c("Logit Sens", "Logit Spec",
"Sensitivity", "Specifcity",
"FPR",
"DOR", "LR+" , "LR-",
"Correlation", "sd_sens", "sd_spec",
"Theta", "Lambda", "beta", "sigma2_theta", "sigma2_alpha")
)
# Create a matrix to store the parameter estimates
nrow <- 17
s.matrix <- matrix(nrow=nrow, ncol=6)
s.matrix[1,1] <- paste0("logit(sensitivity) ", "( ", HTML("μ<sub>1</sub>"), " )")
s.matrix[2,1] <- paste0("logit(specificity) ", "( ", HTML("μ<sub>0</sub>"), " )")
s.matrix[3,1] <- paste0("Sensitivity ", "( ", paste0("logit", HTML("<sup>-1</sup>")), "(", HTML("μ<sub>1</sub>"), ")", ")")
s.matrix[4,1] <- paste0("Specificity ", "( ", paste0("logit", HTML("<sup>-1</sup>")), "(", HTML("μ<sub>0</sub>"), ")", ")")
s.matrix[5,1] <- "False Positive Rate (1 - Specificity)"
s.matrix[6,1] <- "Diagnostic Odds Ratio"
s.matrix[7,1] <- "Likelihood Ratio +ve"
s.matrix[8,1] <- "Likelihood Ratio -ve"
s.matrix[9,1] <- paste0("Between-study Correlation" , "( ", HTML("ρ"), " )")
s.matrix[10,1] <- paste0("Between-study SD for logit(Sensitivity) ", "( ", HTML("σ<sub>1</sub>"), " )")
s.matrix[11,1] <- paste0("Between-study SD for logit(Specificity) ", "( ", HTML("σ<sub>0</sub>"), " )")
# HSROC model parameters
s.matrix[12,1] <- paste0("Cutpoint parameter ", "( ", HTML("Θ"), " )")
s.matrix[13,1] <- paste0("Accuracy parameter ", "( ", HTML("Λ"), " )")
s.matrix[14,1] <- paste0("Shape parameter ", "( ", HTML("β"), " )")
s.matrix[15,1] <- paste0("SD of cutpoint parameter ", "( ", HTML("σ<sub>θ</sub>"), " )")
s.matrix[16,1] <- paste0("SD of accuracy parameter ", "( ", HTML("σ<sub>α</sub>"), " )")
for (i in 1:4) {
for (j in 1:(nrow)-1) {
s.matrix[j,i+1] <- sprintf('%4.3f', Z[j,i])
}
}
s.matrix[, 6] <- paste0("(", s.matrix[, 4], ", ", s.matrix[, 5], ")")
s.matrix[nrow, 1:6] <- ""
s.matrix <- s.matrix[, c(1, 2, 3, 6)]
#Name the columns of the matrix
colnames(s.matrix) <- c("Parameter", "Posterior Median", "Standard Deviation", "95% Posterior Interval")
#Conditions to display which statistics are shown in the table
#Start with a logical vector of false and replace ths with true if the corresponding box is ticked
statticks <- logical(length=nrow) # default is false
statticks[nrow] <- TRUE # always have the bottom empty row showing
# which rows are displayed will depend on the options selected
if ('1' %in% input$statscheck) {statticks[1] <- TRUE} # sens
if ('2' %in% input$statscheck) {statticks[2] <- TRUE} # spec
if ('1' %in% input$statscheck) {statticks[3] <- TRUE}
if ('2' %in% input$statscheck) {statticks[4] <- TRUE}
if ('3' %in% input$statscheck) {statticks[5] <- TRUE} # fp
if ('4' %in% input$statscheck) {statticks[9] <- TRUE} # between-study corr
if ('5' %in% input$statscheck) {statticks[10:11] <- TRUE} # between-study SD
if ('6' %in% input$statscheck) {statticks[12:16] <- TRUE} # HSROC model parameters
if ('7' %in% input$statscheck) {statticks[6] <- TRUE} # DOR
if ('8' %in% input$statscheck) {statticks[7:8] <- TRUE} # Likelihood ratios
#Only the rows of s.matrix where statticks=T will be displayed
s.matrix <- s.matrix[statticks,]
# Run the Garabage Collector to Ensure any excess memory used by stan is freed
gc()
return(s.matrix)
})
# output table
output$table <- renderTable({
req(data(), draws(), cancelOutput = TRUE)
table_obj()
}, sanitize.text.function = function(x) x)
# download table
output$download_table <- downloadHandler(
filename = "parameter_estimates_table.csv",
content = function(file) {
write.csv(table_obj(), file, row.names = TRUE)
}
)
}