diff --git a/404.html b/404.html index 3d52a5e8..30a08e66 100644 --- a/404.html +++ b/404.html @@ -27,7 +27,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/LICENSE.html b/LICENSE.html index 4c25ad45..b5be911a 100644 --- a/LICENSE.html +++ b/LICENSE.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a00_introduction.html b/articles/a00_introduction.html index 4a27425d..c4b588b8 100644 --- a/articles/a00_introduction.html +++ b/articles/a00_introduction.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a01_building_base_cohorts.html b/articles/a01_building_base_cohorts.html index 6ad0f194..a73d0a56 100644 --- a/articles/a01_building_base_cohorts.html +++ b/articles/a01_building_base_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -130,7 +130,7 @@ Demographic based cohort creation median_start_age = median(age), max_start_age = max(age)) #> # Source: SQL [1 x 3] -#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/Rtmp8sghhQ/file1c2d736042a2.duckdb] +#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/Rtmp9pveQh/file1caf702c9ae5.duckdb] #> min_start_age median_start_age max_start_age #> <int> <dbl> <int> #> 1 17 18 18 @@ -141,7 +141,7 @@ Demographic based cohort creation median_start_age = median(age), max_start_age = max(age)) #> # Source: SQL [1 x 3] -#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/Rtmp8sghhQ/file1c2d736042a2.duckdb] +#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/Rtmp9pveQh/file1caf702c9ae5.duckdb] #> min_start_age median_start_age max_start_age #> <int> <dbl> <int> #> 1 31 57 65 diff --git a/articles/a02_cohort_table_requirements.html b/articles/a02_cohort_table_requirements.html index 61cc5830..6c536a85 100644 --- a/articles/a02_cohort_table_requirements.html +++ b/articles/a02_cohort_table_requirements.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -127,7 +127,7 @@ Keep only the first record per pe summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates changes to cohort 1 (acetaminophen +The flow chart above illustrates changes to cohort 1 (acetaminophen users) when restricted to only the first record for each individual. While the number of individuals remains unchanged, 6,785 records are excluded. @@ -144,7 +144,7 @@ Keep only a specific range of rec summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only the first five records for each individual. While the number of individuals remains unchanged, 6,785 records are excluded. @@ -161,7 +161,7 @@ Keep only the last record per pers summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates changes to cohort 1 when restricted +The flow chart above illustrates changes to cohort 1 when restricted to only the last record for each individual. While the number of individuals remains unchanged, 6,785 records are excluded. @@ -179,7 +179,7 @@ Keep only records within a date r summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to a specified date range. 1,948 individuals and 8,660 records are excluded. @@ -198,7 +198,7 @@ Kee summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -Cohort 1 includes 2,580 individuals, so none were excluded due to the +Cohort 1 includes 2,580 individuals, so none were excluded due to the minimum cohort size restriction of 1,000. @@ -216,7 +216,7 @@ Running multiple requirementssummary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include the first record of each individual over a specified date range. 2,529 individuals and 9,314 records are excluded. diff --git a/articles/a03_require_demographics.html b/articles/a03_require_demographics.html index d84b0650..19304e81 100644 --- a/articles/a03_require_demographics.html +++ b/articles/a03_require_demographics.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -126,7 +126,7 @@ Restrict cohort by agesummary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 (users of +The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when restricted to only include individuals aged 18 to 90. 226 individuals and 2,863 records were excluded. The variable ‘cohort_start_date’ is used so that individuals are @@ -144,7 +144,7 @@ Restrict cohort by sexsummary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include ‘female’ individuals. 1,264 individuals and 4,647 records were excluded. @@ -162,7 +162,7 @@ Restrict cohort by numb summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals with at least 365 days of prior observations. 5 individuals and 109 records were excluded. @@ -180,7 +180,7 @@ Restrict cohort by num summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals with at least 365 days of future observations. 14 individuals and 206 records were excluded. @@ -202,7 +202,7 @@ Applying multipl summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when multiple demographic restrictions, so that only female individuals between 18 and 100 years old, with at least 365 days of prior and future observations are included. 1,413 individuals and 6156 records were diff --git a/articles/a04_require_intersections.html b/articles/a04_require_intersections.html index 75278372..d9c39827 100644 --- a/articles/a04_require_intersections.html +++ b/articles/a04_require_intersections.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -129,7 +129,7 @@ Restrictions on cohort presencesummary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 (users of +The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when restricted to only include individuals who intersect with the GI bleed cohort at least once before the cohort start date. 2,296 individuals and 8,765 records were excluded. @@ -151,7 +151,7 @@ Restrictions on cohort presencesummary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals with no intersects with the GI bleed cohort before the cohort start date. 36 individuals and 600 records were excluded. @@ -174,7 +174,7 @@ Restrictions on concept presencesummary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who have had events of GI bleeding at least once before the cohort start date. 2,296 individuals and 8,765 records were excluded. @@ -194,7 +194,7 @@ Restrictions on concept presencesummary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who have not had events of GI bleeding before the cohort start date. 36 individuals and 600 records were excluded. @@ -217,7 +217,7 @@ Restrictions on presence in summary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who intersect with the GI bleeding clinical table at least once before the cohort start date. 2,296 individuals and 8,765 records were excluded. @@ -238,7 +238,7 @@ Restrictions on presence in summary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who have no intersects with the GI bleeding clinical table before the cohort start date. 36 individuals and 600 records were excluded. @@ -258,7 +258,7 @@ Restrictions on deathssummary_attrition <- summariseCohortAttrition(cdm$medications_deaths) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who died after the cohort start date. None of the individuals in cohort 1 died and therefore they are all excluded from this cohort. @@ -273,7 +273,7 @@ Restrictions on deathssummary_attrition <- summariseCohortAttrition(cdm$medications_no_deaths) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who did not die after the cohort start date. None of the individuals in cohort 1 died and therefore no one was excluded. diff --git a/articles/a05_update_cohort_start_end.html b/articles/a05_update_cohort_start_end.html index 47d92b50..51696503 100644 --- a/articles/a05_update_cohort_start_end.html +++ b/articles/a05_update_cohort_start_end.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a06_concatanate_cohorts.html b/articles/a06_concatanate_cohorts.html index 0f6a6b3d..2e415a49 100644 --- a/articles/a06_concatanate_cohorts.html +++ b/articles/a06_concatanate_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -121,17 +121,17 @@ cdm$medications %>% filter(subject_id == 1) #> # Source: SQL [4 x 4] -#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmpQT59lg/file1dcd4337393f.duckdb] +#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmplazfDY/file1e0b2d32d912.duckdb] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> -#> 1 1 1 1976-10-20 1976-11-03 +#> 1 1 1 1980-03-15 1980-03-29 #> 2 1 1 1971-01-04 1971-01-18 #> 3 1 1 1982-09-11 1982-10-02 -#> 4 1 1 1980-03-15 1980-03-29 +#> 4 1 1 1976-10-20 1976-11-03 cdm$medications_collapsed %>% filter(subject_id == 1) #> # Source: SQL [3 x 4] -#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmpQT59lg/file1dcd4337393f.duckdb] +#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmplazfDY/file1e0b2d32d912.duckdb] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> #> 1 1 1 1976-10-20 1976-11-03 @@ -147,14 +147,14 @@ summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 (users of +The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when entries within 3 years of each other are merged. We see that collapsing the cohort has led to 1,390 fewer records. summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed) plotCohortAttrition(summary_attrition, cohortId = 2) -The flow chart above illustrates the changes to cohort 2 (users of +The flow chart above illustrates the changes to cohort 2 (users of diclofenac) when entries within 3 years of each other are merged. Since this cohort only has one record per individual the function collapseCohorts() had no impact on the final number of records. diff --git a/articles/a07_filter_cohorts.html b/articles/a07_filter_cohorts.html index fab5d0c3..d5abc93f 100644 --- a/articles/a07_filter_cohorts.html +++ b/articles/a07_filter_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -109,7 +109,7 @@ #> # A tibble: 2 × 3 #> cohort_definition_id number_records number_subjects #> <int> <int> <int> -#> 1 1 366 100 +#> 1 1 350 100 #> 2 2 830 830 diff --git a/articles/a08_split_cohorts.html b/articles/a08_split_cohorts.html index b55599d3..328b8abd 100644 --- a/articles/a08_split_cohorts.html +++ b/articles/a08_split_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a09_combine_cohorts.html b/articles/a09_combine_cohorts.html index e3e52d8d..755bd599 100644 --- a/articles/a09_combine_cohorts.html +++ b/articles/a09_combine_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a10_match_cohorts.html b/articles/a10_match_cohorts.html index e486ba69..c5d767c3 100644 --- a/articles/a10_match_cohorts.html +++ b/articles/a10_match_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a11_benchmark.html b/articles/a11_benchmark.html index 61fd9e7a..211be7f4 100644 --- a/articles/a11_benchmark.html +++ b/articles/a11_benchmark.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -586,16 +586,18 @@ Code and collaboration -OMOP table - - Database +OMOP table + + Database -CPRD Aurum - CORIVA-Estonia - CPRD Gold 100k - OHDSI SQL server +CPRD Aurum + CORIVA-Estonia + CPRD Gold 100k + OHDSI Postgres server + OHDSI SQL server @@ -608,6 +610,8 @@ Code and collaboration100,000 1,000 +1,000 @@ -619,6 +623,8 @@ Code and collaboration100,000 1,048 +1,048 @@ -630,6 +636,8 @@ Code and collaboration12,403,195 49,542 +49,542 @@ -641,6 +649,8 @@ Code and collaboration3,191,739 160,322 +160,322 @@ -652,6 +662,8 @@ Code and collaboration1,914,271 62,189 +62,189 @@ -663,6 +675,8 @@ Code and collaboration9,183,206 47,457 +47,457 @@ -674,6 +688,8 @@ Code and collaboration10,913,588 2,858 +2,858 @@ -685,6 +701,8 @@ Code and collaboration11,107,039 13,481 +13,481 @@ -1163,25 +1181,29 @@ Cohort counts and overlap - - - Tool + + + Tool -Cohort name - - CIRCE +Cohort name + + CIRCE - - CohortConstructor + + CohortConstructor -Number records - Number subjects - Number records - Number subjects +Number records + Number subjects + Number records + Number subjects @@ -1192,136 +1214,136 @@ Cohort counts and overlapAcquired neutropenia or unspecified leukopenia 1429966 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,429,966 632966 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">632,966 1302498 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,302,498 633030 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">633,030 Asthma without COPD 4009925 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,009,925 4009925 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,009,925 3934106 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,934,106 3934106 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">3,934,106 COVID-19 5600429 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,600,429 4452410 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,452,410 6206907 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">6,206,907 4452196 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">4,452,196 COVID-19: female 3111643 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,111,643 2434062 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,434,062 3452138 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,452,138 2438759 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">2,438,759 COVID-19: female, 0 to 50 2172113 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,172,113 1730180 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,730,180 2382039 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,382,039 1730116 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,730,116 COVID-19: female, 51 to 150 939818 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">939,818 708838 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">708,838 1070099 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,070,099 708643 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">708,643 COVID-19: male 2488786 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,488,786 2018348 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,018,348 2754769 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,754,769 2020625 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">2,020,625 COVID-19: male, 0 to 50 1709375 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,709,375 1422999 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,422,999 1862219 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,862,219 1422962 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,422,962 COVID-19: male, 51 to 150 779629 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">779,629 597804 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">597,804 892550 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">892,550 597663 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">597,663 Endometriosis procedure @@ -1357,61 +1379,61 @@ Cohort counts and overlapMajor non cardiac surgery 1932745 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,932,745 1932745 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,932,745 1932745 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,932,745 1932745 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,932,745 New fluoroquinolone users 1765274 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,765,274 1765274 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,765,274 1817439 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,817,439 1817439 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,817,439 New users of beta blockers nested in essential hypertension 98592 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">98,592 98592 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">98,592 102589 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">102,589 102589 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">102,589 Transverse myelitis 11930 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">11,930 4040 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,040 5818 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,818 4119 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">4,119 CORIVA-Estonia @@ -1420,13 +1442,13 @@ Cohort counts and overlapAcquired neutropenia or unspecified leukopenia 2231 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,231 634 2188 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,188 634 @@ -1435,121 +1457,121 @@ Cohort counts and overlapAsthma without COPD 25867 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">25,867 25867 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">25,867 25867 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">25,867 25867 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">25,867 COVID-19 421053 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">421,053 193435 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">193,435 435059 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">435,059 193435 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">193,435 COVID-19: female 235740 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">235,740 105849 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">105,849 243773 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">243,773 106322 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">106,322 COVID-19: female, 0 to 50 150121 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">150,121 69168 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">69,168 155256 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">155,256 69168 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">69,168 COVID-19: female, 51 to 150 85620 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">85,620 37154 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">37,154 88517 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">88,517 37154 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">37,154 COVID-19: male 185313 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">185,313 87586 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">87,586 191286 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">191,286 87891 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">87,891 COVID-19: male, 0 to 50 130252 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">130,252 63558 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">63,558 134415 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">134,415 63558 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">63,558 COVID-19: male, 51 to 150 55062 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">55,062 24333 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">24,333 56871 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">56,871 24333 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">24,333 Endometriosis procedure @@ -1570,61 +1592,61 @@ Cohort counts and overlapInpatient hospitalisation 267010 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">267,010 133705 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">133,705 267010 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">267,010 133705 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">133,705 Major non cardiac surgery 4025 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,025 4025 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,025 4025 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,025 4025 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">4,025 New fluoroquinolone users 39712 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">39,712 39712 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">39,712 39712 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">39,712 39712 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">39,712 New users of beta blockers nested in essential hypertension 18967 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">18,967 18967 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">18,967 18967 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">18,967 18967 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">18,967 Transverse myelitis @@ -1648,76 +1670,76 @@ Cohort counts and overlapAcquired neutropenia or unspecified leukopenia 2719 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,719 1167 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,167 2675 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,675 1167 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,167 Asthma without COPD 8808 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">8,808 8808 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">8,808 8741 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">8,741 8741 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">8,741 COVID-19 3231 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,231 2881 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,881 3275 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,275 2881 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">2,881 COVID-19: female 1748 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,748 1543 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,543 1771 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,771 1543 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,543 COVID-19: female, 0 to 50 1271 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,271 1125 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,125 1291 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,291 1125 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,125 COVID-19: female, 51 to 150 @@ -1738,28 +1760,28 @@ Cohort counts and overlapCOVID-19: male 1483 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,483 1338 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,338 1504 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,504 1341 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,341 COVID-19: male, 0 to 50 1054 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,054 960 1072 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,072 960 @@ -1813,46 +1835,46 @@ Cohort counts and overlapMajor non cardiac surgery 4146 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,146 4146 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,146 4146 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,146 4146 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">4,146 New fluoroquinolone users 5412 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,412 5412 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,412 5412 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,412 5412 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">5,412 New users of beta blockers nested in essential hypertension 1723 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,723 1723 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,723 1723 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,723 1723 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,723 Transverse myelitis @@ -1870,6 +1892,234 @@ Cohort counts and overlap11 +OHDSI Postgres server + + +Acquired neutropenia or unspecified leukopenia +151 +86 +106 +86 + + +Asthma without COPD +126 +126 +126 +126 + + +COVID-19 +0 +0 +0 +0 + + +COVID-19: female +0 +0 +0 +0 + + +COVID-19: female, 0 to 50 +0 +0 +0 +0 + + +COVID-19: female, 51 to 150 +0 +0 +0 +0 + + +COVID-19: male +0 +0 +0 +0 + + +COVID-19: male, 0 to 50 +0 +0 +0 +0 + + +COVID-19: male, 51 to 150 +0 +0 +0 +0 + + +Endometriosis procedure +0 +0 +0 +0 + + +Inpatient hospitalisation +522 +321 +522 +321 + + +Major non cardiac surgery +88 +88 +92 +92 + + +New fluoroquinolone users +145 +145 +145 +145 + + +New users of beta blockers nested in essential hypertension +112 +112 +112 +112 + + +Transverse myelitis +0 +0 +0 +0 + + OHDSI SQL server @@ -2134,23 +2384,23 @@ By domain - @@ -2587,13 +2837,14 @@ By domain Database_name - - Time (minutes) + + Time (minutes) -CIRCE - CohortConstructor +CIRCE + CohortConstructor @@ -2619,6 +2870,13 @@ By domain7.85 +OHDSI Postgres server +4.32 +29.20 + + OHDSI SQL server 2.89 @@ -2638,23 +2896,23 @@ Cohort stratification - - @@ -3091,13 +3349,14 @@ Cohort stratification Database - - Time (minutes) + + Time (minutes) -CIRCE - CohortConstructor +CIRCE + CohortConstructor @@ -3123,6 +3382,13 @@ Cohort stratification19.52 +OHDSI Postgres server +6.75 +73.24 + + OHDSI SQL server 4.56 diff --git a/articles/a11_benchmark_files/figure-html/unnamed-chunk-10-1.png b/articles/a11_benchmark_files/figure-html/unnamed-chunk-10-1.png index 077c57cd..a22054df 100644 Binary files a/articles/a11_benchmark_files/figure-html/unnamed-chunk-10-1.png and b/articles/a11_benchmark_files/figure-html/unnamed-chunk-10-1.png differ diff --git a/articles/a11_benchmark_files/figure-html/unnamed-chunk-5-1.png b/articles/a11_benchmark_files/figure-html/unnamed-chunk-5-1.png index 4ee21baa..49f847ee 100644 Binary files a/articles/a11_benchmark_files/figure-html/unnamed-chunk-5-1.png and b/articles/a11_benchmark_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/articles/a11_benchmark_files/figure-html/unnamed-chunk-7-1.png b/articles/a11_benchmark_files/figure-html/unnamed-chunk-7-1.png index 4effca28..aedb495d 100644 Binary files a/articles/a11_benchmark_files/figure-html/unnamed-chunk-7-1.png and b/articles/a11_benchmark_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/articles/index.html b/articles/index.html index 52cd76cf..63245934 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/authors.html b/authors.html index c5042185..0b771723 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -90,13 +90,13 @@ Citation Burn E, Catala M, Mercade-Besora N, Alcalde-Herraiz M, Du M, Guo Y, Chen X, Lopez-Guell K, Rowlands E (2024). CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model. -R package version 0.3.0.900, https://ohdsi.github.io/CohortConstructor/. +R package version 0.3.1, https://ohdsi.github.io/CohortConstructor/. @Manual{, title = {CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model}, author = {Edward Burn and Marti Catala and Nuria Mercade-Besora and Marta Alcalde-Herraiz and Mike Du and Yuchen Guo and Xihang Chen and Kim Lopez-Guell and Elin Rowlands}, year = {2024}, - note = {R package version 0.3.0.900}, + note = {R package version 0.3.1}, url = {https://ohdsi.github.io/CohortConstructor/}, } diff --git a/index.html b/index.html index f3aa2541..9385ede9 100644 --- a/index.html +++ b/index.html @@ -29,7 +29,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/pkgdown.yml b/pkgdown.yml index 22df4163..51332e54 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -14,7 +14,7 @@ articles: a09_combine_cohorts: a09_combine_cohorts.html a10_match_cohorts: a10_match_cohorts.html a11_benchmark: a11_benchmark.html -last_built: 2024-10-01T17:04Z +last_built: 2024-10-08T08:55Z urls: reference: https://ohdsi.github.io/CohortConstructor/reference article: https://ohdsi.github.io/CohortConstructor/articles diff --git a/reference/CohortConstructor-package.html b/reference/CohortConstructor-package.html index 9849f0f6..0fc0c101 100644 --- a/reference/CohortConstructor-package.html +++ b/reference/CohortConstructor-package.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -67,7 +67,7 @@ Author< Mike Du mike.du@ndorms.ox.ac.uk (ORCID) Yuchen Guo yuchen.guo@ndorms.ox.ac.uk (ORCID) Xihang Chen xihang.chen@ndorms.ox.ac.uk (ORCID) -Kim Lopez kim.lopez@spc.ox.ac.uk (ORCID) +Kim Lopez-Guell kim.lopez@spc.ox.ac.uk (ORCID) Elin Rowlands elin.rowlands@ndorms.ox.ac.uk (ORCID) diff --git a/reference/benchmarkData.html b/reference/benchmarkData.html index 45a91354..097fc193 100644 --- a/reference/benchmarkData.html +++ b/reference/benchmarkData.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/cdmDoc.html b/reference/cdmDoc.html index 6423cb33..2134bf9b 100644 --- a/reference/cdmDoc.html +++ b/reference/cdmDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/cohortDoc.html b/reference/cohortDoc.html index aec5f84e..c8ab3cff 100644 --- a/reference/cohortDoc.html +++ b/reference/cohortDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/cohortIdModifyDoc.html b/reference/cohortIdModifyDoc.html index f679f471..1ac353a4 100644 --- a/reference/cohortIdModifyDoc.html +++ b/reference/cohortIdModifyDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/cohortIdSubsetDoc.html b/reference/cohortIdSubsetDoc.html index 560175c8..9327a0fd 100644 --- a/reference/cohortIdSubsetDoc.html +++ b/reference/cohortIdSubsetDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/collapseCohorts.html b/reference/collapseCohorts.html index 1f3c2772..1e48519e 100644 --- a/reference/collapseCohorts.html +++ b/reference/collapseCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/columnDateDoc.html b/reference/columnDateDoc.html index 173af6f0..331c1e39 100644 --- a/reference/columnDateDoc.html +++ b/reference/columnDateDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/conceptCohort.html b/reference/conceptCohort.html index 3ac1ad39..7cc2b200 100644 --- a/reference/conceptCohort.html +++ b/reference/conceptCohort.html @@ -53,7 +53,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/conceptSetDoc.html b/reference/conceptSetDoc.html index 4df9c20e..3f205c7a 100644 --- a/reference/conceptSetDoc.html +++ b/reference/conceptSetDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/demographicsCohort.html b/reference/demographicsCohort.html index d8738441..4a27b6c8 100644 --- a/reference/demographicsCohort.html +++ b/reference/demographicsCohort.html @@ -13,7 +13,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/entryAtFirstDate.html b/reference/entryAtFirstDate.html index 37934adf..5c512b7c 100644 --- a/reference/entryAtFirstDate.html +++ b/reference/entryAtFirstDate.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -181,10 +181,10 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date entry_reason #> <dbl> <dbl> <date> <date> <chr> -#> 1 1 4 2002-12-09 2002-12-09 date_1; dat… -#> 2 1 1 2001-08-01 2001-09-01 date_1; dat… -#> 3 1 2 2001-01-01 2001-01-12 date_1 -#> 4 1 3 2015-02-14 2015-02-15 date_2 +#> 1 1 1 2001-08-01 2001-09-01 date_1; dat… +#> 2 1 4 2002-12-09 2002-12-09 date_1; dat… +#> 3 1 3 2015-02-14 2015-02-15 date_2 +#> 4 1 2 2001-01-01 2001-01-12 date_1 # } diff --git a/reference/entryAtLastDate.html b/reference/entryAtLastDate.html index 1e6882e3..a64422ff 100644 --- a/reference/entryAtLastDate.html +++ b/reference/entryAtLastDate.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -181,10 +181,10 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date entry_reason #> <dbl> <dbl> <date> <date> <chr> -#> 1 1 2 2001-01-01 2001-01-12 date_1 +#> 1 1 1 2001-08-01 2001-09-01 date_2; dat… #> 2 1 3 2015-02-14 2015-02-15 date_2 -#> 3 1 1 2001-08-01 2001-09-01 date_1; dat… -#> 4 1 4 2002-12-09 2002-12-09 date_1; dat… +#> 3 1 2 2001-01-01 2001-01-12 date_1 +#> 4 1 4 2002-12-09 2002-12-09 date_2; dat… # } diff --git a/reference/exitAtDeath.html b/reference/exitAtDeath.html index ea102781..13ac96cc 100644 --- a/reference/exitAtDeath.html +++ b/reference/exitAtDeath.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -105,16 +105,16 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> -#> 1 1 1 1943-11-26 1949-06-21 -#> 2 2 4 1949-03-07 1951-05-08 -#> 3 1 10 1948-08-08 1952-03-27 -#> 4 3 6 1966-09-27 1975-03-05 +#> 1 3 2 1918-06-14 1921-11-28 +#> 2 1 9 1940-05-01 1945-06-29 +#> 3 3 8 1921-10-07 1931-05-01 +#> 4 3 7 1962-05-14 1964-03-08 #> 5 2 5 1966-05-05 1970-11-15 #> 6 2 3 1939-07-24 1944-01-14 -#> 7 3 8 1921-10-07 1931-05-01 -#> 8 1 9 1940-05-01 1945-06-29 -#> 9 3 2 1918-06-14 1921-11-28 -#> 10 3 7 1962-05-14 1964-03-08 +#> 7 1 10 1948-08-08 1952-03-27 +#> 8 2 4 1949-03-07 1951-05-08 +#> 9 3 6 1966-09-27 1975-03-05 +#> 10 1 1 1943-11-26 1949-06-21 # } diff --git a/reference/exitAtFirstDate.html b/reference/exitAtFirstDate.html index e27c4b27..a55a5a53 100644 --- a/reference/exitAtFirstDate.html +++ b/reference/exitAtFirstDate.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -181,10 +181,10 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date exit_reason #> <dbl> <dbl> <date> <date> <chr> -#> 1 1 4 2000-12-09 2002-12-09 date_1; dat… -#> 2 1 1 2000-06-03 2001-08-01 date_1; dat… +#> 1 1 1 2000-06-03 2001-08-01 date_2; dat… +#> 2 1 3 2015-01-15 2015-01-15 date_1 #> 3 1 2 2000-01-01 2001-01-01 date_1 -#> 4 1 3 2015-01-15 2015-01-15 date_1 +#> 4 1 4 2000-12-09 2002-12-09 date_2; dat… # } diff --git a/reference/exitAtLastDate.html b/reference/exitAtLastDate.html index 9d33087c..584c19bb 100644 --- a/reference/exitAtLastDate.html +++ b/reference/exitAtLastDate.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -181,10 +181,10 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date exit_reason #> <dbl> <dbl> <date> <date> <chr> -#> 1 1 4 2000-12-09 2002-12-09 date_2; dat… -#> 2 1 2 2000-01-01 2001-01-01 date_1 -#> 3 1 3 2015-01-15 2015-04-15 date_2 -#> 4 1 1 2000-06-03 2001-08-01 date_2; dat… +#> 1 1 3 2015-01-15 2015-04-15 date_2 +#> 2 1 4 2000-12-09 2002-12-09 date_1; dat… +#> 3 1 2 2000-01-01 2001-01-01 date_1 +#> 4 1 1 2000-06-03 2001-08-01 date_1; dat… # } diff --git a/reference/exitAtObservationEnd.html b/reference/exitAtObservationEnd.html index 13e6e420..0885f9d1 100644 --- a/reference/exitAtObservationEnd.html +++ b/reference/exitAtObservationEnd.html @@ -15,7 +15,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -103,10 +103,10 @@ Examples#> <int> <int> <date> <date> #> 1 1 9 2012-01-18 2012-06-30 #> 2 1 6 2003-10-31 2005-11-04 -#> 3 1 2 1964-09-18 1968-04-03 -#> 4 1 4 1998-06-22 2013-05-12 -#> 5 1 5 2007-10-19 2014-09-25 -#> 6 1 3 1976-11-28 2000-04-25 +#> 3 1 5 2007-10-19 2014-09-25 +#> 4 1 3 1976-11-28 2000-04-25 +#> 5 1 4 1998-06-22 2013-05-12 +#> 6 1 2 1964-09-18 1968-04-03 # } diff --git a/reference/gapDoc.html b/reference/gapDoc.html index 532b87c1..cbfd865b 100644 --- a/reference/gapDoc.html +++ b/reference/gapDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/index.html b/reference/index.html index 75dd1022..557ab837 100644 --- a/reference/index.html +++ b/reference/index.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/intersectCohorts.html b/reference/intersectCohorts.html index 2c04a75d..602c177d 100644 --- a/reference/intersectCohorts.html +++ b/reference/intersectCohorts.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/matchCohorts.html b/reference/matchCohorts.html index 3e62f0c8..746b16bb 100644 --- a/reference/matchCohorts.html +++ b/reference/matchCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -141,16 +141,16 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date cluster_id #> <int> <int> <date> <date> <dbl> -#> 1 1 33 1993-05-10 1997-04-01 14 -#> 2 1 103 1982-05-27 1987-10-22 21 -#> 3 1 67 2009-01-30 2010-03-12 78 -#> 4 1 110 2005-10-01 2006-06-12 100 -#> 5 1 19 2015-04-24 2015-09-01 108 -#> 6 1 89 2002-04-17 2005-07-29 54 -#> 7 1 150 2008-01-10 2008-11-21 113 -#> 8 1 62 2008-04-08 2008-07-26 44 -#> 9 1 16 2004-09-11 2006-10-01 101 -#> 10 1 16 2007-05-18 2007-10-08 102 +#> 1 1 89 2005-07-30 2007-06-17 54 +#> 2 1 150 2008-01-10 2008-11-21 113 +#> 3 1 30 2008-04-20 2010-01-04 27 +#> 4 1 47 1994-03-23 1997-08-27 89 +#> 5 1 62 2008-04-08 2008-07-26 44 +#> 6 1 16 2004-09-11 2006-10-01 102 +#> 7 1 110 2005-06-30 2005-09-30 99 +#> 8 1 19 2015-04-24 2015-09-01 108 +#> 9 1 33 1993-05-10 1997-04-01 14 +#> 10 1 80 2000-12-22 2002-04-17 18 #> # ℹ more rows # } diff --git a/reference/measurementCohort.html b/reference/measurementCohort.html index 397e145b..12fa2148 100644 --- a/reference/measurementCohort.html +++ b/reference/measurementCohort.html @@ -31,7 +31,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/mockCohortConstructor.html b/reference/mockCohortConstructor.html index 829e48d4..e3dcfb97 100644 --- a/reference/mockCohortConstructor.html +++ b/reference/mockCohortConstructor.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/nameDoc.html b/reference/nameDoc.html index 0505c849..03e2134d 100644 --- a/reference/nameDoc.html +++ b/reference/nameDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/padCohortStart.html b/reference/padCohortStart.html index 17784fee..3a86b5c4 100644 --- a/reference/padCohortStart.html +++ b/reference/padCohortStart.html @@ -21,7 +21,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/reexports.html b/reference/reexports.html index a5cfaced..8383dba6 100644 --- a/reference/reexports.html +++ b/reference/reexports.html @@ -29,7 +29,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireAge.html b/reference/requireAge.html index f78f7691..7c7031e9 100644 --- a/reference/requireAge.html +++ b/reference/requireAge.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireCohortIntersect.html b/reference/requireCohortIntersect.html index c2463b97..c052fb88 100644 --- a/reference/requireCohortIntersect.html +++ b/reference/requireCohortIntersect.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireConceptIntersect.html b/reference/requireConceptIntersect.html index a1cd712e..dd451c79 100644 --- a/reference/requireConceptIntersect.html +++ b/reference/requireConceptIntersect.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireDeathFlag.html b/reference/requireDeathFlag.html index 7f4c753c..8015f9bd 100644 --- a/reference/requireDeathFlag.html +++ b/reference/requireDeathFlag.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireDemographics.html b/reference/requireDemographics.html index 161c3de9..23199dda 100644 --- a/reference/requireDemographics.html +++ b/reference/requireDemographics.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireDemographicsDoc.html b/reference/requireDemographicsDoc.html index d656c541..6a898592 100644 --- a/reference/requireDemographicsDoc.html +++ b/reference/requireDemographicsDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireFutureObservation.html b/reference/requireFutureObservation.html index da73506f..08544a5a 100644 --- a/reference/requireFutureObservation.html +++ b/reference/requireFutureObservation.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -111,15 +111,15 @@ Examples#> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> #> 1 1 2 1964-09-18 1965-08-30 -#> 2 1 3 1976-11-28 1977-03-11 +#> 2 1 3 1978-04-04 1987-02-26 #> 3 1 4 1998-06-22 2001-02-12 -#> 4 1 5 2008-11-11 2011-09-12 +#> 4 1 5 2007-10-19 2008-11-10 #> 5 1 6 2003-11-15 2004-04-10 #> 6 1 9 2012-01-18 2012-03-08 -#> 7 1 3 1978-04-04 1987-02-26 -#> 8 1 5 2007-10-19 2008-11-10 +#> 7 1 3 1977-03-12 1978-04-03 +#> 8 1 5 2008-11-11 2011-09-12 #> 9 1 6 2003-10-31 2003-11-14 -#> 10 1 3 1977-03-12 1978-04-03 +#> 10 1 3 1976-11-28 1977-03-11 # } diff --git a/reference/requireInDateRange.html b/reference/requireInDateRange.html index 9724ee92..4b172ef4 100644 --- a/reference/requireInDateRange.html +++ b/reference/requireInDateRange.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireIntersectDoc.html b/reference/requireIntersectDoc.html index e7bff0cd..ec0e1661 100644 --- a/reference/requireIntersectDoc.html +++ b/reference/requireIntersectDoc.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireIsEntry.html b/reference/requireIsEntry.html index eec327c8..570d6be7 100644 --- a/reference/requireIsEntry.html +++ b/reference/requireIsEntry.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireIsFirstEntry.html b/reference/requireIsFirstEntry.html index df884919..92096723 100644 --- a/reference/requireIsFirstEntry.html +++ b/reference/requireIsFirstEntry.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireIsLastEntry.html b/reference/requireIsLastEntry.html index 07b334a0..976d8093 100644 --- a/reference/requireIsLastEntry.html +++ b/reference/requireIsLastEntry.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireMinCohortCount.html b/reference/requireMinCohortCount.html index 0fab0031..b3ee0a99 100644 --- a/reference/requireMinCohortCount.html +++ b/reference/requireMinCohortCount.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requirePriorObservation.html b/reference/requirePriorObservation.html index d6a71240..c2044362 100644 --- a/reference/requirePriorObservation.html +++ b/reference/requirePriorObservation.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireSex.html b/reference/requireSex.html index f7611e9b..376bc429 100644 --- a/reference/requireSex.html +++ b/reference/requireSex.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireTableIntersect.html b/reference/requireTableIntersect.html index a8803b02..ba9dad21 100644 --- a/reference/requireTableIntersect.html +++ b/reference/requireTableIntersect.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/sampleCohorts.html b/reference/sampleCohorts.html index 03c06583..1d2a5de9 100644 --- a/reference/sampleCohorts.html +++ b/reference/sampleCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -97,16 +97,16 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> -#> 1 1 13 1991-04-14 1992-10-21 -#> 2 1 13 1992-11-03 2000-11-27 -#> 3 1 14 1998-11-10 1999-03-09 -#> 4 1 14 1999-03-10 2001-06-02 -#> 5 1 14 2001-06-03 2001-12-04 -#> 6 1 25 2018-01-10 2018-03-13 -#> 7 1 25 2018-03-14 2018-03-22 -#> 8 1 25 2018-03-23 2018-06-26 -#> 9 1 25 2018-06-27 2018-11-13 -#> 10 1 39 2000-03-07 2009-03-17 +#> 1 1 9 2011-12-20 2011-12-20 +#> 2 1 9 2011-12-21 2011-12-29 +#> 3 1 9 2011-12-30 2012-04-02 +#> 4 1 17 2009-06-18 2013-02-01 +#> 5 1 21 1981-04-20 1986-09-10 +#> 6 1 23 2000-10-08 2001-06-04 +#> 7 1 23 2001-06-05 2003-08-26 +#> 8 1 34 2005-06-12 2006-03-16 +#> 9 1 34 2006-03-17 2008-07-24 +#> 10 1 41 2000-07-02 2000-11-21 #> # ℹ more rows # } diff --git a/reference/stratifyCohorts.html b/reference/stratifyCohorts.html index 274a18a6..a622b201 100644 --- a/reference/stratifyCohorts.html +++ b/reference/stratifyCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -115,15 +115,15 @@ Examples#> cohort_definition_id subject_id cohort_start_date cohort_end_date age #> <int> <int> <date> <date> <int> #> 1 1 2 1964-09-18 1965-08-30 9 -#> 2 1 3 1978-04-04 1987-02-26 19 +#> 2 1 3 1976-11-28 1977-03-11 17 #> 3 1 4 1998-06-22 2001-02-12 16 -#> 4 1 5 2007-10-19 2008-11-10 44 +#> 4 1 5 2008-11-11 2011-09-12 45 #> 5 2 6 2003-11-15 2004-04-10 35 #> 6 1 9 2012-01-18 2012-03-08 27 -#> 7 1 3 1977-03-12 1978-04-03 17 -#> 8 1 5 2008-11-11 2011-09-12 45 +#> 7 1 3 1978-04-04 1987-02-26 19 +#> 8 1 5 2007-10-19 2008-11-10 44 #> 9 2 6 2003-10-31 2003-11-14 35 -#> 10 1 3 1976-11-28 1977-03-11 17 +#> 10 1 3 1977-03-12 1978-04-03 17 #> # ℹ more rows settings(cdm$my_cohort) diff --git a/reference/subsetCohorts.html b/reference/subsetCohorts.html index 53bc0f2a..f2e8c40d 100644 --- a/reference/subsetCohorts.html +++ b/reference/subsetCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/trimDemographics.html b/reference/trimDemographics.html index 4cbe0a79..b2b0844a 100644 --- a/reference/trimDemographics.html +++ b/reference/trimDemographics.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -125,16 +125,16 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> -#> 1 1 38 1979-07-05 1981-02-09 -#> 2 1 43 1990-02-26 1995-03-03 -#> 3 1 51 1982-12-05 1997-10-16 -#> 4 1 53 1998-02-23 1998-12-09 -#> 5 1 74 2007-11-14 2008-11-21 -#> 6 1 78 2001-08-27 2005-04-11 -#> 7 1 38 1978-07-24 1979-07-04 -#> 8 1 51 1979-04-13 1982-12-04 -#> 9 1 53 1997-03-26 1998-02-22 -#> 10 1 74 2007-06-26 2007-11-13 +#> 1 1 4 1998-12-14 2002-02-14 +#> 2 1 9 2011-12-30 2012-04-02 +#> 3 1 21 1985-08-16 1986-09-10 +#> 4 1 26 1984-05-16 1989-03-22 +#> 5 1 35 2007-05-13 2010-07-22 +#> 6 1 39 2000-03-07 2009-03-17 +#> 7 1 41 2000-11-22 2000-12-09 +#> 8 1 69 2003-01-14 2003-05-28 +#> 9 1 71 2009-02-01 2009-09-05 +#> 10 1 83 2007-05-29 2008-05-01 #> # ℹ more rows # } diff --git a/reference/trimToDateRange.html b/reference/trimToDateRange.html index 4fd66f17..660ef3cb 100644 --- a/reference/trimToDateRange.html +++ b/reference/trimToDateRange.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/unionCohorts.html b/reference/unionCohorts.html index 85248807..18ebd77e 100644 --- a/reference/unionCohorts.html +++ b/reference/unionCohorts.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/windowDoc.html b/reference/windowDoc.html index 7a0fc213..8f33c2db 100644 --- a/reference/windowDoc.html +++ b/reference/windowDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/yearCohorts.html b/reference/yearCohorts.html index 5a66225f..b785b804 100644 --- a/reference/yearCohorts.html +++ b/reference/yearCohorts.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/search.json b/search.json index a8a08488..9872166a 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://ohdsi.github.io/CohortConstructor/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 ","code":""},{"path":[]},{"path":"https://ohdsi.github.io/CohortConstructor/LICENSE.html","id":"id_1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. Definitions","title":"Apache License","text":"“License” shall mean terms conditions use, reproduction, distribution defined Sections 1 9 document. “Licensor” shall mean copyright owner entity authorized copyright owner granting License. “Legal Entity” shall mean union acting entity entities control, controlled , common control entity. purposes definition, “control” means () power, direct indirect, cause direction management entity, whether contract otherwise, (ii) ownership fifty percent (50%) outstanding shares, (iii) beneficial ownership entity. “” (“”) shall mean individual Legal Entity exercising permissions granted License. “Source” form shall mean preferred form making modifications, including limited software source code, documentation source, configuration files. “Object” form shall mean form resulting mechanical transformation translation Source form, including limited compiled object code, generated documentation, conversions media types. “Work” shall mean work authorship, whether Source Object form, made available License, indicated copyright notice included attached work (example provided Appendix ). “Derivative Works” shall mean work, whether Source Object form, based (derived ) Work editorial revisions, annotations, elaborations, modifications represent, whole, original work authorship. purposes License, Derivative Works shall include works remain separable , merely link (bind name) interfaces , Work Derivative Works thereof. “Contribution” shall mean work authorship, including original version Work modifications additions Work Derivative Works thereof, intentionally submitted Licensor inclusion Work copyright owner individual Legal Entity authorized submit behalf copyright owner. purposes definition, “submitted” means form electronic, verbal, written communication sent Licensor representatives, including limited communication electronic mailing lists, source code control systems, issue tracking systems managed , behalf , Licensor purpose discussing improving Work, excluding communication conspicuously marked otherwise designated writing copyright owner “Contribution.” “Contributor” shall mean Licensor individual Legal Entity behalf Contribution received Licensor subsequently incorporated within Work.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/LICENSE.html","id":"id_2-grant-of-copyright-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"2. 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(Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner] 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."},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a00_introduction.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction","text":"CohortConstructor package designed support cohort building pipelines. using package general workflow first build set base cohorts subsequently apply inclusion criteria derive final study cohorts interest. Base cohorts built domain (rather cohort definition) one base cohort many study cohorts can derived.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a00_introduction.html","id":"building-a-cohort-set-by-domain","dir":"Articles","previous_headings":"","what":"Building a cohort set by domain","title":"Introduction","text":"Let´s say want build 5 cohorts 3 (asthma, copd, diabetes) defined based concepts seen condition occurrence table 2 (acetaminophen warfarin) based concepts recorded drug exposure table. can build cohorts independently, one . However, approach mean repeating 3 joins condition occurrence tables 2 joins drug exposure table (concepts concept sets). make less computationally expensive, instead create cohorts domain. case instead make one join condition occurrence table one drug exposure (using concept sets together).","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a00_introduction.html","id":"deriving-study-cohorts-from-base-cohorts","dir":"Articles","previous_headings":"","what":"Deriving study cohorts from base cohorts","title":"Introduction","text":"making study cohorts often concept sets define clinical event along various study-specific inclusion criteria, example criteria around amount prior observation age. Often may sensitivity analysis concept set remains inclusion criteria change. situations can make cohorts one--one. However, can lead duplication can see example identify asthma records multiple times. alternative approach build base cohort, case based asthma records, derive multiple cohorts different inclusion criteria applied.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a00_introduction.html","id":"considerations-when-building-cohorts","dir":"Articles","previous_headings":"","what":"Considerations when building cohorts","title":"Introduction","text":"CohortConstructor provides means building cohorts via pipeline, cohorts created application sequence functions. important note order sequence often important implications. example just one individual three recorded diagnoses asthma. One diagnosis 2008 two 2009, last coming individual´s 18th birthday. three cohort pipelines shown restrictions around calendar dates, age, record first. cohort pipeline , however, individual included final cohort, third diagnosis used cohort start. pipeline B C individual excluded.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a01_building_base_cohorts.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Building base cohorts","text":"Let’s first create cdm reference Eunomia synthetic data.","code":"library(CDMConnector) library(CodelistGenerator) library(PatientProfiles) library(CohortConstructor) library(dplyr) con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir()) cdm <- cdm_from_con(con, cdm_schema = \"main\", write_schema = c(prefix = \"my_study_\", schema = \"main\"))"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a01_building_base_cohorts.html","id":"demographic-based-cohort-creation","dir":"Articles","previous_headings":"","what":"Demographic based cohort creation","title":"Building base cohorts","text":"One base cohort can create based around patient demographics. example create cohort people enter 18th birthday leave age 65 ","code":"cdm$working_age_cohort <- demographicsCohort(cdm = cdm, ageRange = c(18, 65), name = \"working_age_cohort\") settings(cdm$working_age_cohort) #> # A tibble: 1 × 3 #> cohort_definition_id cohort_name age_range #> #> 1 1 demographics 18_65 cohortCount(cdm$working_age_cohort) #> # A tibble: 1 × 3 #> cohort_definition_id number_records number_subjects #> #> 1 1 2694 2694 attrition(cdm$working_age_cohort) #> # A tibble: 2 × 7 #> cohort_definition_id number_records number_subjects reason_id reason #> #> 1 1 2694 2694 1 Initial qualify… #> 2 1 2694 2694 2 Age requirement… #> # ℹ 2 more variables: excluded_records , excluded_subjects cdm$working_age_cohort |> addAge(indexDate = \"cohort_start_date\") |> summarise(min_start_age = min(age), median_start_age = median(age), max_start_age = max(age)) #> # Source: SQL [1 x 3] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/Rtmp8sghhQ/file1c2d736042a2.duckdb] #> min_start_age median_start_age max_start_age #> #> 1 17 18 18 cdm$working_age_cohort |> addAge(indexDate = \"cohort_end_date\") |> summarise(min_start_age = min(age), median_start_age = median(age), max_start_age = max(age)) #> # Source: SQL [1 x 3] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/Rtmp8sghhQ/file1c2d736042a2.duckdb] #> min_start_age median_start_age max_start_age #> #> 1 31 57 65"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a01_building_base_cohorts.html","id":"concept-based-cohort-creation","dir":"Articles","previous_headings":"","what":"Concept based cohort creation","title":"Building base cohorts","text":"","code":"drug_codes <- getDrugIngredientCodes(cdm, name = c(\"diclofenac\", \"acetaminophen\")) drug_codes #> #> - 161_acetaminophen (7 codes) #> - 3355_diclofenac (1 codes) cdm$medications <- conceptCohort(cdm = cdm, conceptSet = drug_codes, name = \"medications\") settings(cdm$medications) #> # A tibble: 2 × 4 #> cohort_definition_id cohort_name cdm_version vocabulary_version #> #> 1 1 161_acetaminophen 5.3 v5.0 18-JAN-19 #> 2 2 3355_diclofenac 5.3 v5.0 18-JAN-19 cohortCount(cdm$medications) #> # A tibble: 2 × 3 #> cohort_definition_id number_records number_subjects #> #> 1 1 13908 2679 #> 2 2 830 830"},{"path":[]},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-the-first-record-per-person","dir":"Articles","previous_headings":"","what":"Keep only the first record per person","title":"Cohort Requirements","text":"Individuals can contribute multiple records per cohort. However now ’ll keep earliest cohort entry remaining records using requireIsFirstEntry() CohortConstructor. flow chart illustrates changes cohort 1 (acetaminophen users) restricted first record individual. number individuals remains unchanged, 6,785 records excluded.","code":"cdm$medications <- cdm$medications %>% requireIsFirstEntry() summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-a-specific-range-of-records","dir":"Articles","previous_headings":"","what":"Keep only a specific range of records","title":"Cohort Requirements","text":"can also choose specific range records using requireIsEntry() CohortConstructor. flow chart illustrates changes cohort 1 restricted first five records individual. number individuals remains unchanged, 6,785 records excluded.","code":"cdm$medications <- cdm$medications %>% requireIsEntry(c(1,5)) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-the-last-record-per-person","dir":"Articles","previous_headings":"","what":"Keep only the last record per person","title":"Cohort Requirements","text":"also possible include last record individual using requireIsLastEntry() CohortConstructor. flow chart illustrates changes cohort 1 restricted last record individual. number individuals remains unchanged, 6,785 records excluded.","code":"cdm$medications <- cdm$medications %>% requireIsLastEntry() summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-records-within-a-date-range","dir":"Articles","previous_headings":"","what":"Keep only records within a date range","title":"Cohort Requirements","text":"Individuals may contribute multiple records extended periods. can define study’s start end dates, filtering records fall outside specified date range using requireInDateRang function CohortConstructor. flow chart illustrates changes cohort 1 restricted specified date range. 1,948 individuals 8,660 records excluded.","code":"cdm$medications <- cdm$medications %>% requireInDateRange(dateRange = as.Date(c(\"2010-01-01\", \"2015-01-01\"))) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-records-from-cohorts-with-a-minimum-number-of-individuals","dir":"Articles","previous_headings":"","what":"Keep only records from cohorts with a minimum number of individuals","title":"Cohort Requirements","text":"studies might require minimum cohort size. can define minimum size, filtering records smaller required, using requireMinCohortCount function CohortConstructor. Cohort 1 includes 2,580 individuals, none excluded due minimum cohort size restriction 1,000.","code":"cdm$medications <- cdm$medications %>% requireMinCohortCount(minCohortCount = 1000) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"running-multiple-requirements","dir":"Articles","previous_headings":"","what":"Running multiple requirements","title":"Cohort Requirements","text":"Multiple restrictions can applied cohort, however care needs taken restrictions placed correct order. example, recommended apply minimum size restriction last. flow chart illustrates changes cohort 1 restricted include first record individual specified date range. 2,529 individuals 9,314 records excluded.","code":"cdm$medications <- cdm$medications %>% requireIsFirstEntry() %>% requireInDateRange(dateRange = as.Date(c(\"2010-01-01\", \"2016-01-01\"))) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"restrict-cohort-by-age","dir":"Articles","previous_headings":"","what":"Restrict cohort by age","title":"Demographic Requirements","text":"can choose specific age range individuals cohort using requireAge() CohortConstructor. flow chart illustrates changes cohort 1 (users acetaminophen) restricted include individuals aged 18 90. 226 individuals 2,863 records excluded. variable ‘cohort_start_date’ used individuals filtered based age entered cohort.","code":"cdm$medications <- cdm$medications %>% requireAge(indexDate = \"cohort_start_date\", ageRange = list(c(18,100))) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"restrict-cohort-by-sex","dir":"Articles","previous_headings":"","what":"Restrict cohort by sex","title":"Demographic Requirements","text":"can also specify sex criteria individuals cohort using requireSex() CohortConstructor. flow chart illustrates changes cohort 1 restricted include ‘female’ individuals. 1,264 individuals 4,647 records excluded.","code":"cdm$medications <- cdm$medications %>% requireSex(sex = \"Female\") summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"restrict-cohort-by-number-of-prior-observations","dir":"Articles","previous_headings":"","what":"Restrict cohort by number of prior observations","title":"Demographic Requirements","text":"can also specify minimum number days prior observations individual using requirePriorObservation() CohortConstructor. flow chart illustrates changes cohort 1 restricted include individuals least 365 days prior observations. 5 individuals 109 records excluded.","code":"cdm$medications <- cdm$medications %>% requirePriorObservation(indexDate = \"cohort_start_date\", minPriorObservation = 365) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"restrict-cohort-by-number-of-future-observations","dir":"Articles","previous_headings":"","what":"Restrict cohort by number of future observations","title":"Demographic Requirements","text":"can also specify minimum number days prior observations individual using requireFutureObservation() CohortConstructor. flow chart illustrates changes cohort 1 restricted include individuals least 365 days future observations. 14 individuals 206 records excluded.","code":"cdm$medications <- cdm$medications %>% requireFutureObservation(indexDate = \"cohort_start_date\", minFutureObservation = 365) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"applying-multiple-demographic-requirements-to-a-cohort","dir":"Articles","previous_headings":"","what":"Applying multiple demographic requirements to a cohort","title":"Demographic Requirements","text":"can implement multiple demographic requirements cohort using requireDemographics() CohortConstructor. flow chart illustrates changes cohort 1 multiple demographic restrictions, female individuals 18 100 years old, least 365 days prior future observations included. 1,413 individuals 6156 records excluded.","code":"cdm$medications <- cdm$medications %>% requireDemographics(indexDate = \"cohort_start_date\", ageRange = c(18,100), sex = \"Female\", minPriorObservation = 365, minFutureObservation = 365) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a04_require_intersections.html","id":"restrictions-on-cohort-presence","dir":"Articles","previous_headings":"","what":"Restrictions on cohort presence","title":"Requirements on Presence and Absence","text":"require individuals medication cohorts seen (seen) another cohort. can use requireCohortIntersect() function, requiring individuals one intersections GI bleed cohort. flow chart illustrates changes cohort 1 (users acetaminophen) restricted include individuals intersect GI bleed cohort least cohort start date. 2,296 individuals 8,765 records excluded. Instead requiring individuals intersect GI bleed cohort, instead require don’t intersect . case can use requireCohortIntersect() function, time set intersections argument 0 require individuals’ absence cohort rather presence . flow chart illustrates changes cohort 1 restricted include individuals intersects GI bleed cohort cohort start date. 36 individuals 600 records excluded.","code":"cdm$medications_gi_bleed <- cdm$medications %>% requireCohortIntersect(intersections = c(1,Inf), targetCohortTable = \"gi_bleed\", targetCohortId = 1, indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) cdm$medications_no_gi_bleed <- cdm$medications %>% requireCohortIntersect(intersections = 0, targetCohortTable = \"gi_bleed\", targetCohortId = 1, indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_no_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a04_require_intersections.html","id":"restrictions-on-concept-presence","dir":"Articles","previous_headings":"","what":"Restrictions on concept presence","title":"Requirements on Presence and Absence","text":"require individuals medication cohorts seen (seen) events related concept list. can use requireConceptIntersect() function, allowing us filter cohort based whether events GI bleeding entered cohort. flow chart illustrates changes cohort 1 restricted include individuals events GI bleeding least cohort start date. 2,296 individuals 8,765 records excluded. Instead requiring individuals events GI bleeding, instead require don’t events . case can use requireConceptIntersect() function, time set intersections argument 0 require individuals without past events GI bleeding. flow chart illustrates changes cohort 1 restricted include individuals events GI bleeding cohort start date. 36 individuals 600 records excluded.","code":"cdm$medications_gi_bleed <- cdm$medications %>% requireConceptIntersect(conceptSet = list(\"gi_bleed\" = 192671), indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) cdm$medications_no_gi_bleed <- cdm$medications %>% requireConceptIntersect(intersections = 0, conceptSet = list(\"gi_bleed\" = 192671), indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_no_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a04_require_intersections.html","id":"restrictions-on-presence-in-clinical-tables","dir":"Articles","previous_headings":"","what":"Restrictions on presence in clinical tables","title":"Requirements on Presence and Absence","text":"clinical table table contains ‘raw’ clinical data. can use clinical tables filter cohorts using requireTableIntersect() function. allow us filter individuals medications cohort based whether intersections GI bleed clinical table . flow chart illustrates changes cohort 1 restricted include individuals intersect GI bleeding clinical table least cohort start date. 2,296 individuals 8,765 records excluded. Instead requiring individuals intersect GI bleed clinical table, instead require don’t intersect . case can use requireCohortIntersect() function, time set intersections argument 0 require individuals’ absence GI bleed clinical table. flow chart illustrates changes cohort 1 restricted include individuals intersects GI bleeding clinical table cohort start date. 36 individuals 600 records excluded.","code":"cdm$medications_gi_bleed <- cdm$medications %>% requireTableIntersect(tableName = \"gi_bleed\", indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) cdm$medications_no_gi_bleed <- cdm$medications %>% requireTableIntersect(tableName = \"gi_bleed\", indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_no_gi_bleed\", intersections = 0) summary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a04_require_intersections.html","id":"restrictions-on-deaths","dir":"Articles","previous_headings":"","what":"Restrictions on deaths","title":"Requirements on Presence and Absence","text":"require individuals medication cohorts seen () death. can use requireDeathFlag() function, requiring individuals seen (seen) died cohort start date. flow chart illustrates changes cohort 1 restricted include individuals died cohort start date. None individuals cohort 1 died therefore excluded cohort. exclude individuals died add argument ‘negate = TRUE’ function requireDeathFlag(). flow chart illustrates changes cohort 1 restricted include individuals die cohort start date. None individuals cohort 1 died therefore one excluded.","code":"cdm$medications_deaths <- cdm$medications %>% requireDeathFlag(window = c(0,Inf), name = \"medications_deaths\") summary_attrition <- summariseCohortAttrition(cdm$medications_deaths) plotCohortAttrition(summary_attrition, cohortId = 1) cdm$medications_no_deaths <- cdm$medications %>% requireDeathFlag(window = c(0,Inf), name = \"medications_no_deaths\", negate = TRUE) summary_attrition <- summariseCohortAttrition(cdm$medications_no_deaths) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Generate a matched age and sex cohort","text":"CohortConstructor packages includes function obtain age sex matched cohort, generateMatchedCohortSet() function. vignette, explore usage function.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"create-mock-data","dir":"Articles","previous_headings":"Introduction","what":"Create mock data","title":"Generate a matched age and sex cohort","text":"first use mockDrugUtilisation() function DrugUtilisation package create mock data. use cohort1 explore generateMatchedCohortSet(), let us first use cohort_attrition() CDMConnector package explore cohort:","code":"library(CohortConstructor) library(dplyr) cdm <- mockCohortConstructor(nPerson = 1000) CDMConnector::cohort_set(cdm$cohort1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"use-generatematchedcohortset-to-create-an-age-sex-matched-cohort","dir":"Articles","previous_headings":"","what":"Use generateMatchedCohortSet() to create an age-sex matched cohort","title":"Generate a matched age and sex cohort","text":"Let us first see example function works. usage, need provide cdm object, targetCohortName, name table containing cohort interest, name new generated tibble containing cohort matched cohort. also use argument targetCohortId specify want matched cohort cohort_definition_id = 1. Notice generated tibble, two cohorts: cohort_definition_id = 1 (original cohort), cohort_definition_id = 4 (matched cohort). target_cohort_name column indicates original cohort. match_sex match_year_of_birth adopt boolean values (TRUE/FALSE) indicating matched sex age, . match_status indicate original cohort (target) matched cohort (matched). target_cohort_id indicates cohort_id original cohort. Check exclusion criteria applied generate new cohorts using cohort_attrition() CDMConnector package: Briefly, original cohort, exclude first individuals match, individuals matching pair observation assigned cohort_start_date. matched cohort, start whole database first exclude individuals original cohort. Afterwards, exclude individuals match, individuals observation assigned cohort_start_date, finally remove many individuals required fulfill ratio. Notice matching pairs randomly assigned, probable every time execute function, generated cohorts change. Use set.seed() avoid .","code":"cdm$matched_cohort1 <- matchCohorts( cohort = cdm$cohort1, cohortId = 1, name = \"matched_cohort1\") CDMConnector::cohort_set(cdm$matched_cohort1) # Original cohort CDMConnector::cohort_attrition(cdm$matched_cohort1) %>% filter(cohort_definition_id == 1) # Matched cohort CDMConnector::cohort_attrition(cdm$matched_cohort1) %>% filter(cohort_definition_id == 4)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"matchsex-parameter","dir":"Articles","previous_headings":"Use generateMatchedCohortSet() to create an age-sex matched cohort","what":"matchSex parameter","title":"Generate a matched age and sex cohort","text":"matchSex boolean parameter (TRUE/FALSE) indicating want match sex (TRUE) want (FALSE).","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"matchyear-parameter","dir":"Articles","previous_headings":"Use generateMatchedCohortSet() to create an age-sex matched cohort","what":"matchYear parameter","title":"Generate a matched age and sex cohort","text":"matchYear another boolean parameter (TRUE/FALSE) indicating want match age (TRUE) want (FALSE). Notice matchSex = FALSE matchYear = FALSE, obtain unmatched comparator cohort.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"ratio-parameter","dir":"Articles","previous_headings":"Use generateMatchedCohortSet() to create an age-sex matched cohort","what":"ratio parameter","title":"Generate a matched age and sex cohort","text":"default matching ratio 1:1 (ratio = 1). Use cohort_counts() CDMConnector check matching done desired. can modify ratio parameter tailor matched cohort. ratio can adopt values 1 Inf.","code":"CDMConnector::cohort_count(cdm$matched_cohort1) cdm$matched_cohort2 <- matchCohorts( cohort = cdm$cohort1, cohortId = 1, name = \"matched_cohort2\", ratio = Inf) CDMConnector::cohort_count(cdm$matched_cohort2)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"generate-matched-cohorts-simultaneously-across-multiple-cohorts","dir":"Articles","previous_headings":"Use generateMatchedCohortSet() to create an age-sex matched cohort","what":"Generate matched cohorts simultaneously across multiple cohorts","title":"Generate a matched age and sex cohort","text":"functionalities can implemented across multiple cohorts simultaneously. Specify targetCohortId parameter cohorts interest. set NULL, cohorts present targetCohortName matched. Notice cohort (independent cohorts) matched cohort.","code":"cdm$matched_cohort3 <- matchCohorts( cohort = cdm$cohort1, cohortId = c(1,3), name = \"matched_cohort3\", ratio = 2) CDMConnector::cohort_set(cdm$matched_cohort3) %>% arrange(cohort_definition_id) CDMConnector::cohort_count(cdm$matched_cohort3) %>% arrange(cohort_definition_id)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"CohortConstructor benchmark","text":"Cohorts fundamental building block studies use OMOP CDM, identifying people satisfy one inclusion criteria duration time based clinical records. Currently cohorts typically built using CIRCE allows complex cohorts represented using JSON. JSON converted SQL execution database containing data mapped OMOP CDM. CIRCE JSON can created via ATLAS GUI programmatically via Capr R package. However, although powerful tool expressing operationalising cohort definitions, SQL generated can cumbersome especially complex cohort definitions, moreover cohorts instantiated independently, leading duplicated work. CohortConstructor package offers alternative approach, emphasizing cohort building pipeline format. first creates base cohorts applies specific inclusion criteria. Unlike “definition” approach, ::cohorts built independently, CohortConstructor follows “domain” approach, minimizes redundant queries large OMOP tables. details approach can found Introduction vignette. benchmarked package using nine phenotypes OHDSI Phenotype library cover range concept domains, entry inclusion criteria, cohort exit options. replicated cohorts using CodelistGenerator CohortConstructor assess computational time agreement CIRCE CohortConstructor.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"code-and-collaboration","dir":"Articles","previous_headings":"Introduction","what":"Code and collaboration","title":"CohortConstructor benchmark","text":"benchmarking code available BenchmarkCohortConstructor repository GitHub. interested running code database, feel free reach us assistance, can also update vignette results! :) benchmark script executed following four databases: CPRD Gold: primary care database UK, capturing data mostly Northern Ireland, Wales, Scotland clinics. benchmark utilized 100,000-person sample dataset, managed using PostgreSQL. CPRD Aurum: Another UK primary care database, primarily covering clinics England. database managed SQL Server. Coriva: sample approximately 400,000 patients Estonia National Health Insurance database, managed PostgreSQL. OHDSI SQL Server: mock OMOP CDM dataset provided OHDSI, hosted SQL Server. table presents number records OMOP tables used benchmark script participating databases.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"cohorts","dir":"Articles","previous_headings":"","what":"Cohorts","title":"CohortConstructor benchmark","text":"replicated following cohorts OHDSI phenotype library: COVID-19 (ID 56), inpatient hospitalisation (23), new users beta blockers nested essential hypertension (1049), transverse myelitis (63), major non cardiac surgery (1289), asthma without COPD (27), endometriosis procedure (722), new fluoroquinolone users (1043), acquired neutropenia unspecified leukopenia (213). COVID-19 cohort used evaluate performance common cohort stratifications. compare package CIRCE, created definitions Atlas, stratified age groups sex, available benchmark GitHub repository benchmark code.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"cohort-counts-and-overlap","dir":"Articles","previous_headings":"Cohorts","what":"Cohort counts and overlap","title":"CohortConstructor benchmark","text":"following table displays number records subjects cohort across participating databases: also computed overlap patients CIRCE CohortConstructor cohorts, results shown plot :","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"performance","dir":"Articles","previous_headings":"","what":"Performance","title":"CohortConstructor benchmark","text":"evaluate CohortConstructor performance generated CIRCE cohorts using functionalities provided CodelistGenerator CohortConstructor, measured computational time taken. Two different approaches CohortConstructor tested: definition: created cohorts seprately. domain: nine targeted cohorts created together set, following domain approach described Introduction vignette. Briefly, approach involves creating base cohorts , requiring one call involved OMOP table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"by-definition","dir":"Articles","previous_headings":"Performance","what":"By definition","title":"CohortConstructor benchmark","text":"following plot shows times taken create cohort using CIRCE CohortConstructor cohorts created separately.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"by-domain","dir":"Articles","previous_headings":"Performance","what":"By domain","title":"CohortConstructor benchmark","text":"table depicts total time took create nine cohorts using domain approach CohortConstructor.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"cohort-stratification","dir":"Articles","previous_headings":"Performance","what":"Cohort stratification","title":"CohortConstructor benchmark","text":"Cohorts often stratified studies. Atlas cohort definitions, stratum requires new CIRCE JSON instantiated, CohortConstructor allows stratifications generated overall cohort. following table shows time taken create age sex stratifications COVID-19 cohort CIRCE CohortConstructor.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"use-of-sql-indexes","dir":"Articles","previous_headings":"Performance","what":"Use of SQL indexes","title":"CohortConstructor benchmark","text":"Postgres SQL databases, package uses indexes conceptCohort default. evaluate much indexes reduce computation time, instantiated subset concept sets benchmark, without indexes. Four calls made conceptCohort, involving different number OMOP tables. combinations : Drug exposure Drug exposure + condition occurrence Drug exposure + condition occurrence + procedure occurrence Drug exposure + condition occurrence + procedure occurrence + measurement plot shows computation time without SQL indexes scenario:","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Edward Burn. Author, maintainer. Marti Catala. Author. Nuria Mercade-Besora. Author. Marta Alcalde-Herraiz. Author. Mike Du. Author. Yuchen Guo. Author. Xihang Chen. Author. Kim Lopez-Guell. Author. Elin Rowlands. Author.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Burn E, Catala M, Mercade-Besora N, Alcalde-Herraiz M, Du M, Guo Y, Chen X, Lopez-Guell K, Rowlands E (2024). CohortConstructor: Build Manipulate Study Cohorts Using Common Data Model. R package version 0.3.0.900, https://ohdsi.github.io/CohortConstructor/.","code":"@Manual{, title = {CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model}, author = {Edward Burn and Marti Catala and Nuria Mercade-Besora and Marta Alcalde-Herraiz and Mike Du and Yuchen Guo and Xihang Chen and Kim Lopez-Guell and Elin Rowlands}, year = {2024}, note = {R package version 0.3.0.900}, url = {https://ohdsi.github.io/CohortConstructor/}, }"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"cohortconstructor-","dir":"","previous_headings":"","what":"Build and Manipulate Study Cohorts Using a Common Data Model","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"goal CohortConstructor support creation manipulation study cohorts data mapped OMOP CDM.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"package can installed CRAN: can install development version package GitHub:","code":"install.packages(\"CohortConstructor\") # install.packages(\"devtools\") devtools::install_github(\"ohdsi/CohortConstructor\")"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"creating-and-manipulating-cohorts","dir":"","previous_headings":"","what":"Creating and manipulating cohorts","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"illustrate functionality provided CohortConstructor let’s create set fracture cohorts using Eunomia dataset. ’ll first load required packages create cdm reference data.","code":"library(omopgenerics) library(CDMConnector) library(PatientProfiles) library(dplyr) library(CohortConstructor) library(CohortCharacteristics) con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir()) cdm <- cdm_from_con(con, cdm_schema = \"main\", write_schema = c(prefix = \"my_study_\", schema = \"main\")) cdm #> #> ── # OMOP CDM reference (duckdb) of Synthea synthetic health database ────────── #> • omop tables: person, observation_period, visit_occurrence, visit_detail, #> condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, #> measurement, observation, death, note, note_nlp, specimen, fact_relationship, #> location, care_site, provider, payer_plan_period, cost, drug_era, dose_era, #> condition_era, metadata, cdm_source, concept, vocabulary, domain, #> concept_class, concept_relationship, relationship, concept_synonym, #> concept_ancestor, source_to_concept_map, drug_strength #> • cohort tables: - #> • achilles tables: - #> • other tables: -"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"generating-concept-based-fracture-cohorts","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Generating concept-based fracture cohorts","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"start making simple concept-based cohort fracture interest. First create codelist ankle, forearm hip fractures (note, just use one code using synthetic data). Now can quickly create set cohorts fracture type. need provide codes defined get cohort back, cohort end date set event date associated records, overlapping records collapsed, records observation kept. can see starting cohorts, add additional restrictions, following associated settings, counts, attrition.","code":"fracture_codes <- newCodelist(list(\"ankle_fracture\" = 4059173L, \"forearm_fracture\" = 4278672L, \"hip_fracture\" = 4230399L)) fracture_codes #> #> ── 3 codelists ───────────────────────────────────────────────────────────────── #> #> - ankle_fracture (1 codes) #> - forearm_fracture (1 codes) #> - hip_fracture (1 codes) cdm$fractures <- cdm |> conceptCohort(conceptSet = fracture_codes, name = \"fractures\") settings(cdm$fractures) %>% glimpse() #> Rows: 3 #> Columns: 4 #> $ cohort_definition_id 1, 2, 3 #> $ cohort_name \"ankle_fracture\", \"forearm_fracture\", \"hip_fractu… #> $ cdm_version \"5.3\", \"5.3\", \"5.3\" #> $ vocabulary_version \"v5.0 18-JAN-19\", \"v5.0 18-JAN-19\", \"v5.0 18-JAN-… cohort_count(cdm$fractures) %>% glimpse() #> Rows: 3 #> Columns: 3 #> $ cohort_definition_id 1, 2, 3 #> $ number_records 464, 569, 138 #> $ number_subjects 427, 510, 132 attrition(cdm$fractures) %>% glimpse() #> Rows: 3 #> Columns: 7 #> $ cohort_definition_id 1, 2, 3 #> $ number_records 464, 569, 138 #> $ number_subjects 427, 510, 132 #> $ reason_id 1, 1, 1 #> $ reason \"Initial qualifying events\", \"Initial qualifying … #> $ excluded_records 0, 0, 0 #> $ excluded_subjects 0, 0, 0"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"create-an-overall-fracture-cohort","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Create an overall fracture cohort","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"far created three separate fracture cohorts. Let’s say also want cohort people fractures. union three cohorts create overall cohort like :","code":"cdm$fractures <- unionCohorts(cdm$fractures, cohortName = \"any_fracture\", keepOriginalCohorts = TRUE, name =\"fractures\") settings(cdm$fractures) #> # A tibble: 4 × 5 #> cohort_definition_id cohort_name cdm_version vocabulary_version gap #> #> 1 1 ankle_fracture 5.3 v5.0 18-JAN-19 NA #> 2 2 forearm_fracture 5.3 v5.0 18-JAN-19 NA #> 3 3 hip_fracture 5.3 v5.0 18-JAN-19 NA #> 4 4 any_fracture 0 cohortCount(cdm$fractures) #> # A tibble: 4 × 3 #> cohort_definition_id number_records number_subjects #> #> 1 1 464 427 #> 2 2 569 510 #> 3 3 138 132 #> 4 4 1171 924"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"require-in-date-range","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Require in date range","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"created base fracture cohort, can start applying additional cohort requirements. example, first can require individuals’ cohort start date fall within certain date range. Now ’ve applied date restriction, can see cohort attributes updated","code":"cdm$fractures <- cdm$fractures %>% requireInDateRange(dateRange = as.Date(c(\"2000-01-01\", \"2020-01-01\"))) cohort_count(cdm$fractures) %>% glimpse() #> Rows: 4 #> Columns: 3 #> $ cohort_definition_id 1, 2, 3, 4 #> $ number_records 108, 152, 62, 322 #> $ number_subjects 104, 143, 60, 287 attrition(cdm$fractures) %>% filter(reason == \"cohort_start_date between 2000-01-01 & 2020-01-01\") %>% glimpse() #> Rows: 0 #> Columns: 7 #> $ cohort_definition_id #> $ number_records #> $ number_subjects #> $ reason_id #> $ reason #> $ excluded_records #> $ excluded_subjects "},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"applying-demographic-requirements","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Applying demographic requirements","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"can also add restrictions patient characteristics age (cohort start date default) sex. can see many individuals ’ve lost applying criteria.","code":"cdm$fractures <- cdm$fractures %>% requireDemographics(ageRange = list(c(40, 65)), sex = \"Female\") attrition(cdm$fractures) %>% filter(reason == \"Age requirement: 40 to 65\") %>% glimpse() #> Rows: 4 #> Columns: 7 #> $ cohort_definition_id 1, 2, 3, 4 #> $ number_records 43, 64, 22, 129 #> $ number_subjects 43, 62, 22, 122 #> $ reason_id 4, 4, 4, 4 #> $ reason \"Age requirement: 40 to 65\", \"Age requirement: 40… #> $ excluded_records 65, 88, 40, 193 #> $ excluded_subjects 61, 81, 38, 165 attrition(cdm$fractures) %>% filter(reason == \"Sex requirement: Female\") %>% glimpse() #> Rows: 4 #> Columns: 7 #> $ cohort_definition_id 1, 2, 3, 4 #> $ number_records 19, 37, 12, 68 #> $ number_subjects 19, 36, 12, 65 #> $ reason_id 5, 5, 5, 5 #> $ reason \"Sex requirement: Female\", \"Sex requirement: Fema… #> $ excluded_records 24, 27, 10, 61 #> $ excluded_subjects 24, 26, 10, 57"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"require-presence-in-another-cohort","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Require presence in another cohort","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"can also require individuals () another cohort window. example require study participants GI bleed cohort time prior entry fractures cohort.","code":"cdm$gibleed <- cdm |> conceptCohort(conceptSet = list(\"gibleed\" = 192671L), name = \"gibleed\") cdm$fractures <- cdm$fractures %>% requireCohortIntersect(targetCohortTable = \"gibleed\", intersections = 0, window = c(-Inf, 0)) attrition(cdm$fractures) %>% filter(reason == \"Not in cohort gibleed between -Inf & 0 days relative to cohort_start_date\") %>% glimpse() #> Rows: 4 #> Columns: 7 #> $ cohort_definition_id 1, 2, 3, 4 #> $ number_records 14, 30, 10, 54 #> $ number_subjects 14, 30, 10, 52 #> $ reason_id 8, 8, 8, 8 #> $ reason \"Not in cohort gibleed between -Inf & 0 days rela… #> $ excluded_records 5, 7, 2, 14 #> $ excluded_subjects 5, 6, 2, 13 cdmDisconnect(cdm)"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"more-information","dir":"","previous_headings":"Creating and manipulating cohorts","what":"More information","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"CohortConstructor provides much functionality creating manipulating cohorts. See package website details.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/CohortConstructor-package.html","id":null,"dir":"Reference","previous_headings":"","what":"CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model — CohortConstructor-package","title":"CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model — CohortConstructor-package","text":"Create manipulate study cohorts data mapped Observational Medical Outcomes Partnership Common Data Model.","code":""},{"path":[]},{"path":"https://ohdsi.github.io/CohortConstructor/reference/CohortConstructor-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model — CohortConstructor-package","text":"Maintainer: Edward Burn edward.burn@ndorms.ox.ac.uk (ORCID) Authors: Marti Catala marti.catalasabate@ndorms.ox.ac.uk (ORCID) Nuria Mercade-Besora nuria.mercadebesora@ndorms.ox.ac.uk (ORCID) Marta Alcalde-Herraiz marta.alcaldeherraiz@ndorms.ox.ac.uk (ORCID) Mike Du mike.du@ndorms.ox.ac.uk (ORCID) Yuchen Guo yuchen.guo@ndorms.ox.ac.uk (ORCID) Xihang Chen xihang.chen@ndorms.ox.ac.uk (ORCID) Kim Lopez kim.lopez@spc.ox.ac.uk (ORCID) Elin Rowlands elin.rowlands@ndorms.ox.ac.uk (ORCID)","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/benchmarkData.html","id":null,"dir":"Reference","previous_headings":"","what":"Benchmarking results — benchmarkData","title":"Benchmarking results — benchmarkData","text":"Benchmarking results","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/benchmarkData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Benchmarking results — benchmarkData","text":"","code":"benchmarkData"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/benchmarkData.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Benchmarking results — benchmarkData","text":"list results benchmarking","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cdmDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of cdm. — cdmDoc","title":"Helper for consistent documentation of cdm. — cdmDoc","text":"Helper consistent documentation cdm.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cdmDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of cdm. — cdmDoc","text":"cdm cdm reference.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of cohort. — cohortDoc","title":"Helper for consistent documentation of cohort. — cohortDoc","text":"Helper consistent documentation cohort.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of cohort. — cohortDoc","text":"cohort cohort table cdm reference.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortIdModifyDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of cohortId. — cohortIdModifyDoc","title":"Helper for consistent documentation of cohortId. — cohortIdModifyDoc","text":"Helper consistent documentation cohortId.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortIdModifyDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of cohortId. — cohortIdModifyDoc","text":"cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortIdSubsetDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of cohortId. — cohortIdSubsetDoc","title":"Helper for consistent documentation of cohortId. — cohortIdSubsetDoc","text":"Helper consistent documentation cohortId.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortIdSubsetDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of cohortId. — cohortIdSubsetDoc","text":"cohortId Vector identifying cohorts include (cohort_definition_id cohort_name). Cohorts included removed cohort set.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/collapseCohorts.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","title":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","text":"collapseCohorts() concatenates cohort records, allowing number days one finishing next starting.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/collapseCohorts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","text":"","code":"collapseCohorts(cohort, cohortId = NULL, gap = 0, name = tableName(cohort))"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/collapseCohorts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. gap Number days two subsequent cohort entries merged single cohort record. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/collapseCohorts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","text":"cohort table","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/columnDateDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of dateColumns and returnReason. — columnDateDoc","title":"Helper for consistent documentation of dateColumns and returnReason. — columnDateDoc","text":"Helper consistent documentation dateColumns returnReason.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/columnDateDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of dateColumns and returnReason. — columnDateDoc","text":"dateColumns Character vector indicating date columns cohort table consider. returnReason TRUE return column indicating dateColumns used.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":null,"dir":"Reference","previous_headings":"","what":"Create cohorts based on a concept set — conceptCohort","title":"Create cohorts based on a concept set — conceptCohort","text":"conceptCohort() creates cohort table patient records clinical tables OMOP CDM. following tables currently supported creating concept cohorts: condition_occurrence device_exposure drug_exposure measurement observation procedure_occurrence visit_occurrence Cohort duration based record start end (e.g. condition_start_date condition_end_date records coming condition_occurrence tables). resulting table satisfies requirements OMOP CDM cohort table: Overlapping records collapsed single cohort entry. record starts outside observation period silently ignored. record ends outside observation period trimmed end preceding observation period end date.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create cohorts based on a concept set — conceptCohort","text":"","code":"conceptCohort( cdm, conceptSet, name, exit = \"event_end_date\", useSourceFields = FALSE )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create cohorts based on a concept set — conceptCohort","text":"cdm cdm reference. conceptSet conceptSet, can either codelist conceptSetExpression. name Name new cohort table created cdm object. exit cohort end date defined. Can either \"event_end_date\" \"event_start_date\". useSourceFields TRUE, source concept_id fields also used identifying relevant clinical records. FALSE, standard concept_id fields used.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create cohorts based on a concept set — conceptCohort","text":"cohort table","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create cohorts based on a concept set — conceptCohort","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(conditionOccurrence = TRUE) #> Note: method with signature ‘DBIConnection#Id’ chosen for function ‘dbExistsTable’, #> target signature ‘duckdb_connection#Id’. #> \"duckdb_connection#ANY\" would also be valid cohort <- conceptCohort(cdm = cdm, conceptSet = list(a = 1), name = \"cohort\") #> Warning: ! `codelist` contains numeric values, they are casted to integers. #> ✖ Domain NA (1 concept) excluded because it is not supported. #> ℹ No cohort entries found, returning empty cohort table. cohort |> attrition() #> # A tibble: 1 × 7 #> cohort_definition_id number_records number_subjects reason_id reason #> #> 1 1 0 0 1 Initial qualify… #> # ℹ 2 more variables: excluded_records , excluded_subjects # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptSetDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of conceptSet. — conceptSetDoc","title":"Helper for consistent documentation of conceptSet. — conceptSetDoc","text":"Helper consistent documentation conceptSet.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptSetDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of conceptSet. — conceptSetDoc","text":"conceptSet conceptSet, can either codelist conceptSetExpression.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":null,"dir":"Reference","previous_headings":"","what":"Create cohorts based on patient demographics — demographicsCohort","title":"Create cohorts based on patient demographics — demographicsCohort","text":"demographicsCohort() creates cohort table based patient characteristics. individual satisfies criteria enter cohort. stop satisfying criteria cohort entry ends.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create cohorts based on patient demographics — demographicsCohort","text":"","code":"demographicsCohort( cdm, name, ageRange = NULL, sex = NULL, minPriorObservation = NULL )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create cohorts based on patient demographics — demographicsCohort","text":"cdm cdm reference. name Name new cohort table created cdm object. ageRange list vectors specifying minimum maximum age. sex Can \"\", \"Male\" \"Female\". minPriorObservation minimum number continuous prior observation days database.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create cohorts based on patient demographics — demographicsCohort","text":"cohort table","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create cohorts based on patient demographics — demographicsCohort","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor() cohort <- cdm |> demographicsCohort(name = \"cohort3\", ageRange = c(18,40), sex = \"Male\") #> ! cohort columns will be reordered to match the expected order: #> cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date. #> ℹ Building new trimmed cohort #> Adding demographics information #> Creating initial cohort #> Trim sex #> Trim age #> ✔ Cohort trimmed attrition(cohort) #> # A tibble: 3 × 7 #> cohort_definition_id number_records number_subjects reason_id reason #> #> 1 1 10 10 1 Initial qualify… #> 2 1 2 2 2 Sex requirement… #> 3 1 2 2 3 Age requirement… #> # ℹ 2 more variables: excluded_records , excluded_subjects # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":null,"dir":"Reference","previous_headings":"","what":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"entryAtFirstDate() resets cohort start date based set specified column dates. first date occurs chosen.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"","code":"entryAtFirstDate( cohort, dateColumns, cohortId = NULL, returnReason = TRUE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"cohort cohort table cdm reference. dateColumns Character vector indicating date columns cohort table consider. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. returnReason TRUE return column indicating dateColumns used. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(tables = list( \"cohort\" = dplyr::tibble( cohort_definition_id = 1, subject_id = c(1, 2, 3, 4), cohort_start_date = as.Date(c(\"2000-06-03\", \"2000-01-01\", \"2015-01-15\", \"2000-12-09\")), cohort_end_date = as.Date(c(\"2001-09-01\", \"2001-01-12\", \"2015-02-15\", \"2002-12-09\")), date_1 = as.Date(c(\"2001-08-01\", \"2001-01-01\", \"2015-01-15\", \"2002-12-09\")), date_2 = as.Date(c(\"2001-08-01\", NA, \"2015-02-14\", \"2002-12-09\")) ) )) #> Warning: ! 7 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `gender_concept_id` is numeric but expected integer #> • `year_of_birth` is numeric but expected integer #> • `month_of_birth` is numeric but expected integer #> • `race_concept_id` is numeric but expected integer #> • `ethnicity_concept_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 2 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> • `period_type_concept_id` is numeric but expected integer #> Warning: ! 9 column in cdm_source do not match expected column type: #> • `cdm_source_abbreviation` is logical but expected character #> • `cdm_holder` is logical but expected character #> • `source_description` is logical but expected character #> • `source_documentation_reference` is logical but expected character #> • `cdm_etl_reference` is logical but expected character #> • `source_release_date` is logical but expected date #> • `cdm_release_date` is logical but expected date #> • `cdm_version` is numeric but expected character #> • `vocabulary_version` is logical but expected character #> Warning: ! 3 column in concept do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 1 column in vocabulary do not match expected column type: #> • `vocabulary_concept_id` is numeric but expected integer #> Warning: ! 5 column in concept_relationship do not match expected column type: #> • `concept_id_1` is numeric but expected integer #> • `concept_id_2` is numeric but expected integer #> • `valid_start_date` is logical but expected date #> • `valid_end_date` is logical but expected date #> • `invalid_reason` is logical but expected character #> Warning: ! 2 column in concept_synonym do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `language_concept_id` is numeric but expected integer #> Warning: ! 4 column in concept_ancestor do not match expected column type: #> • `ancestor_concept_id` is numeric but expected integer #> • `descendant_concept_id` is numeric but expected integer #> • `min_levels_of_separation` is numeric but expected integer #> • `max_levels_of_separation` is numeric but expected integer #> Warning: ! 9 column in drug_strength do not match expected column type: #> • `drug_concept_id` is numeric but expected integer #> • `ingredient_concept_id` is numeric but expected integer #> • `amount_unit_concept_id` is numeric but expected integer #> • `numerator_unit_concept_id` is numeric but expected integer #> • `denominator_value` is logical but expected numeric #> • `denominator_unit_concept_id` is numeric but expected integer #> • `box_size` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> Warning: ! 2 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 1 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer cdm$cohort |> entryAtLastDate(dateColumns = c(\"date_1\", \"date_2\")) #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> # Source: table [4 x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date entry_reason #> #> 1 1 4 2002-12-09 2002-12-09 date_1; dat… #> 2 1 1 2001-08-01 2001-09-01 date_1; dat… #> 3 1 2 2001-01-01 2001-01-12 date_1 #> 4 1 3 2015-02-14 2015-02-15 date_2 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort start date to the last of a set of column dates — entryAtLastDate","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"entryAtLastDate() resets cohort end date based set specified column dates. last date chosen.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"","code":"entryAtLastDate( cohort, dateColumns, cohortId = NULL, returnReason = TRUE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"cohort cohort table cdm reference. dateColumns Character vector indicating date columns cohort table consider. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. returnReason TRUE return column indicating dateColumns used. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(tables = list( \"cohort\" = dplyr::tibble( cohort_definition_id = 1, subject_id = c(1, 2, 3, 4), cohort_start_date = as.Date(c(\"2000-06-03\", \"2000-01-01\", \"2015-01-15\", \"2000-12-09\")), cohort_end_date = as.Date(c(\"2001-09-01\", \"2001-01-12\", \"2015-02-15\", \"2002-12-09\")), date_1 = as.Date(c(\"2001-08-01\", \"2001-01-01\", \"2015-01-15\", \"2002-12-09\")), date_2 = as.Date(c(\"2001-08-01\", NA, \"2015-02-14\", \"2002-12-09\")) ) )) #> Warning: ! 7 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `gender_concept_id` is numeric but expected integer #> • `year_of_birth` is numeric but expected integer #> • `month_of_birth` is numeric but expected integer #> • `race_concept_id` is numeric but expected integer #> • `ethnicity_concept_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 2 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> • `period_type_concept_id` is numeric but expected integer #> Warning: ! 9 column in cdm_source do not match expected column type: #> • `cdm_source_abbreviation` is logical but expected character #> • `cdm_holder` is logical but expected character #> • `source_description` is logical but expected character #> • `source_documentation_reference` is logical but expected character #> • `cdm_etl_reference` is logical but expected character #> • `source_release_date` is logical but expected date #> • `cdm_release_date` is logical but expected date #> • `cdm_version` is numeric but expected character #> • `vocabulary_version` is logical but expected character #> Warning: ! 3 column in concept do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 1 column in vocabulary do not match expected column type: #> • `vocabulary_concept_id` is numeric but expected integer #> Warning: ! 5 column in concept_relationship do not match expected column type: #> • `concept_id_1` is numeric but expected integer #> • `concept_id_2` is numeric but expected integer #> • `valid_start_date` is logical but expected date #> • `valid_end_date` is logical but expected date #> • `invalid_reason` is logical but expected character #> Warning: ! 2 column in concept_synonym do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `language_concept_id` is numeric but expected integer #> Warning: ! 4 column in concept_ancestor do not match expected column type: #> • `ancestor_concept_id` is numeric but expected integer #> • `descendant_concept_id` is numeric but expected integer #> • `min_levels_of_separation` is numeric but expected integer #> • `max_levels_of_separation` is numeric but expected integer #> Warning: ! 9 column in drug_strength do not match expected column type: #> • `drug_concept_id` is numeric but expected integer #> • `ingredient_concept_id` is numeric but expected integer #> • `amount_unit_concept_id` is numeric but expected integer #> • `numerator_unit_concept_id` is numeric but expected integer #> • `denominator_value` is logical but expected numeric #> • `denominator_unit_concept_id` is numeric but expected integer #> • `box_size` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> Warning: ! 2 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 1 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer cdm$cohort |> entryAtLastDate(dateColumns = c(\"date_1\", \"date_2\")) #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> # Source: table [4 x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date entry_reason #> #> 1 1 2 2001-01-01 2001-01-12 date_1 #> 2 1 3 2015-02-14 2015-02-15 date_2 #> 3 1 1 2001-08-01 2001-09-01 date_1; dat… #> 4 1 4 2002-12-09 2002-12-09 date_1; dat… # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort end date to death date — exitAtDeath","title":"Set cohort end date to death date — exitAtDeath","text":"functions changes cohort end date subject's death date. case generates overlapping records cohort, overlapping entries merged.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort end date to death date — exitAtDeath","text":"","code":"exitAtDeath( cohort, cohortId = NULL, requireDeath = FALSE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort end date to death date — exitAtDeath","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. requireDeath TRUE, subjects without death record dropped, FALSE end date left . name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort end date to death date — exitAtDeath","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort end date to death date — exitAtDeath","text":"","code":"# \\donttest{ library(PatientProfiles) library(CohortConstructor) cdm <- mockPatientProfiles() cdm$cohort1 |> exitAtDeath() #> # Source: table [10 x 4] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> #> 1 1 1 1943-11-26 1949-06-21 #> 2 2 4 1949-03-07 1951-05-08 #> 3 1 10 1948-08-08 1952-03-27 #> 4 3 6 1966-09-27 1975-03-05 #> 5 2 5 1966-05-05 1970-11-15 #> 6 2 3 1939-07-24 1944-01-14 #> 7 3 8 1921-10-07 1931-05-01 #> 8 1 9 1940-05-01 1945-06-29 #> 9 3 2 1918-06-14 1921-11-28 #> 10 3 7 1962-05-14 1964-03-08 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"exitAtFirstDate() resets cohort end date based set specified column dates. first date occurs chosen.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"","code":"exitAtFirstDate( cohort, dateColumns, cohortId = NULL, returnReason = TRUE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"cohort cohort table cdm reference. dateColumns Character vector indicating date columns cohort table consider. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. returnReason TRUE return column indicating dateColumns used. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(tables = list( \"cohort\" = dplyr::tibble( cohort_definition_id = 1, subject_id = c(1, 2, 3, 4), cohort_start_date = as.Date(c(\"2000-06-03\", \"2000-01-01\", \"2015-01-15\", \"2000-12-09\")), cohort_end_date = as.Date(c(\"2001-09-01\", \"2001-01-12\", \"2015-02-15\", \"2002-12-09\")), date_1 = as.Date(c(\"2001-08-01\", \"2001-01-01\", \"2015-01-15\", \"2002-12-09\")), date_2 = as.Date(c(\"2001-08-01\", NA, \"2015-04-15\", \"2002-12-09\")) ) )) #> Warning: ! 7 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `gender_concept_id` is numeric but expected integer #> • `year_of_birth` is numeric but expected integer #> • `month_of_birth` is numeric but expected integer #> • `race_concept_id` is numeric but expected integer #> • `ethnicity_concept_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 2 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> • `period_type_concept_id` is numeric but expected integer #> Warning: ! 9 column in cdm_source do not match expected column type: #> • `cdm_source_abbreviation` is logical but expected character #> • `cdm_holder` is logical but expected character #> • `source_description` is logical but expected character #> • `source_documentation_reference` is logical but expected character #> • `cdm_etl_reference` is logical but expected character #> • `source_release_date` is logical but expected date #> • `cdm_release_date` is logical but expected date #> • `cdm_version` is numeric but expected character #> • `vocabulary_version` is logical but expected character #> Warning: ! 3 column in concept do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 1 column in vocabulary do not match expected column type: #> • `vocabulary_concept_id` is numeric but expected integer #> Warning: ! 5 column in concept_relationship do not match expected column type: #> • `concept_id_1` is numeric but expected integer #> • `concept_id_2` is numeric but expected integer #> • `valid_start_date` is logical but expected date #> • `valid_end_date` is logical but expected date #> • `invalid_reason` is logical but expected character #> Warning: ! 2 column in concept_synonym do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `language_concept_id` is numeric but expected integer #> Warning: ! 4 column in concept_ancestor do not match expected column type: #> • `ancestor_concept_id` is numeric but expected integer #> • `descendant_concept_id` is numeric but expected integer #> • `min_levels_of_separation` is numeric but expected integer #> • `max_levels_of_separation` is numeric but expected integer #> Warning: ! 9 column in drug_strength do not match expected column type: #> • `drug_concept_id` is numeric but expected integer #> • `ingredient_concept_id` is numeric but expected integer #> • `amount_unit_concept_id` is numeric but expected integer #> • `numerator_unit_concept_id` is numeric but expected integer #> • `denominator_value` is logical but expected numeric #> • `denominator_unit_concept_id` is numeric but expected integer #> • `box_size` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> Warning: ! 2 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 1 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer cdm$cohort |> exitAtFirstDate(dateColumns = c(\"date_1\", \"date_2\")) #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> # Source: table [4 x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date exit_reason #> #> 1 1 4 2000-12-09 2002-12-09 date_1; dat… #> 2 1 1 2000-06-03 2001-08-01 date_1; dat… #> 3 1 2 2000-01-01 2001-01-01 date_1 #> 4 1 3 2015-01-15 2015-01-15 date_1 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort end date to the last of a set of column dates — exitAtLastDate","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"exitAtLastDate() resets cohort end date based set specified column dates. last date occurs chosen.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"","code":"exitAtLastDate( cohort, dateColumns, cohortId = NULL, returnReason = TRUE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"cohort cohort table cdm reference. dateColumns Character vector indicating date columns cohort table consider. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. returnReason TRUE return column indicating dateColumns used. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(tables = list( \"cohort\" = dplyr::tibble( cohort_definition_id = 1, subject_id = c(1, 2, 3, 4), cohort_start_date = as.Date(c(\"2000-06-03\", \"2000-01-01\", \"2015-01-15\", \"2000-12-09\")), cohort_end_date = as.Date(c(\"2001-09-01\", \"2001-01-12\", \"2015-02-15\", \"2002-12-09\")), date_1 = as.Date(c(\"2001-08-01\", \"2001-01-01\", \"2015-01-15\", \"2002-12-09\")), date_2 = as.Date(c(\"2001-08-01\", NA, \"2015-04-15\", \"2002-12-09\")) ) )) #> Warning: ! 7 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `gender_concept_id` is numeric but expected integer #> • `year_of_birth` is numeric but expected integer #> • `month_of_birth` is numeric but expected integer #> • `race_concept_id` is numeric but expected integer #> • `ethnicity_concept_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 2 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> • `period_type_concept_id` is numeric but expected integer #> Warning: ! 9 column in cdm_source do not match expected column type: #> • `cdm_source_abbreviation` is logical but expected character #> • `cdm_holder` is logical but expected character #> • `source_description` is logical but expected character #> • `source_documentation_reference` is logical but expected character #> • `cdm_etl_reference` is logical but expected character #> • `source_release_date` is logical but expected date #> • `cdm_release_date` is logical but expected date #> • `cdm_version` is numeric but expected character #> • `vocabulary_version` is logical but expected character #> Warning: ! 3 column in concept do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 1 column in vocabulary do not match expected column type: #> • `vocabulary_concept_id` is numeric but expected integer #> Warning: ! 5 column in concept_relationship do not match expected column type: #> • `concept_id_1` is numeric but expected integer #> • `concept_id_2` is numeric but expected integer #> • `valid_start_date` is logical but expected date #> • `valid_end_date` is logical but expected date #> • `invalid_reason` is logical but expected character #> Warning: ! 2 column in concept_synonym do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `language_concept_id` is numeric but expected integer #> Warning: ! 4 column in concept_ancestor do not match expected column type: #> • `ancestor_concept_id` is numeric but expected integer #> • `descendant_concept_id` is numeric but expected integer #> • `min_levels_of_separation` is numeric but expected integer #> • `max_levels_of_separation` is numeric but expected integer #> Warning: ! 9 column in drug_strength do not match expected column type: #> • `drug_concept_id` is numeric but expected integer #> • `ingredient_concept_id` is numeric but expected integer #> • `amount_unit_concept_id` is numeric but expected integer #> • `numerator_unit_concept_id` is numeric but expected integer #> • `denominator_value` is logical but expected numeric #> • `denominator_unit_concept_id` is numeric but expected integer #> • `box_size` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> Warning: ! 2 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 1 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer cdm$cohort |> exitAtLastDate(dateColumns = c(\"date_1\", \"date_2\")) #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> # Source: table [4 x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date exit_reason #> #> 1 1 4 2000-12-09 2002-12-09 date_2; dat… #> 2 1 2 2000-01-01 2001-01-01 date_1 #> 3 1 3 2015-01-15 2015-04-15 date_2 #> 4 1 1 2000-06-03 2001-08-01 date_2; dat… # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort end date to end of observation — exitAtObservationEnd","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"exitAtObservationEnd() resets cohort end date based set specified column dates. last date occurs chosen. functions changes cohort end date end date observation period corresponding cohort entry. case generates overlapping records cohort, overlapping entries merged.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"","code":"exitAtObservationEnd(cohort, cohortId = NULL, name = tableName(cohort))"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor() cdm$cohort1 |> exitAtObservationEnd() #> # Source: table [6 x 4] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> #> 1 1 9 2012-01-18 2012-06-30 #> 2 1 6 2003-10-31 2005-11-04 #> 3 1 2 1964-09-18 1968-04-03 #> 4 1 4 1998-06-22 2013-05-12 #> 5 1 5 2007-10-19 2014-09-25 #> 6 1 3 1976-11-28 2000-04-25 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/gapDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of gap. — gapDoc","title":"Helper for consistent documentation of gap. — gapDoc","text":"Helper consistent documentation gap.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/gapDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of gap. — gapDoc","text":"gap Number days two subsequent cohort entries merged single cohort record.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"intersectCohorts() combines different cohort entries, records overlap combined kept. Cohort entries individual cohorts.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"","code":"intersectCohorts( cohort, cohortId = NULL, gap = 0, returnNonOverlappingCohorts = FALSE, keepOriginalCohorts = FALSE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts include (cohort_definition_id cohort_name). Cohorts included removed cohort set. gap Number days two subsequent cohort entries merged single cohort record. returnNonOverlappingCohorts Whether generated cohorts mutually exclusive . keepOriginalCohorts TRUE original cohorts newly created intersection cohort returned. FALSE new cohort returned. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(nPerson = 100) cdm$cohort3 <- intersectCohorts( cohort = cdm$cohort2, name = \"cohort3\", ) settings(cdm$cohort3) #> # A tibble: 1 × 5 #> cohort_definition_id cohort_name gap cohort_1 cohort_2 #> #> 1 1 cohort_1_cohort_2 0 1 1 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a new cohort matched cohort — matchCohorts","title":"Generate a new cohort matched cohort — matchCohorts","text":"matchCohorts() generate new cohort matched individuals existing cohort. Individuals can matched based year birth sex.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a new cohort matched cohort — matchCohorts","text":"","code":"matchCohorts( cohort, cohortId = NULL, matchSex = TRUE, matchYearOfBirth = TRUE, ratio = 1, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a new cohort matched cohort — matchCohorts","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts include (cohort_definition_id cohort_name). Cohorts included removed cohort set. matchSex Whether match sex. matchYearOfBirth Whether match year birth. ratio Number allowed matches per individual target cohort. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a new cohort matched cohort — matchCohorts","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a new cohort matched cohort — matchCohorts","text":"","code":"# \\donttest{ library(CohortConstructor) library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union cdm <- mockCohortConstructor(nPerson = 200) cdm$new_matched_cohort <- cdm$cohort2 |> matchCohorts( name = \"new_matched_cohort\", cohortId = 2, matchSex = TRUE, matchYearOfBirth = TRUE, ratio = 1) #> Starting matching #> Warning: Multiple records per person detected. The matchCohorts() function is designed #> to operate under the assumption that there is only one record per person within #> each cohort. If this assumption is not met, each record will be treated #> independently. As a result, the same individual may be matched multiple times, #> leading to inconsistent and potentially misleading results. #> ℹ Creating copy of target cohort. #> • 1 cohort to be matched. #> ℹ Creating controls cohorts. #> ℹ Excluding cases from controls #> • Matching by gender_concept_id and year_of_birth #> • Removing controls that were not in observation at index date #> • Excluding target records whose pair is not in observation #> • Adjusting ratio #> Binding both cohorts #> ✔ Done cdm$new_matched_cohort #> # Source: table [?? x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date cluster_id #>
The flow chart above illustrates changes to cohort 1 (acetaminophen +
The flow chart above illustrates changes to cohort 1 (acetaminophen users) when restricted to only the first record for each individual. While the number of individuals remains unchanged, 6,785 records are excluded.
The flow chart above illustrates the changes to cohort 1 when +
The flow chart above illustrates the changes to cohort 1 when restricted to only the first five records for each individual. While the number of individuals remains unchanged, 6,785 records are excluded.
The flow chart above illustrates changes to cohort 1 when restricted +
The flow chart above illustrates changes to cohort 1 when restricted to only the last record for each individual. While the number of individuals remains unchanged, 6,785 records are excluded.
The flow chart above illustrates the changes to cohort 1 when restricted to a specified date range. 1,948 individuals and 8,660 records are excluded.
Cohort 1 includes 2,580 individuals, so none were excluded due to the +
Cohort 1 includes 2,580 individuals, so none were excluded due to the minimum cohort size restriction of 1,000.
The flow chart above illustrates the changes to cohort 1 when restricted to only include the first record of each individual over a specified date range. 2,529 individuals and 9,314 records are excluded.
The flow chart above illustrates the changes to cohort 1 (users of +
The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when restricted to only include individuals aged 18 to 90. 226 individuals and 2,863 records were excluded.
The variable ‘cohort_start_date’ is used so that individuals are @@ -144,7 +144,7 @@
The flow chart above illustrates the changes to cohort 1 when restricted to only include ‘female’ individuals. 1,264 individuals and 4,647 records were excluded.
The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals with at least 365 days of prior observations. 5 individuals and 109 records were excluded.
The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals with at least 365 days of future observations. 14 individuals and 206 records were excluded.
The flow chart above illustrates the changes to cohort 1 when multiple demographic restrictions, so that only female individuals between 18 and 100 years old, with at least 365 days of prior and future observations are included. 1,413 individuals and 6156 records were diff --git a/articles/a04_require_intersections.html b/articles/a04_require_intersections.html index 75278372..d9c39827 100644 --- a/articles/a04_require_intersections.html +++ b/articles/a04_require_intersections.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -129,7 +129,7 @@ Restrictions on cohort presencesummary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 (users of +The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when restricted to only include individuals who intersect with the GI bleed cohort at least once before the cohort start date. 2,296 individuals and 8,765 records were excluded. @@ -151,7 +151,7 @@ Restrictions on cohort presencesummary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals with no intersects with the GI bleed cohort before the cohort start date. 36 individuals and 600 records were excluded. @@ -174,7 +174,7 @@ Restrictions on concept presencesummary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who have had events of GI bleeding at least once before the cohort start date. 2,296 individuals and 8,765 records were excluded. @@ -194,7 +194,7 @@ Restrictions on concept presencesummary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who have not had events of GI bleeding before the cohort start date. 36 individuals and 600 records were excluded. @@ -217,7 +217,7 @@ Restrictions on presence in summary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who intersect with the GI bleeding clinical table at least once before the cohort start date. 2,296 individuals and 8,765 records were excluded. @@ -238,7 +238,7 @@ Restrictions on presence in summary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who have no intersects with the GI bleeding clinical table before the cohort start date. 36 individuals and 600 records were excluded. @@ -258,7 +258,7 @@ Restrictions on deathssummary_attrition <- summariseCohortAttrition(cdm$medications_deaths) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who died after the cohort start date. None of the individuals in cohort 1 died and therefore they are all excluded from this cohort. @@ -273,7 +273,7 @@ Restrictions on deathssummary_attrition <- summariseCohortAttrition(cdm$medications_no_deaths) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 when +The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who did not die after the cohort start date. None of the individuals in cohort 1 died and therefore no one was excluded. diff --git a/articles/a05_update_cohort_start_end.html b/articles/a05_update_cohort_start_end.html index 47d92b50..51696503 100644 --- a/articles/a05_update_cohort_start_end.html +++ b/articles/a05_update_cohort_start_end.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a06_concatanate_cohorts.html b/articles/a06_concatanate_cohorts.html index 0f6a6b3d..2e415a49 100644 --- a/articles/a06_concatanate_cohorts.html +++ b/articles/a06_concatanate_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -121,17 +121,17 @@ cdm$medications %>% filter(subject_id == 1) #> # Source: SQL [4 x 4] -#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmpQT59lg/file1dcd4337393f.duckdb] +#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmplazfDY/file1e0b2d32d912.duckdb] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> -#> 1 1 1 1976-10-20 1976-11-03 +#> 1 1 1 1980-03-15 1980-03-29 #> 2 1 1 1971-01-04 1971-01-18 #> 3 1 1 1982-09-11 1982-10-02 -#> 4 1 1 1980-03-15 1980-03-29 +#> 4 1 1 1976-10-20 1976-11-03 cdm$medications_collapsed %>% filter(subject_id == 1) #> # Source: SQL [3 x 4] -#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmpQT59lg/file1dcd4337393f.duckdb] +#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmplazfDY/file1e0b2d32d912.duckdb] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> #> 1 1 1 1976-10-20 1976-11-03 @@ -147,14 +147,14 @@ summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 (users of +The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when entries within 3 years of each other are merged. We see that collapsing the cohort has led to 1,390 fewer records. summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed) plotCohortAttrition(summary_attrition, cohortId = 2) -The flow chart above illustrates the changes to cohort 2 (users of +The flow chart above illustrates the changes to cohort 2 (users of diclofenac) when entries within 3 years of each other are merged. Since this cohort only has one record per individual the function collapseCohorts() had no impact on the final number of records. diff --git a/articles/a07_filter_cohorts.html b/articles/a07_filter_cohorts.html index fab5d0c3..d5abc93f 100644 --- a/articles/a07_filter_cohorts.html +++ b/articles/a07_filter_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -109,7 +109,7 @@ #> # A tibble: 2 × 3 #> cohort_definition_id number_records number_subjects #> <int> <int> <int> -#> 1 1 366 100 +#> 1 1 350 100 #> 2 2 830 830 diff --git a/articles/a08_split_cohorts.html b/articles/a08_split_cohorts.html index b55599d3..328b8abd 100644 --- a/articles/a08_split_cohorts.html +++ b/articles/a08_split_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a09_combine_cohorts.html b/articles/a09_combine_cohorts.html index e3e52d8d..755bd599 100644 --- a/articles/a09_combine_cohorts.html +++ b/articles/a09_combine_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a10_match_cohorts.html b/articles/a10_match_cohorts.html index e486ba69..c5d767c3 100644 --- a/articles/a10_match_cohorts.html +++ b/articles/a10_match_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/articles/a11_benchmark.html b/articles/a11_benchmark.html index 61fd9e7a..211be7f4 100644 --- a/articles/a11_benchmark.html +++ b/articles/a11_benchmark.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -586,16 +586,18 @@ Code and collaboration -OMOP table - - Database +OMOP table + + Database -CPRD Aurum - CORIVA-Estonia - CPRD Gold 100k - OHDSI SQL server +CPRD Aurum + CORIVA-Estonia + CPRD Gold 100k + OHDSI Postgres server + OHDSI SQL server @@ -608,6 +610,8 @@ Code and collaboration100,000 1,000 +1,000 @@ -619,6 +623,8 @@ Code and collaboration100,000 1,048 +1,048 @@ -630,6 +636,8 @@ Code and collaboration12,403,195 49,542 +49,542 @@ -641,6 +649,8 @@ Code and collaboration3,191,739 160,322 +160,322 @@ -652,6 +662,8 @@ Code and collaboration1,914,271 62,189 +62,189 @@ -663,6 +675,8 @@ Code and collaboration9,183,206 47,457 +47,457 @@ -674,6 +688,8 @@ Code and collaboration10,913,588 2,858 +2,858 @@ -685,6 +701,8 @@ Code and collaboration11,107,039 13,481 +13,481 @@ -1163,25 +1181,29 @@ Cohort counts and overlap - - - Tool + + + Tool -Cohort name - - CIRCE +Cohort name + + CIRCE - - CohortConstructor + + CohortConstructor -Number records - Number subjects - Number records - Number subjects +Number records + Number subjects + Number records + Number subjects @@ -1192,136 +1214,136 @@ Cohort counts and overlapAcquired neutropenia or unspecified leukopenia 1429966 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,429,966 632966 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">632,966 1302498 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,302,498 633030 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">633,030 Asthma without COPD 4009925 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,009,925 4009925 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,009,925 3934106 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,934,106 3934106 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">3,934,106 COVID-19 5600429 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,600,429 4452410 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,452,410 6206907 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">6,206,907 4452196 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">4,452,196 COVID-19: female 3111643 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,111,643 2434062 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,434,062 3452138 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,452,138 2438759 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">2,438,759 COVID-19: female, 0 to 50 2172113 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,172,113 1730180 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,730,180 2382039 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,382,039 1730116 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,730,116 COVID-19: female, 51 to 150 939818 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">939,818 708838 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">708,838 1070099 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,070,099 708643 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">708,643 COVID-19: male 2488786 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,488,786 2018348 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,018,348 2754769 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,754,769 2020625 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">2,020,625 COVID-19: male, 0 to 50 1709375 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,709,375 1422999 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,422,999 1862219 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,862,219 1422962 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,422,962 COVID-19: male, 51 to 150 779629 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">779,629 597804 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">597,804 892550 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">892,550 597663 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">597,663 Endometriosis procedure @@ -1357,61 +1379,61 @@ Cohort counts and overlapMajor non cardiac surgery 1932745 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,932,745 1932745 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,932,745 1932745 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,932,745 1932745 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,932,745 New fluoroquinolone users 1765274 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,765,274 1765274 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,765,274 1817439 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,817,439 1817439 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,817,439 New users of beta blockers nested in essential hypertension 98592 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">98,592 98592 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">98,592 102589 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">102,589 102589 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">102,589 Transverse myelitis 11930 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">11,930 4040 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,040 5818 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,818 4119 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">4,119 CORIVA-Estonia @@ -1420,13 +1442,13 @@ Cohort counts and overlapAcquired neutropenia or unspecified leukopenia 2231 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,231 634 2188 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,188 634 @@ -1435,121 +1457,121 @@ Cohort counts and overlapAsthma without COPD 25867 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">25,867 25867 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">25,867 25867 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">25,867 25867 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">25,867 COVID-19 421053 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">421,053 193435 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">193,435 435059 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">435,059 193435 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">193,435 COVID-19: female 235740 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">235,740 105849 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">105,849 243773 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">243,773 106322 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">106,322 COVID-19: female, 0 to 50 150121 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">150,121 69168 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">69,168 155256 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">155,256 69168 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">69,168 COVID-19: female, 51 to 150 85620 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">85,620 37154 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">37,154 88517 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">88,517 37154 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">37,154 COVID-19: male 185313 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">185,313 87586 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">87,586 191286 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">191,286 87891 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">87,891 COVID-19: male, 0 to 50 130252 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">130,252 63558 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">63,558 134415 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">134,415 63558 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">63,558 COVID-19: male, 51 to 150 55062 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">55,062 24333 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">24,333 56871 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">56,871 24333 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">24,333 Endometriosis procedure @@ -1570,61 +1592,61 @@ Cohort counts and overlapInpatient hospitalisation 267010 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">267,010 133705 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">133,705 267010 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">267,010 133705 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">133,705 Major non cardiac surgery 4025 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,025 4025 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,025 4025 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,025 4025 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">4,025 New fluoroquinolone users 39712 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">39,712 39712 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">39,712 39712 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">39,712 39712 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">39,712 New users of beta blockers nested in essential hypertension 18967 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">18,967 18967 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">18,967 18967 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">18,967 18967 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">18,967 Transverse myelitis @@ -1648,76 +1670,76 @@ Cohort counts and overlapAcquired neutropenia or unspecified leukopenia 2719 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,719 1167 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,167 2675 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,675 1167 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,167 Asthma without COPD 8808 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">8,808 8808 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">8,808 8741 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">8,741 8741 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">8,741 COVID-19 3231 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,231 2881 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">2,881 3275 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">3,275 2881 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">2,881 COVID-19: female 1748 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,748 1543 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,543 1771 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,771 1543 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,543 COVID-19: female, 0 to 50 1271 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,271 1125 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,125 1291 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,291 1125 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,125 COVID-19: female, 51 to 150 @@ -1738,28 +1760,28 @@ Cohort counts and overlapCOVID-19: male 1483 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,483 1338 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,338 1504 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,504 1341 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,341 COVID-19: male, 0 to 50 1054 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,054 960 1072 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,072 960 @@ -1813,46 +1835,46 @@ Cohort counts and overlapMajor non cardiac surgery 4146 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,146 4146 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,146 4146 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">4,146 4146 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">4,146 New fluoroquinolone users 5412 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,412 5412 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,412 5412 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">5,412 5412 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">5,412 New users of beta blockers nested in essential hypertension 1723 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,723 1723 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,723 1723 +[header_level]Number records" class="gt_row gt_right" style="text-align: right; border-left-width: 1px; border-left-style: solid; border-left-color: #D3D3D3; border-right-width: 1px; border-right-style: solid; border-right-color: #D3D3D3; border-top-width: 1px; border-top-style: solid; border-top-color: #D3D3D3; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #D3D3D3;">1,723 1723 +[header_level]Number subjects" class="gt_row gt_right" style="text-align: right;">1,723 Transverse myelitis @@ -1870,6 +1892,234 @@ Cohort counts and overlap11 +OHDSI Postgres server + + +Acquired neutropenia or unspecified leukopenia +151 +86 +106 +86 + + +Asthma without COPD +126 +126 +126 +126 + + +COVID-19 +0 +0 +0 +0 + + +COVID-19: female +0 +0 +0 +0 + + +COVID-19: female, 0 to 50 +0 +0 +0 +0 + + +COVID-19: female, 51 to 150 +0 +0 +0 +0 + + +COVID-19: male +0 +0 +0 +0 + + +COVID-19: male, 0 to 50 +0 +0 +0 +0 + + +COVID-19: male, 51 to 150 +0 +0 +0 +0 + + +Endometriosis procedure +0 +0 +0 +0 + + +Inpatient hospitalisation +522 +321 +522 +321 + + +Major non cardiac surgery +88 +88 +92 +92 + + +New fluoroquinolone users +145 +145 +145 +145 + + +New users of beta blockers nested in essential hypertension +112 +112 +112 +112 + + +Transverse myelitis +0 +0 +0 +0 + + OHDSI SQL server @@ -2134,23 +2384,23 @@ By domain - @@ -2587,13 +2837,14 @@ By domain Database_name - - Time (minutes) + + Time (minutes) -CIRCE - CohortConstructor +CIRCE + CohortConstructor @@ -2619,6 +2870,13 @@ By domain7.85 +OHDSI Postgres server +4.32 +29.20 + + OHDSI SQL server 2.89 @@ -2638,23 +2896,23 @@ Cohort stratification - - @@ -3091,13 +3349,14 @@ Cohort stratification Database - - Time (minutes) + + Time (minutes) -CIRCE - CohortConstructor +CIRCE + CohortConstructor @@ -3123,6 +3382,13 @@ Cohort stratification19.52 +OHDSI Postgres server +6.75 +73.24 + + OHDSI SQL server 4.56 diff --git a/articles/a11_benchmark_files/figure-html/unnamed-chunk-10-1.png b/articles/a11_benchmark_files/figure-html/unnamed-chunk-10-1.png index 077c57cd..a22054df 100644 Binary files a/articles/a11_benchmark_files/figure-html/unnamed-chunk-10-1.png and b/articles/a11_benchmark_files/figure-html/unnamed-chunk-10-1.png differ diff --git a/articles/a11_benchmark_files/figure-html/unnamed-chunk-5-1.png b/articles/a11_benchmark_files/figure-html/unnamed-chunk-5-1.png index 4ee21baa..49f847ee 100644 Binary files a/articles/a11_benchmark_files/figure-html/unnamed-chunk-5-1.png and b/articles/a11_benchmark_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/articles/a11_benchmark_files/figure-html/unnamed-chunk-7-1.png b/articles/a11_benchmark_files/figure-html/unnamed-chunk-7-1.png index 4effca28..aedb495d 100644 Binary files a/articles/a11_benchmark_files/figure-html/unnamed-chunk-7-1.png and b/articles/a11_benchmark_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/articles/index.html b/articles/index.html index 52cd76cf..63245934 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/authors.html b/authors.html index c5042185..0b771723 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -90,13 +90,13 @@ Citation Burn E, Catala M, Mercade-Besora N, Alcalde-Herraiz M, Du M, Guo Y, Chen X, Lopez-Guell K, Rowlands E (2024). CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model. -R package version 0.3.0.900, https://ohdsi.github.io/CohortConstructor/. +R package version 0.3.1, https://ohdsi.github.io/CohortConstructor/. @Manual{, title = {CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model}, author = {Edward Burn and Marti Catala and Nuria Mercade-Besora and Marta Alcalde-Herraiz and Mike Du and Yuchen Guo and Xihang Chen and Kim Lopez-Guell and Elin Rowlands}, year = {2024}, - note = {R package version 0.3.0.900}, + note = {R package version 0.3.1}, url = {https://ohdsi.github.io/CohortConstructor/}, } diff --git a/index.html b/index.html index f3aa2541..9385ede9 100644 --- a/index.html +++ b/index.html @@ -29,7 +29,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/pkgdown.yml b/pkgdown.yml index 22df4163..51332e54 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -14,7 +14,7 @@ articles: a09_combine_cohorts: a09_combine_cohorts.html a10_match_cohorts: a10_match_cohorts.html a11_benchmark: a11_benchmark.html -last_built: 2024-10-01T17:04Z +last_built: 2024-10-08T08:55Z urls: reference: https://ohdsi.github.io/CohortConstructor/reference article: https://ohdsi.github.io/CohortConstructor/articles diff --git a/reference/CohortConstructor-package.html b/reference/CohortConstructor-package.html index 9849f0f6..0fc0c101 100644 --- a/reference/CohortConstructor-package.html +++ b/reference/CohortConstructor-package.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -67,7 +67,7 @@ Author< Mike Du mike.du@ndorms.ox.ac.uk (ORCID) Yuchen Guo yuchen.guo@ndorms.ox.ac.uk (ORCID) Xihang Chen xihang.chen@ndorms.ox.ac.uk (ORCID) -Kim Lopez kim.lopez@spc.ox.ac.uk (ORCID) +Kim Lopez-Guell kim.lopez@spc.ox.ac.uk (ORCID) Elin Rowlands elin.rowlands@ndorms.ox.ac.uk (ORCID) diff --git a/reference/benchmarkData.html b/reference/benchmarkData.html index 45a91354..097fc193 100644 --- a/reference/benchmarkData.html +++ b/reference/benchmarkData.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/cdmDoc.html b/reference/cdmDoc.html index 6423cb33..2134bf9b 100644 --- a/reference/cdmDoc.html +++ b/reference/cdmDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/cohortDoc.html b/reference/cohortDoc.html index aec5f84e..c8ab3cff 100644 --- a/reference/cohortDoc.html +++ b/reference/cohortDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/cohortIdModifyDoc.html b/reference/cohortIdModifyDoc.html index f679f471..1ac353a4 100644 --- a/reference/cohortIdModifyDoc.html +++ b/reference/cohortIdModifyDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/cohortIdSubsetDoc.html b/reference/cohortIdSubsetDoc.html index 560175c8..9327a0fd 100644 --- a/reference/cohortIdSubsetDoc.html +++ b/reference/cohortIdSubsetDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/collapseCohorts.html b/reference/collapseCohorts.html index 1f3c2772..1e48519e 100644 --- a/reference/collapseCohorts.html +++ b/reference/collapseCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/columnDateDoc.html b/reference/columnDateDoc.html index 173af6f0..331c1e39 100644 --- a/reference/columnDateDoc.html +++ b/reference/columnDateDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/conceptCohort.html b/reference/conceptCohort.html index 3ac1ad39..7cc2b200 100644 --- a/reference/conceptCohort.html +++ b/reference/conceptCohort.html @@ -53,7 +53,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/conceptSetDoc.html b/reference/conceptSetDoc.html index 4df9c20e..3f205c7a 100644 --- a/reference/conceptSetDoc.html +++ b/reference/conceptSetDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/demographicsCohort.html b/reference/demographicsCohort.html index d8738441..4a27b6c8 100644 --- a/reference/demographicsCohort.html +++ b/reference/demographicsCohort.html @@ -13,7 +13,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/entryAtFirstDate.html b/reference/entryAtFirstDate.html index 37934adf..5c512b7c 100644 --- a/reference/entryAtFirstDate.html +++ b/reference/entryAtFirstDate.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -181,10 +181,10 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date entry_reason #> <dbl> <dbl> <date> <date> <chr> -#> 1 1 4 2002-12-09 2002-12-09 date_1; dat… -#> 2 1 1 2001-08-01 2001-09-01 date_1; dat… -#> 3 1 2 2001-01-01 2001-01-12 date_1 -#> 4 1 3 2015-02-14 2015-02-15 date_2 +#> 1 1 1 2001-08-01 2001-09-01 date_1; dat… +#> 2 1 4 2002-12-09 2002-12-09 date_1; dat… +#> 3 1 3 2015-02-14 2015-02-15 date_2 +#> 4 1 2 2001-01-01 2001-01-12 date_1 # } diff --git a/reference/entryAtLastDate.html b/reference/entryAtLastDate.html index 1e6882e3..a64422ff 100644 --- a/reference/entryAtLastDate.html +++ b/reference/entryAtLastDate.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -181,10 +181,10 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date entry_reason #> <dbl> <dbl> <date> <date> <chr> -#> 1 1 2 2001-01-01 2001-01-12 date_1 +#> 1 1 1 2001-08-01 2001-09-01 date_2; dat… #> 2 1 3 2015-02-14 2015-02-15 date_2 -#> 3 1 1 2001-08-01 2001-09-01 date_1; dat… -#> 4 1 4 2002-12-09 2002-12-09 date_1; dat… +#> 3 1 2 2001-01-01 2001-01-12 date_1 +#> 4 1 4 2002-12-09 2002-12-09 date_2; dat… # } diff --git a/reference/exitAtDeath.html b/reference/exitAtDeath.html index ea102781..13ac96cc 100644 --- a/reference/exitAtDeath.html +++ b/reference/exitAtDeath.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -105,16 +105,16 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> -#> 1 1 1 1943-11-26 1949-06-21 -#> 2 2 4 1949-03-07 1951-05-08 -#> 3 1 10 1948-08-08 1952-03-27 -#> 4 3 6 1966-09-27 1975-03-05 +#> 1 3 2 1918-06-14 1921-11-28 +#> 2 1 9 1940-05-01 1945-06-29 +#> 3 3 8 1921-10-07 1931-05-01 +#> 4 3 7 1962-05-14 1964-03-08 #> 5 2 5 1966-05-05 1970-11-15 #> 6 2 3 1939-07-24 1944-01-14 -#> 7 3 8 1921-10-07 1931-05-01 -#> 8 1 9 1940-05-01 1945-06-29 -#> 9 3 2 1918-06-14 1921-11-28 -#> 10 3 7 1962-05-14 1964-03-08 +#> 7 1 10 1948-08-08 1952-03-27 +#> 8 2 4 1949-03-07 1951-05-08 +#> 9 3 6 1966-09-27 1975-03-05 +#> 10 1 1 1943-11-26 1949-06-21 # } diff --git a/reference/exitAtFirstDate.html b/reference/exitAtFirstDate.html index e27c4b27..a55a5a53 100644 --- a/reference/exitAtFirstDate.html +++ b/reference/exitAtFirstDate.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -181,10 +181,10 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date exit_reason #> <dbl> <dbl> <date> <date> <chr> -#> 1 1 4 2000-12-09 2002-12-09 date_1; dat… -#> 2 1 1 2000-06-03 2001-08-01 date_1; dat… +#> 1 1 1 2000-06-03 2001-08-01 date_2; dat… +#> 2 1 3 2015-01-15 2015-01-15 date_1 #> 3 1 2 2000-01-01 2001-01-01 date_1 -#> 4 1 3 2015-01-15 2015-01-15 date_1 +#> 4 1 4 2000-12-09 2002-12-09 date_2; dat… # } diff --git a/reference/exitAtLastDate.html b/reference/exitAtLastDate.html index 9d33087c..584c19bb 100644 --- a/reference/exitAtLastDate.html +++ b/reference/exitAtLastDate.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -181,10 +181,10 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date exit_reason #> <dbl> <dbl> <date> <date> <chr> -#> 1 1 4 2000-12-09 2002-12-09 date_2; dat… -#> 2 1 2 2000-01-01 2001-01-01 date_1 -#> 3 1 3 2015-01-15 2015-04-15 date_2 -#> 4 1 1 2000-06-03 2001-08-01 date_2; dat… +#> 1 1 3 2015-01-15 2015-04-15 date_2 +#> 2 1 4 2000-12-09 2002-12-09 date_1; dat… +#> 3 1 2 2000-01-01 2001-01-01 date_1 +#> 4 1 1 2000-06-03 2001-08-01 date_1; dat… # } diff --git a/reference/exitAtObservationEnd.html b/reference/exitAtObservationEnd.html index 13e6e420..0885f9d1 100644 --- a/reference/exitAtObservationEnd.html +++ b/reference/exitAtObservationEnd.html @@ -15,7 +15,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -103,10 +103,10 @@ Examples#> <int> <int> <date> <date> #> 1 1 9 2012-01-18 2012-06-30 #> 2 1 6 2003-10-31 2005-11-04 -#> 3 1 2 1964-09-18 1968-04-03 -#> 4 1 4 1998-06-22 2013-05-12 -#> 5 1 5 2007-10-19 2014-09-25 -#> 6 1 3 1976-11-28 2000-04-25 +#> 3 1 5 2007-10-19 2014-09-25 +#> 4 1 3 1976-11-28 2000-04-25 +#> 5 1 4 1998-06-22 2013-05-12 +#> 6 1 2 1964-09-18 1968-04-03 # } diff --git a/reference/gapDoc.html b/reference/gapDoc.html index 532b87c1..cbfd865b 100644 --- a/reference/gapDoc.html +++ b/reference/gapDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/index.html b/reference/index.html index 75dd1022..557ab837 100644 --- a/reference/index.html +++ b/reference/index.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/intersectCohorts.html b/reference/intersectCohorts.html index 2c04a75d..602c177d 100644 --- a/reference/intersectCohorts.html +++ b/reference/intersectCohorts.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/matchCohorts.html b/reference/matchCohorts.html index 3e62f0c8..746b16bb 100644 --- a/reference/matchCohorts.html +++ b/reference/matchCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -141,16 +141,16 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date cluster_id #> <int> <int> <date> <date> <dbl> -#> 1 1 33 1993-05-10 1997-04-01 14 -#> 2 1 103 1982-05-27 1987-10-22 21 -#> 3 1 67 2009-01-30 2010-03-12 78 -#> 4 1 110 2005-10-01 2006-06-12 100 -#> 5 1 19 2015-04-24 2015-09-01 108 -#> 6 1 89 2002-04-17 2005-07-29 54 -#> 7 1 150 2008-01-10 2008-11-21 113 -#> 8 1 62 2008-04-08 2008-07-26 44 -#> 9 1 16 2004-09-11 2006-10-01 101 -#> 10 1 16 2007-05-18 2007-10-08 102 +#> 1 1 89 2005-07-30 2007-06-17 54 +#> 2 1 150 2008-01-10 2008-11-21 113 +#> 3 1 30 2008-04-20 2010-01-04 27 +#> 4 1 47 1994-03-23 1997-08-27 89 +#> 5 1 62 2008-04-08 2008-07-26 44 +#> 6 1 16 2004-09-11 2006-10-01 102 +#> 7 1 110 2005-06-30 2005-09-30 99 +#> 8 1 19 2015-04-24 2015-09-01 108 +#> 9 1 33 1993-05-10 1997-04-01 14 +#> 10 1 80 2000-12-22 2002-04-17 18 #> # ℹ more rows # } diff --git a/reference/measurementCohort.html b/reference/measurementCohort.html index 397e145b..12fa2148 100644 --- a/reference/measurementCohort.html +++ b/reference/measurementCohort.html @@ -31,7 +31,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/mockCohortConstructor.html b/reference/mockCohortConstructor.html index 829e48d4..e3dcfb97 100644 --- a/reference/mockCohortConstructor.html +++ b/reference/mockCohortConstructor.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/nameDoc.html b/reference/nameDoc.html index 0505c849..03e2134d 100644 --- a/reference/nameDoc.html +++ b/reference/nameDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/padCohortStart.html b/reference/padCohortStart.html index 17784fee..3a86b5c4 100644 --- a/reference/padCohortStart.html +++ b/reference/padCohortStart.html @@ -21,7 +21,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/reexports.html b/reference/reexports.html index a5cfaced..8383dba6 100644 --- a/reference/reexports.html +++ b/reference/reexports.html @@ -29,7 +29,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireAge.html b/reference/requireAge.html index f78f7691..7c7031e9 100644 --- a/reference/requireAge.html +++ b/reference/requireAge.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireCohortIntersect.html b/reference/requireCohortIntersect.html index c2463b97..c052fb88 100644 --- a/reference/requireCohortIntersect.html +++ b/reference/requireCohortIntersect.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireConceptIntersect.html b/reference/requireConceptIntersect.html index a1cd712e..dd451c79 100644 --- a/reference/requireConceptIntersect.html +++ b/reference/requireConceptIntersect.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireDeathFlag.html b/reference/requireDeathFlag.html index 7f4c753c..8015f9bd 100644 --- a/reference/requireDeathFlag.html +++ b/reference/requireDeathFlag.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireDemographics.html b/reference/requireDemographics.html index 161c3de9..23199dda 100644 --- a/reference/requireDemographics.html +++ b/reference/requireDemographics.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireDemographicsDoc.html b/reference/requireDemographicsDoc.html index d656c541..6a898592 100644 --- a/reference/requireDemographicsDoc.html +++ b/reference/requireDemographicsDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireFutureObservation.html b/reference/requireFutureObservation.html index da73506f..08544a5a 100644 --- a/reference/requireFutureObservation.html +++ b/reference/requireFutureObservation.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -111,15 +111,15 @@ Examples#> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> #> 1 1 2 1964-09-18 1965-08-30 -#> 2 1 3 1976-11-28 1977-03-11 +#> 2 1 3 1978-04-04 1987-02-26 #> 3 1 4 1998-06-22 2001-02-12 -#> 4 1 5 2008-11-11 2011-09-12 +#> 4 1 5 2007-10-19 2008-11-10 #> 5 1 6 2003-11-15 2004-04-10 #> 6 1 9 2012-01-18 2012-03-08 -#> 7 1 3 1978-04-04 1987-02-26 -#> 8 1 5 2007-10-19 2008-11-10 +#> 7 1 3 1977-03-12 1978-04-03 +#> 8 1 5 2008-11-11 2011-09-12 #> 9 1 6 2003-10-31 2003-11-14 -#> 10 1 3 1977-03-12 1978-04-03 +#> 10 1 3 1976-11-28 1977-03-11 # } diff --git a/reference/requireInDateRange.html b/reference/requireInDateRange.html index 9724ee92..4b172ef4 100644 --- a/reference/requireInDateRange.html +++ b/reference/requireInDateRange.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireIntersectDoc.html b/reference/requireIntersectDoc.html index e7bff0cd..ec0e1661 100644 --- a/reference/requireIntersectDoc.html +++ b/reference/requireIntersectDoc.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireIsEntry.html b/reference/requireIsEntry.html index eec327c8..570d6be7 100644 --- a/reference/requireIsEntry.html +++ b/reference/requireIsEntry.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireIsFirstEntry.html b/reference/requireIsFirstEntry.html index df884919..92096723 100644 --- a/reference/requireIsFirstEntry.html +++ b/reference/requireIsFirstEntry.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireIsLastEntry.html b/reference/requireIsLastEntry.html index 07b334a0..976d8093 100644 --- a/reference/requireIsLastEntry.html +++ b/reference/requireIsLastEntry.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireMinCohortCount.html b/reference/requireMinCohortCount.html index 0fab0031..b3ee0a99 100644 --- a/reference/requireMinCohortCount.html +++ b/reference/requireMinCohortCount.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requirePriorObservation.html b/reference/requirePriorObservation.html index d6a71240..c2044362 100644 --- a/reference/requirePriorObservation.html +++ b/reference/requirePriorObservation.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireSex.html b/reference/requireSex.html index f7611e9b..376bc429 100644 --- a/reference/requireSex.html +++ b/reference/requireSex.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/requireTableIntersect.html b/reference/requireTableIntersect.html index a8803b02..ba9dad21 100644 --- a/reference/requireTableIntersect.html +++ b/reference/requireTableIntersect.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/sampleCohorts.html b/reference/sampleCohorts.html index 03c06583..1d2a5de9 100644 --- a/reference/sampleCohorts.html +++ b/reference/sampleCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -97,16 +97,16 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> -#> 1 1 13 1991-04-14 1992-10-21 -#> 2 1 13 1992-11-03 2000-11-27 -#> 3 1 14 1998-11-10 1999-03-09 -#> 4 1 14 1999-03-10 2001-06-02 -#> 5 1 14 2001-06-03 2001-12-04 -#> 6 1 25 2018-01-10 2018-03-13 -#> 7 1 25 2018-03-14 2018-03-22 -#> 8 1 25 2018-03-23 2018-06-26 -#> 9 1 25 2018-06-27 2018-11-13 -#> 10 1 39 2000-03-07 2009-03-17 +#> 1 1 9 2011-12-20 2011-12-20 +#> 2 1 9 2011-12-21 2011-12-29 +#> 3 1 9 2011-12-30 2012-04-02 +#> 4 1 17 2009-06-18 2013-02-01 +#> 5 1 21 1981-04-20 1986-09-10 +#> 6 1 23 2000-10-08 2001-06-04 +#> 7 1 23 2001-06-05 2003-08-26 +#> 8 1 34 2005-06-12 2006-03-16 +#> 9 1 34 2006-03-17 2008-07-24 +#> 10 1 41 2000-07-02 2000-11-21 #> # ℹ more rows # } diff --git a/reference/stratifyCohorts.html b/reference/stratifyCohorts.html index 274a18a6..a622b201 100644 --- a/reference/stratifyCohorts.html +++ b/reference/stratifyCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -115,15 +115,15 @@ Examples#> cohort_definition_id subject_id cohort_start_date cohort_end_date age #> <int> <int> <date> <date> <int> #> 1 1 2 1964-09-18 1965-08-30 9 -#> 2 1 3 1978-04-04 1987-02-26 19 +#> 2 1 3 1976-11-28 1977-03-11 17 #> 3 1 4 1998-06-22 2001-02-12 16 -#> 4 1 5 2007-10-19 2008-11-10 44 +#> 4 1 5 2008-11-11 2011-09-12 45 #> 5 2 6 2003-11-15 2004-04-10 35 #> 6 1 9 2012-01-18 2012-03-08 27 -#> 7 1 3 1977-03-12 1978-04-03 17 -#> 8 1 5 2008-11-11 2011-09-12 45 +#> 7 1 3 1978-04-04 1987-02-26 19 +#> 8 1 5 2007-10-19 2008-11-10 44 #> 9 2 6 2003-10-31 2003-11-14 35 -#> 10 1 3 1976-11-28 1977-03-11 17 +#> 10 1 3 1977-03-12 1978-04-03 17 #> # ℹ more rows settings(cdm$my_cohort) diff --git a/reference/subsetCohorts.html b/reference/subsetCohorts.html index 53bc0f2a..f2e8c40d 100644 --- a/reference/subsetCohorts.html +++ b/reference/subsetCohorts.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/trimDemographics.html b/reference/trimDemographics.html index 4cbe0a79..b2b0844a 100644 --- a/reference/trimDemographics.html +++ b/reference/trimDemographics.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -125,16 +125,16 @@ Examples#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> -#> 1 1 38 1979-07-05 1981-02-09 -#> 2 1 43 1990-02-26 1995-03-03 -#> 3 1 51 1982-12-05 1997-10-16 -#> 4 1 53 1998-02-23 1998-12-09 -#> 5 1 74 2007-11-14 2008-11-21 -#> 6 1 78 2001-08-27 2005-04-11 -#> 7 1 38 1978-07-24 1979-07-04 -#> 8 1 51 1979-04-13 1982-12-04 -#> 9 1 53 1997-03-26 1998-02-22 -#> 10 1 74 2007-06-26 2007-11-13 +#> 1 1 4 1998-12-14 2002-02-14 +#> 2 1 9 2011-12-30 2012-04-02 +#> 3 1 21 1985-08-16 1986-09-10 +#> 4 1 26 1984-05-16 1989-03-22 +#> 5 1 35 2007-05-13 2010-07-22 +#> 6 1 39 2000-03-07 2009-03-17 +#> 7 1 41 2000-11-22 2000-12-09 +#> 8 1 69 2003-01-14 2003-05-28 +#> 9 1 71 2009-02-01 2009-09-05 +#> 10 1 83 2007-05-29 2008-05-01 #> # ℹ more rows # } diff --git a/reference/trimToDateRange.html b/reference/trimToDateRange.html index 4fd66f17..660ef3cb 100644 --- a/reference/trimToDateRange.html +++ b/reference/trimToDateRange.html @@ -9,7 +9,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/unionCohorts.html b/reference/unionCohorts.html index 85248807..18ebd77e 100644 --- a/reference/unionCohorts.html +++ b/reference/unionCohorts.html @@ -11,7 +11,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/windowDoc.html b/reference/windowDoc.html index 7a0fc213..8f33c2db 100644 --- a/reference/windowDoc.html +++ b/reference/windowDoc.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/reference/yearCohorts.html b/reference/yearCohorts.html index 5a66225f..b785b804 100644 --- a/reference/yearCohorts.html +++ b/reference/yearCohorts.html @@ -7,7 +7,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 diff --git a/search.json b/search.json index a8a08488..9872166a 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://ohdsi.github.io/CohortConstructor/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 ","code":""},{"path":[]},{"path":"https://ohdsi.github.io/CohortConstructor/LICENSE.html","id":"id_1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. 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Limitation of Liability","title":"Apache License","text":"event legal theory, whether tort (including negligence), contract, otherwise, unless required applicable law (deliberate grossly negligent acts) agreed writing, shall Contributor liable damages, including direct, indirect, special, incidental, consequential damages character arising result License use inability use Work (including limited damages loss goodwill, work stoppage, computer failure malfunction, commercial damages losses), even Contributor advised possibility damages.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/LICENSE.html","id":"id_9-accepting-warranty-or-additional-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"9. Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner] 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."},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a00_introduction.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction","text":"CohortConstructor package designed support cohort building pipelines. using package general workflow first build set base cohorts subsequently apply inclusion criteria derive final study cohorts interest. Base cohorts built domain (rather cohort definition) one base cohort many study cohorts can derived.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a00_introduction.html","id":"building-a-cohort-set-by-domain","dir":"Articles","previous_headings":"","what":"Building a cohort set by domain","title":"Introduction","text":"Let´s say want build 5 cohorts 3 (asthma, copd, diabetes) defined based concepts seen condition occurrence table 2 (acetaminophen warfarin) based concepts recorded drug exposure table. can build cohorts independently, one . However, approach mean repeating 3 joins condition occurrence tables 2 joins drug exposure table (concepts concept sets). make less computationally expensive, instead create cohorts domain. case instead make one join condition occurrence table one drug exposure (using concept sets together).","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a00_introduction.html","id":"deriving-study-cohorts-from-base-cohorts","dir":"Articles","previous_headings":"","what":"Deriving study cohorts from base cohorts","title":"Introduction","text":"making study cohorts often concept sets define clinical event along various study-specific inclusion criteria, example criteria around amount prior observation age. Often may sensitivity analysis concept set remains inclusion criteria change. situations can make cohorts one--one. However, can lead duplication can see example identify asthma records multiple times. alternative approach build base cohort, case based asthma records, derive multiple cohorts different inclusion criteria applied.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a00_introduction.html","id":"considerations-when-building-cohorts","dir":"Articles","previous_headings":"","what":"Considerations when building cohorts","title":"Introduction","text":"CohortConstructor provides means building cohorts via pipeline, cohorts created application sequence functions. important note order sequence often important implications. example just one individual three recorded diagnoses asthma. One diagnosis 2008 two 2009, last coming individual´s 18th birthday. three cohort pipelines shown restrictions around calendar dates, age, record first. cohort pipeline , however, individual included final cohort, third diagnosis used cohort start. pipeline B C individual excluded.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a01_building_base_cohorts.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Building base cohorts","text":"Let’s first create cdm reference Eunomia synthetic data.","code":"library(CDMConnector) library(CodelistGenerator) library(PatientProfiles) library(CohortConstructor) library(dplyr) con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir()) cdm <- cdm_from_con(con, cdm_schema = \"main\", write_schema = c(prefix = \"my_study_\", schema = \"main\"))"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a01_building_base_cohorts.html","id":"demographic-based-cohort-creation","dir":"Articles","previous_headings":"","what":"Demographic based cohort creation","title":"Building base cohorts","text":"One base cohort can create based around patient demographics. example create cohort people enter 18th birthday leave age 65 ","code":"cdm$working_age_cohort <- demographicsCohort(cdm = cdm, ageRange = c(18, 65), name = \"working_age_cohort\") settings(cdm$working_age_cohort) #> # A tibble: 1 × 3 #> cohort_definition_id cohort_name age_range #> #> 1 1 demographics 18_65 cohortCount(cdm$working_age_cohort) #> # A tibble: 1 × 3 #> cohort_definition_id number_records number_subjects #> #> 1 1 2694 2694 attrition(cdm$working_age_cohort) #> # A tibble: 2 × 7 #> cohort_definition_id number_records number_subjects reason_id reason #> #> 1 1 2694 2694 1 Initial qualify… #> 2 1 2694 2694 2 Age requirement… #> # ℹ 2 more variables: excluded_records , excluded_subjects cdm$working_age_cohort |> addAge(indexDate = \"cohort_start_date\") |> summarise(min_start_age = min(age), median_start_age = median(age), max_start_age = max(age)) #> # Source: SQL [1 x 3] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/Rtmp8sghhQ/file1c2d736042a2.duckdb] #> min_start_age median_start_age max_start_age #> #> 1 17 18 18 cdm$working_age_cohort |> addAge(indexDate = \"cohort_end_date\") |> summarise(min_start_age = min(age), median_start_age = median(age), max_start_age = max(age)) #> # Source: SQL [1 x 3] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/Rtmp8sghhQ/file1c2d736042a2.duckdb] #> min_start_age median_start_age max_start_age #> #> 1 31 57 65"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a01_building_base_cohorts.html","id":"concept-based-cohort-creation","dir":"Articles","previous_headings":"","what":"Concept based cohort creation","title":"Building base cohorts","text":"","code":"drug_codes <- getDrugIngredientCodes(cdm, name = c(\"diclofenac\", \"acetaminophen\")) drug_codes #> #> - 161_acetaminophen (7 codes) #> - 3355_diclofenac (1 codes) cdm$medications <- conceptCohort(cdm = cdm, conceptSet = drug_codes, name = \"medications\") settings(cdm$medications) #> # A tibble: 2 × 4 #> cohort_definition_id cohort_name cdm_version vocabulary_version #> #> 1 1 161_acetaminophen 5.3 v5.0 18-JAN-19 #> 2 2 3355_diclofenac 5.3 v5.0 18-JAN-19 cohortCount(cdm$medications) #> # A tibble: 2 × 3 #> cohort_definition_id number_records number_subjects #> #> 1 1 13908 2679 #> 2 2 830 830"},{"path":[]},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-the-first-record-per-person","dir":"Articles","previous_headings":"","what":"Keep only the first record per person","title":"Cohort Requirements","text":"Individuals can contribute multiple records per cohort. However now ’ll keep earliest cohort entry remaining records using requireIsFirstEntry() CohortConstructor. flow chart illustrates changes cohort 1 (acetaminophen users) restricted first record individual. number individuals remains unchanged, 6,785 records excluded.","code":"cdm$medications <- cdm$medications %>% requireIsFirstEntry() summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-a-specific-range-of-records","dir":"Articles","previous_headings":"","what":"Keep only a specific range of records","title":"Cohort Requirements","text":"can also choose specific range records using requireIsEntry() CohortConstructor. flow chart illustrates changes cohort 1 restricted first five records individual. number individuals remains unchanged, 6,785 records excluded.","code":"cdm$medications <- cdm$medications %>% requireIsEntry(c(1,5)) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-the-last-record-per-person","dir":"Articles","previous_headings":"","what":"Keep only the last record per person","title":"Cohort Requirements","text":"also possible include last record individual using requireIsLastEntry() CohortConstructor. flow chart illustrates changes cohort 1 restricted last record individual. number individuals remains unchanged, 6,785 records excluded.","code":"cdm$medications <- cdm$medications %>% requireIsLastEntry() summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-records-within-a-date-range","dir":"Articles","previous_headings":"","what":"Keep only records within a date range","title":"Cohort Requirements","text":"Individuals may contribute multiple records extended periods. can define study’s start end dates, filtering records fall outside specified date range using requireInDateRang function CohortConstructor. flow chart illustrates changes cohort 1 restricted specified date range. 1,948 individuals 8,660 records excluded.","code":"cdm$medications <- cdm$medications %>% requireInDateRange(dateRange = as.Date(c(\"2010-01-01\", \"2015-01-01\"))) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"keep-only-records-from-cohorts-with-a-minimum-number-of-individuals","dir":"Articles","previous_headings":"","what":"Keep only records from cohorts with a minimum number of individuals","title":"Cohort Requirements","text":"studies might require minimum cohort size. can define minimum size, filtering records smaller required, using requireMinCohortCount function CohortConstructor. Cohort 1 includes 2,580 individuals, none excluded due minimum cohort size restriction 1,000.","code":"cdm$medications <- cdm$medications %>% requireMinCohortCount(minCohortCount = 1000) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a02_cohort_table_requirements.html","id":"running-multiple-requirements","dir":"Articles","previous_headings":"","what":"Running multiple requirements","title":"Cohort Requirements","text":"Multiple restrictions can applied cohort, however care needs taken restrictions placed correct order. example, recommended apply minimum size restriction last. flow chart illustrates changes cohort 1 restricted include first record individual specified date range. 2,529 individuals 9,314 records excluded.","code":"cdm$medications <- cdm$medications %>% requireIsFirstEntry() %>% requireInDateRange(dateRange = as.Date(c(\"2010-01-01\", \"2016-01-01\"))) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"restrict-cohort-by-age","dir":"Articles","previous_headings":"","what":"Restrict cohort by age","title":"Demographic Requirements","text":"can choose specific age range individuals cohort using requireAge() CohortConstructor. flow chart illustrates changes cohort 1 (users acetaminophen) restricted include individuals aged 18 90. 226 individuals 2,863 records excluded. variable ‘cohort_start_date’ used individuals filtered based age entered cohort.","code":"cdm$medications <- cdm$medications %>% requireAge(indexDate = \"cohort_start_date\", ageRange = list(c(18,100))) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"restrict-cohort-by-sex","dir":"Articles","previous_headings":"","what":"Restrict cohort by sex","title":"Demographic Requirements","text":"can also specify sex criteria individuals cohort using requireSex() CohortConstructor. flow chart illustrates changes cohort 1 restricted include ‘female’ individuals. 1,264 individuals 4,647 records excluded.","code":"cdm$medications <- cdm$medications %>% requireSex(sex = \"Female\") summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"restrict-cohort-by-number-of-prior-observations","dir":"Articles","previous_headings":"","what":"Restrict cohort by number of prior observations","title":"Demographic Requirements","text":"can also specify minimum number days prior observations individual using requirePriorObservation() CohortConstructor. flow chart illustrates changes cohort 1 restricted include individuals least 365 days prior observations. 5 individuals 109 records excluded.","code":"cdm$medications <- cdm$medications %>% requirePriorObservation(indexDate = \"cohort_start_date\", minPriorObservation = 365) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"restrict-cohort-by-number-of-future-observations","dir":"Articles","previous_headings":"","what":"Restrict cohort by number of future observations","title":"Demographic Requirements","text":"can also specify minimum number days prior observations individual using requireFutureObservation() CohortConstructor. flow chart illustrates changes cohort 1 restricted include individuals least 365 days future observations. 14 individuals 206 records excluded.","code":"cdm$medications <- cdm$medications %>% requireFutureObservation(indexDate = \"cohort_start_date\", minFutureObservation = 365) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a03_require_demographics.html","id":"applying-multiple-demographic-requirements-to-a-cohort","dir":"Articles","previous_headings":"","what":"Applying multiple demographic requirements to a cohort","title":"Demographic Requirements","text":"can implement multiple demographic requirements cohort using requireDemographics() CohortConstructor. flow chart illustrates changes cohort 1 multiple demographic restrictions, female individuals 18 100 years old, least 365 days prior future observations included. 1,413 individuals 6156 records excluded.","code":"cdm$medications <- cdm$medications %>% requireDemographics(indexDate = \"cohort_start_date\", ageRange = c(18,100), sex = \"Female\", minPriorObservation = 365, minFutureObservation = 365) summary_attrition <- summariseCohortAttrition(cdm$medications) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a04_require_intersections.html","id":"restrictions-on-cohort-presence","dir":"Articles","previous_headings":"","what":"Restrictions on cohort presence","title":"Requirements on Presence and Absence","text":"require individuals medication cohorts seen (seen) another cohort. can use requireCohortIntersect() function, requiring individuals one intersections GI bleed cohort. flow chart illustrates changes cohort 1 (users acetaminophen) restricted include individuals intersect GI bleed cohort least cohort start date. 2,296 individuals 8,765 records excluded. Instead requiring individuals intersect GI bleed cohort, instead require don’t intersect . case can use requireCohortIntersect() function, time set intersections argument 0 require individuals’ absence cohort rather presence . flow chart illustrates changes cohort 1 restricted include individuals intersects GI bleed cohort cohort start date. 36 individuals 600 records excluded.","code":"cdm$medications_gi_bleed <- cdm$medications %>% requireCohortIntersect(intersections = c(1,Inf), targetCohortTable = \"gi_bleed\", targetCohortId = 1, indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) cdm$medications_no_gi_bleed <- cdm$medications %>% requireCohortIntersect(intersections = 0, targetCohortTable = \"gi_bleed\", targetCohortId = 1, indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_no_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a04_require_intersections.html","id":"restrictions-on-concept-presence","dir":"Articles","previous_headings":"","what":"Restrictions on concept presence","title":"Requirements on Presence and Absence","text":"require individuals medication cohorts seen (seen) events related concept list. can use requireConceptIntersect() function, allowing us filter cohort based whether events GI bleeding entered cohort. flow chart illustrates changes cohort 1 restricted include individuals events GI bleeding least cohort start date. 2,296 individuals 8,765 records excluded. Instead requiring individuals events GI bleeding, instead require don’t events . case can use requireConceptIntersect() function, time set intersections argument 0 require individuals without past events GI bleeding. flow chart illustrates changes cohort 1 restricted include individuals events GI bleeding cohort start date. 36 individuals 600 records excluded.","code":"cdm$medications_gi_bleed <- cdm$medications %>% requireConceptIntersect(conceptSet = list(\"gi_bleed\" = 192671), indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) cdm$medications_no_gi_bleed <- cdm$medications %>% requireConceptIntersect(intersections = 0, conceptSet = list(\"gi_bleed\" = 192671), indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_no_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a04_require_intersections.html","id":"restrictions-on-presence-in-clinical-tables","dir":"Articles","previous_headings":"","what":"Restrictions on presence in clinical tables","title":"Requirements on Presence and Absence","text":"clinical table table contains ‘raw’ clinical data. can use clinical tables filter cohorts using requireTableIntersect() function. allow us filter individuals medications cohort based whether intersections GI bleed clinical table . flow chart illustrates changes cohort 1 restricted include individuals intersect GI bleeding clinical table least cohort start date. 2,296 individuals 8,765 records excluded. Instead requiring individuals intersect GI bleed clinical table, instead require don’t intersect . case can use requireCohortIntersect() function, time set intersections argument 0 require individuals’ absence GI bleed clinical table. flow chart illustrates changes cohort 1 restricted include individuals intersects GI bleeding clinical table cohort start date. 36 individuals 600 records excluded.","code":"cdm$medications_gi_bleed <- cdm$medications %>% requireTableIntersect(tableName = \"gi_bleed\", indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_gi_bleed\") summary_attrition <- summariseCohortAttrition(cdm$medications_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1) cdm$medications_no_gi_bleed <- cdm$medications %>% requireTableIntersect(tableName = \"gi_bleed\", indexDate = \"cohort_start_date\", window = c(-Inf, 0), name = \"medications_no_gi_bleed\", intersections = 0) summary_attrition <- summariseCohortAttrition(cdm$medications_no_gi_bleed) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a04_require_intersections.html","id":"restrictions-on-deaths","dir":"Articles","previous_headings":"","what":"Restrictions on deaths","title":"Requirements on Presence and Absence","text":"require individuals medication cohorts seen () death. can use requireDeathFlag() function, requiring individuals seen (seen) died cohort start date. flow chart illustrates changes cohort 1 restricted include individuals died cohort start date. None individuals cohort 1 died therefore excluded cohort. exclude individuals died add argument ‘negate = TRUE’ function requireDeathFlag(). flow chart illustrates changes cohort 1 restricted include individuals die cohort start date. None individuals cohort 1 died therefore one excluded.","code":"cdm$medications_deaths <- cdm$medications %>% requireDeathFlag(window = c(0,Inf), name = \"medications_deaths\") summary_attrition <- summariseCohortAttrition(cdm$medications_deaths) plotCohortAttrition(summary_attrition, cohortId = 1) cdm$medications_no_deaths <- cdm$medications %>% requireDeathFlag(window = c(0,Inf), name = \"medications_no_deaths\", negate = TRUE) summary_attrition <- summariseCohortAttrition(cdm$medications_no_deaths) plotCohortAttrition(summary_attrition, cohortId = 1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Generate a matched age and sex cohort","text":"CohortConstructor packages includes function obtain age sex matched cohort, generateMatchedCohortSet() function. vignette, explore usage function.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"create-mock-data","dir":"Articles","previous_headings":"Introduction","what":"Create mock data","title":"Generate a matched age and sex cohort","text":"first use mockDrugUtilisation() function DrugUtilisation package create mock data. use cohort1 explore generateMatchedCohortSet(), let us first use cohort_attrition() CDMConnector package explore cohort:","code":"library(CohortConstructor) library(dplyr) cdm <- mockCohortConstructor(nPerson = 1000) CDMConnector::cohort_set(cdm$cohort1)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"use-generatematchedcohortset-to-create-an-age-sex-matched-cohort","dir":"Articles","previous_headings":"","what":"Use generateMatchedCohortSet() to create an age-sex matched cohort","title":"Generate a matched age and sex cohort","text":"Let us first see example function works. usage, need provide cdm object, targetCohortName, name table containing cohort interest, name new generated tibble containing cohort matched cohort. also use argument targetCohortId specify want matched cohort cohort_definition_id = 1. Notice generated tibble, two cohorts: cohort_definition_id = 1 (original cohort), cohort_definition_id = 4 (matched cohort). target_cohort_name column indicates original cohort. match_sex match_year_of_birth adopt boolean values (TRUE/FALSE) indicating matched sex age, . match_status indicate original cohort (target) matched cohort (matched). target_cohort_id indicates cohort_id original cohort. Check exclusion criteria applied generate new cohorts using cohort_attrition() CDMConnector package: Briefly, original cohort, exclude first individuals match, individuals matching pair observation assigned cohort_start_date. matched cohort, start whole database first exclude individuals original cohort. Afterwards, exclude individuals match, individuals observation assigned cohort_start_date, finally remove many individuals required fulfill ratio. Notice matching pairs randomly assigned, probable every time execute function, generated cohorts change. Use set.seed() avoid .","code":"cdm$matched_cohort1 <- matchCohorts( cohort = cdm$cohort1, cohortId = 1, name = \"matched_cohort1\") CDMConnector::cohort_set(cdm$matched_cohort1) # Original cohort CDMConnector::cohort_attrition(cdm$matched_cohort1) %>% filter(cohort_definition_id == 1) # Matched cohort CDMConnector::cohort_attrition(cdm$matched_cohort1) %>% filter(cohort_definition_id == 4)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"matchsex-parameter","dir":"Articles","previous_headings":"Use generateMatchedCohortSet() to create an age-sex matched cohort","what":"matchSex parameter","title":"Generate a matched age and sex cohort","text":"matchSex boolean parameter (TRUE/FALSE) indicating want match sex (TRUE) want (FALSE).","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"matchyear-parameter","dir":"Articles","previous_headings":"Use generateMatchedCohortSet() to create an age-sex matched cohort","what":"matchYear parameter","title":"Generate a matched age and sex cohort","text":"matchYear another boolean parameter (TRUE/FALSE) indicating want match age (TRUE) want (FALSE). Notice matchSex = FALSE matchYear = FALSE, obtain unmatched comparator cohort.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"ratio-parameter","dir":"Articles","previous_headings":"Use generateMatchedCohortSet() to create an age-sex matched cohort","what":"ratio parameter","title":"Generate a matched age and sex cohort","text":"default matching ratio 1:1 (ratio = 1). Use cohort_counts() CDMConnector check matching done desired. can modify ratio parameter tailor matched cohort. ratio can adopt values 1 Inf.","code":"CDMConnector::cohort_count(cdm$matched_cohort1) cdm$matched_cohort2 <- matchCohorts( cohort = cdm$cohort1, cohortId = 1, name = \"matched_cohort2\", ratio = Inf) CDMConnector::cohort_count(cdm$matched_cohort2)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a10_match_cohorts.html","id":"generate-matched-cohorts-simultaneously-across-multiple-cohorts","dir":"Articles","previous_headings":"Use generateMatchedCohortSet() to create an age-sex matched cohort","what":"Generate matched cohorts simultaneously across multiple cohorts","title":"Generate a matched age and sex cohort","text":"functionalities can implemented across multiple cohorts simultaneously. Specify targetCohortId parameter cohorts interest. set NULL, cohorts present targetCohortName matched. Notice cohort (independent cohorts) matched cohort.","code":"cdm$matched_cohort3 <- matchCohorts( cohort = cdm$cohort1, cohortId = c(1,3), name = \"matched_cohort3\", ratio = 2) CDMConnector::cohort_set(cdm$matched_cohort3) %>% arrange(cohort_definition_id) CDMConnector::cohort_count(cdm$matched_cohort3) %>% arrange(cohort_definition_id)"},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"CohortConstructor benchmark","text":"Cohorts fundamental building block studies use OMOP CDM, identifying people satisfy one inclusion criteria duration time based clinical records. Currently cohorts typically built using CIRCE allows complex cohorts represented using JSON. JSON converted SQL execution database containing data mapped OMOP CDM. CIRCE JSON can created via ATLAS GUI programmatically via Capr R package. However, although powerful tool expressing operationalising cohort definitions, SQL generated can cumbersome especially complex cohort definitions, moreover cohorts instantiated independently, leading duplicated work. CohortConstructor package offers alternative approach, emphasizing cohort building pipeline format. first creates base cohorts applies specific inclusion criteria. Unlike “definition” approach, ::cohorts built independently, CohortConstructor follows “domain” approach, minimizes redundant queries large OMOP tables. details approach can found Introduction vignette. benchmarked package using nine phenotypes OHDSI Phenotype library cover range concept domains, entry inclusion criteria, cohort exit options. replicated cohorts using CodelistGenerator CohortConstructor assess computational time agreement CIRCE CohortConstructor.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"code-and-collaboration","dir":"Articles","previous_headings":"Introduction","what":"Code and collaboration","title":"CohortConstructor benchmark","text":"benchmarking code available BenchmarkCohortConstructor repository GitHub. interested running code database, feel free reach us assistance, can also update vignette results! :) benchmark script executed following four databases: CPRD Gold: primary care database UK, capturing data mostly Northern Ireland, Wales, Scotland clinics. benchmark utilized 100,000-person sample dataset, managed using PostgreSQL. CPRD Aurum: Another UK primary care database, primarily covering clinics England. database managed SQL Server. Coriva: sample approximately 400,000 patients Estonia National Health Insurance database, managed PostgreSQL. OHDSI SQL Server: mock OMOP CDM dataset provided OHDSI, hosted SQL Server. table presents number records OMOP tables used benchmark script participating databases.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"cohorts","dir":"Articles","previous_headings":"","what":"Cohorts","title":"CohortConstructor benchmark","text":"replicated following cohorts OHDSI phenotype library: COVID-19 (ID 56), inpatient hospitalisation (23), new users beta blockers nested essential hypertension (1049), transverse myelitis (63), major non cardiac surgery (1289), asthma without COPD (27), endometriosis procedure (722), new fluoroquinolone users (1043), acquired neutropenia unspecified leukopenia (213). COVID-19 cohort used evaluate performance common cohort stratifications. compare package CIRCE, created definitions Atlas, stratified age groups sex, available benchmark GitHub repository benchmark code.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"cohort-counts-and-overlap","dir":"Articles","previous_headings":"Cohorts","what":"Cohort counts and overlap","title":"CohortConstructor benchmark","text":"following table displays number records subjects cohort across participating databases: also computed overlap patients CIRCE CohortConstructor cohorts, results shown plot :","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"performance","dir":"Articles","previous_headings":"","what":"Performance","title":"CohortConstructor benchmark","text":"evaluate CohortConstructor performance generated CIRCE cohorts using functionalities provided CodelistGenerator CohortConstructor, measured computational time taken. Two different approaches CohortConstructor tested: definition: created cohorts seprately. domain: nine targeted cohorts created together set, following domain approach described Introduction vignette. Briefly, approach involves creating base cohorts , requiring one call involved OMOP table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"by-definition","dir":"Articles","previous_headings":"Performance","what":"By definition","title":"CohortConstructor benchmark","text":"following plot shows times taken create cohort using CIRCE CohortConstructor cohorts created separately.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"by-domain","dir":"Articles","previous_headings":"Performance","what":"By domain","title":"CohortConstructor benchmark","text":"table depicts total time took create nine cohorts using domain approach CohortConstructor.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"cohort-stratification","dir":"Articles","previous_headings":"Performance","what":"Cohort stratification","title":"CohortConstructor benchmark","text":"Cohorts often stratified studies. Atlas cohort definitions, stratum requires new CIRCE JSON instantiated, CohortConstructor allows stratifications generated overall cohort. following table shows time taken create age sex stratifications COVID-19 cohort CIRCE CohortConstructor.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/articles/a11_benchmark.html","id":"use-of-sql-indexes","dir":"Articles","previous_headings":"Performance","what":"Use of SQL indexes","title":"CohortConstructor benchmark","text":"Postgres SQL databases, package uses indexes conceptCohort default. evaluate much indexes reduce computation time, instantiated subset concept sets benchmark, without indexes. Four calls made conceptCohort, involving different number OMOP tables. combinations : Drug exposure Drug exposure + condition occurrence Drug exposure + condition occurrence + procedure occurrence Drug exposure + condition occurrence + procedure occurrence + measurement plot shows computation time without SQL indexes scenario:","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Edward Burn. Author, maintainer. Marti Catala. Author. Nuria Mercade-Besora. Author. Marta Alcalde-Herraiz. Author. Mike Du. Author. Yuchen Guo. Author. Xihang Chen. Author. Kim Lopez-Guell. Author. Elin Rowlands. Author.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Burn E, Catala M, Mercade-Besora N, Alcalde-Herraiz M, Du M, Guo Y, Chen X, Lopez-Guell K, Rowlands E (2024). CohortConstructor: Build Manipulate Study Cohorts Using Common Data Model. R package version 0.3.0.900, https://ohdsi.github.io/CohortConstructor/.","code":"@Manual{, title = {CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model}, author = {Edward Burn and Marti Catala and Nuria Mercade-Besora and Marta Alcalde-Herraiz and Mike Du and Yuchen Guo and Xihang Chen and Kim Lopez-Guell and Elin Rowlands}, year = {2024}, note = {R package version 0.3.0.900}, url = {https://ohdsi.github.io/CohortConstructor/}, }"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"cohortconstructor-","dir":"","previous_headings":"","what":"Build and Manipulate Study Cohorts Using a Common Data Model","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"goal CohortConstructor support creation manipulation study cohorts data mapped OMOP CDM.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"package can installed CRAN: can install development version package GitHub:","code":"install.packages(\"CohortConstructor\") # install.packages(\"devtools\") devtools::install_github(\"ohdsi/CohortConstructor\")"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"creating-and-manipulating-cohorts","dir":"","previous_headings":"","what":"Creating and manipulating cohorts","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"illustrate functionality provided CohortConstructor let’s create set fracture cohorts using Eunomia dataset. ’ll first load required packages create cdm reference data.","code":"library(omopgenerics) library(CDMConnector) library(PatientProfiles) library(dplyr) library(CohortConstructor) library(CohortCharacteristics) con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir()) cdm <- cdm_from_con(con, cdm_schema = \"main\", write_schema = c(prefix = \"my_study_\", schema = \"main\")) cdm #> #> ── # OMOP CDM reference (duckdb) of Synthea synthetic health database ────────── #> • omop tables: person, observation_period, visit_occurrence, visit_detail, #> condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, #> measurement, observation, death, note, note_nlp, specimen, fact_relationship, #> location, care_site, provider, payer_plan_period, cost, drug_era, dose_era, #> condition_era, metadata, cdm_source, concept, vocabulary, domain, #> concept_class, concept_relationship, relationship, concept_synonym, #> concept_ancestor, source_to_concept_map, drug_strength #> • cohort tables: - #> • achilles tables: - #> • other tables: -"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"generating-concept-based-fracture-cohorts","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Generating concept-based fracture cohorts","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"start making simple concept-based cohort fracture interest. First create codelist ankle, forearm hip fractures (note, just use one code using synthetic data). Now can quickly create set cohorts fracture type. need provide codes defined get cohort back, cohort end date set event date associated records, overlapping records collapsed, records observation kept. can see starting cohorts, add additional restrictions, following associated settings, counts, attrition.","code":"fracture_codes <- newCodelist(list(\"ankle_fracture\" = 4059173L, \"forearm_fracture\" = 4278672L, \"hip_fracture\" = 4230399L)) fracture_codes #> #> ── 3 codelists ───────────────────────────────────────────────────────────────── #> #> - ankle_fracture (1 codes) #> - forearm_fracture (1 codes) #> - hip_fracture (1 codes) cdm$fractures <- cdm |> conceptCohort(conceptSet = fracture_codes, name = \"fractures\") settings(cdm$fractures) %>% glimpse() #> Rows: 3 #> Columns: 4 #> $ cohort_definition_id 1, 2, 3 #> $ cohort_name \"ankle_fracture\", \"forearm_fracture\", \"hip_fractu… #> $ cdm_version \"5.3\", \"5.3\", \"5.3\" #> $ vocabulary_version \"v5.0 18-JAN-19\", \"v5.0 18-JAN-19\", \"v5.0 18-JAN-… cohort_count(cdm$fractures) %>% glimpse() #> Rows: 3 #> Columns: 3 #> $ cohort_definition_id 1, 2, 3 #> $ number_records 464, 569, 138 #> $ number_subjects 427, 510, 132 attrition(cdm$fractures) %>% glimpse() #> Rows: 3 #> Columns: 7 #> $ cohort_definition_id 1, 2, 3 #> $ number_records 464, 569, 138 #> $ number_subjects 427, 510, 132 #> $ reason_id 1, 1, 1 #> $ reason \"Initial qualifying events\", \"Initial qualifying … #> $ excluded_records 0, 0, 0 #> $ excluded_subjects 0, 0, 0"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"create-an-overall-fracture-cohort","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Create an overall fracture cohort","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"far created three separate fracture cohorts. Let’s say also want cohort people fractures. union three cohorts create overall cohort like :","code":"cdm$fractures <- unionCohorts(cdm$fractures, cohortName = \"any_fracture\", keepOriginalCohorts = TRUE, name =\"fractures\") settings(cdm$fractures) #> # A tibble: 4 × 5 #> cohort_definition_id cohort_name cdm_version vocabulary_version gap #> #> 1 1 ankle_fracture 5.3 v5.0 18-JAN-19 NA #> 2 2 forearm_fracture 5.3 v5.0 18-JAN-19 NA #> 3 3 hip_fracture 5.3 v5.0 18-JAN-19 NA #> 4 4 any_fracture 0 cohortCount(cdm$fractures) #> # A tibble: 4 × 3 #> cohort_definition_id number_records number_subjects #> #> 1 1 464 427 #> 2 2 569 510 #> 3 3 138 132 #> 4 4 1171 924"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"require-in-date-range","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Require in date range","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"created base fracture cohort, can start applying additional cohort requirements. example, first can require individuals’ cohort start date fall within certain date range. Now ’ve applied date restriction, can see cohort attributes updated","code":"cdm$fractures <- cdm$fractures %>% requireInDateRange(dateRange = as.Date(c(\"2000-01-01\", \"2020-01-01\"))) cohort_count(cdm$fractures) %>% glimpse() #> Rows: 4 #> Columns: 3 #> $ cohort_definition_id 1, 2, 3, 4 #> $ number_records 108, 152, 62, 322 #> $ number_subjects 104, 143, 60, 287 attrition(cdm$fractures) %>% filter(reason == \"cohort_start_date between 2000-01-01 & 2020-01-01\") %>% glimpse() #> Rows: 0 #> Columns: 7 #> $ cohort_definition_id #> $ number_records #> $ number_subjects #> $ reason_id #> $ reason #> $ excluded_records #> $ excluded_subjects "},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"applying-demographic-requirements","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Applying demographic requirements","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"can also add restrictions patient characteristics age (cohort start date default) sex. can see many individuals ’ve lost applying criteria.","code":"cdm$fractures <- cdm$fractures %>% requireDemographics(ageRange = list(c(40, 65)), sex = \"Female\") attrition(cdm$fractures) %>% filter(reason == \"Age requirement: 40 to 65\") %>% glimpse() #> Rows: 4 #> Columns: 7 #> $ cohort_definition_id 1, 2, 3, 4 #> $ number_records 43, 64, 22, 129 #> $ number_subjects 43, 62, 22, 122 #> $ reason_id 4, 4, 4, 4 #> $ reason \"Age requirement: 40 to 65\", \"Age requirement: 40… #> $ excluded_records 65, 88, 40, 193 #> $ excluded_subjects 61, 81, 38, 165 attrition(cdm$fractures) %>% filter(reason == \"Sex requirement: Female\") %>% glimpse() #> Rows: 4 #> Columns: 7 #> $ cohort_definition_id 1, 2, 3, 4 #> $ number_records 19, 37, 12, 68 #> $ number_subjects 19, 36, 12, 65 #> $ reason_id 5, 5, 5, 5 #> $ reason \"Sex requirement: Female\", \"Sex requirement: Fema… #> $ excluded_records 24, 27, 10, 61 #> $ excluded_subjects 24, 26, 10, 57"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"require-presence-in-another-cohort","dir":"","previous_headings":"Creating and manipulating cohorts","what":"Require presence in another cohort","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"can also require individuals () another cohort window. example require study participants GI bleed cohort time prior entry fractures cohort.","code":"cdm$gibleed <- cdm |> conceptCohort(conceptSet = list(\"gibleed\" = 192671L), name = \"gibleed\") cdm$fractures <- cdm$fractures %>% requireCohortIntersect(targetCohortTable = \"gibleed\", intersections = 0, window = c(-Inf, 0)) attrition(cdm$fractures) %>% filter(reason == \"Not in cohort gibleed between -Inf & 0 days relative to cohort_start_date\") %>% glimpse() #> Rows: 4 #> Columns: 7 #> $ cohort_definition_id 1, 2, 3, 4 #> $ number_records 14, 30, 10, 54 #> $ number_subjects 14, 30, 10, 52 #> $ reason_id 8, 8, 8, 8 #> $ reason \"Not in cohort gibleed between -Inf & 0 days rela… #> $ excluded_records 5, 7, 2, 14 #> $ excluded_subjects 5, 6, 2, 13 cdmDisconnect(cdm)"},{"path":"https://ohdsi.github.io/CohortConstructor/index.html","id":"more-information","dir":"","previous_headings":"Creating and manipulating cohorts","what":"More information","title":"Build and Manipulate Study Cohorts Using a Common Data Model","text":"CohortConstructor provides much functionality creating manipulating cohorts. See package website details.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/CohortConstructor-package.html","id":null,"dir":"Reference","previous_headings":"","what":"CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model — CohortConstructor-package","title":"CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model — CohortConstructor-package","text":"Create manipulate study cohorts data mapped Observational Medical Outcomes Partnership Common Data Model.","code":""},{"path":[]},{"path":"https://ohdsi.github.io/CohortConstructor/reference/CohortConstructor-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model — CohortConstructor-package","text":"Maintainer: Edward Burn edward.burn@ndorms.ox.ac.uk (ORCID) Authors: Marti Catala marti.catalasabate@ndorms.ox.ac.uk (ORCID) Nuria Mercade-Besora nuria.mercadebesora@ndorms.ox.ac.uk (ORCID) Marta Alcalde-Herraiz marta.alcaldeherraiz@ndorms.ox.ac.uk (ORCID) Mike Du mike.du@ndorms.ox.ac.uk (ORCID) Yuchen Guo yuchen.guo@ndorms.ox.ac.uk (ORCID) Xihang Chen xihang.chen@ndorms.ox.ac.uk (ORCID) Kim Lopez kim.lopez@spc.ox.ac.uk (ORCID) Elin Rowlands elin.rowlands@ndorms.ox.ac.uk (ORCID)","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/benchmarkData.html","id":null,"dir":"Reference","previous_headings":"","what":"Benchmarking results — benchmarkData","title":"Benchmarking results — benchmarkData","text":"Benchmarking results","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/benchmarkData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Benchmarking results — benchmarkData","text":"","code":"benchmarkData"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/benchmarkData.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Benchmarking results — benchmarkData","text":"list results benchmarking","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cdmDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of cdm. — cdmDoc","title":"Helper for consistent documentation of cdm. — cdmDoc","text":"Helper consistent documentation cdm.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cdmDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of cdm. — cdmDoc","text":"cdm cdm reference.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of cohort. — cohortDoc","title":"Helper for consistent documentation of cohort. — cohortDoc","text":"Helper consistent documentation cohort.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of cohort. — cohortDoc","text":"cohort cohort table cdm reference.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortIdModifyDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of cohortId. — cohortIdModifyDoc","title":"Helper for consistent documentation of cohortId. — cohortIdModifyDoc","text":"Helper consistent documentation cohortId.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortIdModifyDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of cohortId. — cohortIdModifyDoc","text":"cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortIdSubsetDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of cohortId. — cohortIdSubsetDoc","title":"Helper for consistent documentation of cohortId. — cohortIdSubsetDoc","text":"Helper consistent documentation cohortId.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/cohortIdSubsetDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of cohortId. — cohortIdSubsetDoc","text":"cohortId Vector identifying cohorts include (cohort_definition_id cohort_name). Cohorts included removed cohort set.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/collapseCohorts.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","title":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","text":"collapseCohorts() concatenates cohort records, allowing number days one finishing next starting.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/collapseCohorts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","text":"","code":"collapseCohorts(cohort, cohortId = NULL, gap = 0, name = tableName(cohort))"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/collapseCohorts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. gap Number days two subsequent cohort entries merged single cohort record. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/collapseCohorts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapse cohort entries using a certain gap to concatenate records. — collapseCohorts","text":"cohort table","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/columnDateDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of dateColumns and returnReason. — columnDateDoc","title":"Helper for consistent documentation of dateColumns and returnReason. — columnDateDoc","text":"Helper consistent documentation dateColumns returnReason.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/columnDateDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of dateColumns and returnReason. — columnDateDoc","text":"dateColumns Character vector indicating date columns cohort table consider. returnReason TRUE return column indicating dateColumns used.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":null,"dir":"Reference","previous_headings":"","what":"Create cohorts based on a concept set — conceptCohort","title":"Create cohorts based on a concept set — conceptCohort","text":"conceptCohort() creates cohort table patient records clinical tables OMOP CDM. following tables currently supported creating concept cohorts: condition_occurrence device_exposure drug_exposure measurement observation procedure_occurrence visit_occurrence Cohort duration based record start end (e.g. condition_start_date condition_end_date records coming condition_occurrence tables). resulting table satisfies requirements OMOP CDM cohort table: Overlapping records collapsed single cohort entry. record starts outside observation period silently ignored. record ends outside observation period trimmed end preceding observation period end date.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create cohorts based on a concept set — conceptCohort","text":"","code":"conceptCohort( cdm, conceptSet, name, exit = \"event_end_date\", useSourceFields = FALSE )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create cohorts based on a concept set — conceptCohort","text":"cdm cdm reference. conceptSet conceptSet, can either codelist conceptSetExpression. name Name new cohort table created cdm object. exit cohort end date defined. Can either \"event_end_date\" \"event_start_date\". useSourceFields TRUE, source concept_id fields also used identifying relevant clinical records. FALSE, standard concept_id fields used.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create cohorts based on a concept set — conceptCohort","text":"cohort table","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptCohort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create cohorts based on a concept set — conceptCohort","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(conditionOccurrence = TRUE) #> Note: method with signature ‘DBIConnection#Id’ chosen for function ‘dbExistsTable’, #> target signature ‘duckdb_connection#Id’. #> \"duckdb_connection#ANY\" would also be valid cohort <- conceptCohort(cdm = cdm, conceptSet = list(a = 1), name = \"cohort\") #> Warning: ! `codelist` contains numeric values, they are casted to integers. #> ✖ Domain NA (1 concept) excluded because it is not supported. #> ℹ No cohort entries found, returning empty cohort table. cohort |> attrition() #> # A tibble: 1 × 7 #> cohort_definition_id number_records number_subjects reason_id reason #> #> 1 1 0 0 1 Initial qualify… #> # ℹ 2 more variables: excluded_records , excluded_subjects # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptSetDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of conceptSet. — conceptSetDoc","title":"Helper for consistent documentation of conceptSet. — conceptSetDoc","text":"Helper consistent documentation conceptSet.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/conceptSetDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of conceptSet. — conceptSetDoc","text":"conceptSet conceptSet, can either codelist conceptSetExpression.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":null,"dir":"Reference","previous_headings":"","what":"Create cohorts based on patient demographics — demographicsCohort","title":"Create cohorts based on patient demographics — demographicsCohort","text":"demographicsCohort() creates cohort table based patient characteristics. individual satisfies criteria enter cohort. stop satisfying criteria cohort entry ends.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create cohorts based on patient demographics — demographicsCohort","text":"","code":"demographicsCohort( cdm, name, ageRange = NULL, sex = NULL, minPriorObservation = NULL )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create cohorts based on patient demographics — demographicsCohort","text":"cdm cdm reference. name Name new cohort table created cdm object. ageRange list vectors specifying minimum maximum age. sex Can \"\", \"Male\" \"Female\". minPriorObservation minimum number continuous prior observation days database.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create cohorts based on patient demographics — demographicsCohort","text":"cohort table","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/demographicsCohort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create cohorts based on patient demographics — demographicsCohort","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor() cohort <- cdm |> demographicsCohort(name = \"cohort3\", ageRange = c(18,40), sex = \"Male\") #> ! cohort columns will be reordered to match the expected order: #> cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date. #> ℹ Building new trimmed cohort #> Adding demographics information #> Creating initial cohort #> Trim sex #> Trim age #> ✔ Cohort trimmed attrition(cohort) #> # A tibble: 3 × 7 #> cohort_definition_id number_records number_subjects reason_id reason #> #> 1 1 10 10 1 Initial qualify… #> 2 1 2 2 2 Sex requirement… #> 3 1 2 2 3 Age requirement… #> # ℹ 2 more variables: excluded_records , excluded_subjects # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":null,"dir":"Reference","previous_headings":"","what":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"entryAtFirstDate() resets cohort start date based set specified column dates. first date occurs chosen.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"","code":"entryAtFirstDate( cohort, dateColumns, cohortId = NULL, returnReason = TRUE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"cohort cohort table cdm reference. dateColumns Character vector indicating date columns cohort table consider. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. returnReason TRUE return column indicating dateColumns used. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtFirstDate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update cohort start date to be the first date from of a set of column dates — entryAtFirstDate","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(tables = list( \"cohort\" = dplyr::tibble( cohort_definition_id = 1, subject_id = c(1, 2, 3, 4), cohort_start_date = as.Date(c(\"2000-06-03\", \"2000-01-01\", \"2015-01-15\", \"2000-12-09\")), cohort_end_date = as.Date(c(\"2001-09-01\", \"2001-01-12\", \"2015-02-15\", \"2002-12-09\")), date_1 = as.Date(c(\"2001-08-01\", \"2001-01-01\", \"2015-01-15\", \"2002-12-09\")), date_2 = as.Date(c(\"2001-08-01\", NA, \"2015-02-14\", \"2002-12-09\")) ) )) #> Warning: ! 7 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `gender_concept_id` is numeric but expected integer #> • `year_of_birth` is numeric but expected integer #> • `month_of_birth` is numeric but expected integer #> • `race_concept_id` is numeric but expected integer #> • `ethnicity_concept_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 2 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> • `period_type_concept_id` is numeric but expected integer #> Warning: ! 9 column in cdm_source do not match expected column type: #> • `cdm_source_abbreviation` is logical but expected character #> • `cdm_holder` is logical but expected character #> • `source_description` is logical but expected character #> • `source_documentation_reference` is logical but expected character #> • `cdm_etl_reference` is logical but expected character #> • `source_release_date` is logical but expected date #> • `cdm_release_date` is logical but expected date #> • `cdm_version` is numeric but expected character #> • `vocabulary_version` is logical but expected character #> Warning: ! 3 column in concept do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 1 column in vocabulary do not match expected column type: #> • `vocabulary_concept_id` is numeric but expected integer #> Warning: ! 5 column in concept_relationship do not match expected column type: #> • `concept_id_1` is numeric but expected integer #> • `concept_id_2` is numeric but expected integer #> • `valid_start_date` is logical but expected date #> • `valid_end_date` is logical but expected date #> • `invalid_reason` is logical but expected character #> Warning: ! 2 column in concept_synonym do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `language_concept_id` is numeric but expected integer #> Warning: ! 4 column in concept_ancestor do not match expected column type: #> • `ancestor_concept_id` is numeric but expected integer #> • `descendant_concept_id` is numeric but expected integer #> • `min_levels_of_separation` is numeric but expected integer #> • `max_levels_of_separation` is numeric but expected integer #> Warning: ! 9 column in drug_strength do not match expected column type: #> • `drug_concept_id` is numeric but expected integer #> • `ingredient_concept_id` is numeric but expected integer #> • `amount_unit_concept_id` is numeric but expected integer #> • `numerator_unit_concept_id` is numeric but expected integer #> • `denominator_value` is logical but expected numeric #> • `denominator_unit_concept_id` is numeric but expected integer #> • `box_size` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> Warning: ! 2 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 1 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer cdm$cohort |> entryAtLastDate(dateColumns = c(\"date_1\", \"date_2\")) #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> # Source: table [4 x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date entry_reason #> #> 1 1 4 2002-12-09 2002-12-09 date_1; dat… #> 2 1 1 2001-08-01 2001-09-01 date_1; dat… #> 3 1 2 2001-01-01 2001-01-12 date_1 #> 4 1 3 2015-02-14 2015-02-15 date_2 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort start date to the last of a set of column dates — entryAtLastDate","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"entryAtLastDate() resets cohort end date based set specified column dates. last date chosen.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"","code":"entryAtLastDate( cohort, dateColumns, cohortId = NULL, returnReason = TRUE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"cohort cohort table cdm reference. dateColumns Character vector indicating date columns cohort table consider. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. returnReason TRUE return column indicating dateColumns used. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/entryAtLastDate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort start date to the last of a set of column dates — entryAtLastDate","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(tables = list( \"cohort\" = dplyr::tibble( cohort_definition_id = 1, subject_id = c(1, 2, 3, 4), cohort_start_date = as.Date(c(\"2000-06-03\", \"2000-01-01\", \"2015-01-15\", \"2000-12-09\")), cohort_end_date = as.Date(c(\"2001-09-01\", \"2001-01-12\", \"2015-02-15\", \"2002-12-09\")), date_1 = as.Date(c(\"2001-08-01\", \"2001-01-01\", \"2015-01-15\", \"2002-12-09\")), date_2 = as.Date(c(\"2001-08-01\", NA, \"2015-02-14\", \"2002-12-09\")) ) )) #> Warning: ! 7 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `gender_concept_id` is numeric but expected integer #> • `year_of_birth` is numeric but expected integer #> • `month_of_birth` is numeric but expected integer #> • `race_concept_id` is numeric but expected integer #> • `ethnicity_concept_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 2 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> • `period_type_concept_id` is numeric but expected integer #> Warning: ! 9 column in cdm_source do not match expected column type: #> • `cdm_source_abbreviation` is logical but expected character #> • `cdm_holder` is logical but expected character #> • `source_description` is logical but expected character #> • `source_documentation_reference` is logical but expected character #> • `cdm_etl_reference` is logical but expected character #> • `source_release_date` is logical but expected date #> • `cdm_release_date` is logical but expected date #> • `cdm_version` is numeric but expected character #> • `vocabulary_version` is logical but expected character #> Warning: ! 3 column in concept do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 1 column in vocabulary do not match expected column type: #> • `vocabulary_concept_id` is numeric but expected integer #> Warning: ! 5 column in concept_relationship do not match expected column type: #> • `concept_id_1` is numeric but expected integer #> • `concept_id_2` is numeric but expected integer #> • `valid_start_date` is logical but expected date #> • `valid_end_date` is logical but expected date #> • `invalid_reason` is logical but expected character #> Warning: ! 2 column in concept_synonym do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `language_concept_id` is numeric but expected integer #> Warning: ! 4 column in concept_ancestor do not match expected column type: #> • `ancestor_concept_id` is numeric but expected integer #> • `descendant_concept_id` is numeric but expected integer #> • `min_levels_of_separation` is numeric but expected integer #> • `max_levels_of_separation` is numeric but expected integer #> Warning: ! 9 column in drug_strength do not match expected column type: #> • `drug_concept_id` is numeric but expected integer #> • `ingredient_concept_id` is numeric but expected integer #> • `amount_unit_concept_id` is numeric but expected integer #> • `numerator_unit_concept_id` is numeric but expected integer #> • `denominator_value` is logical but expected numeric #> • `denominator_unit_concept_id` is numeric but expected integer #> • `box_size` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> Warning: ! 2 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 1 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer cdm$cohort |> entryAtLastDate(dateColumns = c(\"date_1\", \"date_2\")) #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> # Source: table [4 x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date entry_reason #> #> 1 1 2 2001-01-01 2001-01-12 date_1 #> 2 1 3 2015-02-14 2015-02-15 date_2 #> 3 1 1 2001-08-01 2001-09-01 date_1; dat… #> 4 1 4 2002-12-09 2002-12-09 date_1; dat… # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort end date to death date — exitAtDeath","title":"Set cohort end date to death date — exitAtDeath","text":"functions changes cohort end date subject's death date. case generates overlapping records cohort, overlapping entries merged.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort end date to death date — exitAtDeath","text":"","code":"exitAtDeath( cohort, cohortId = NULL, requireDeath = FALSE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort end date to death date — exitAtDeath","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. requireDeath TRUE, subjects without death record dropped, FALSE end date left . name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort end date to death date — exitAtDeath","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtDeath.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort end date to death date — exitAtDeath","text":"","code":"# \\donttest{ library(PatientProfiles) library(CohortConstructor) cdm <- mockPatientProfiles() cdm$cohort1 |> exitAtDeath() #> # Source: table [10 x 4] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> #> 1 1 1 1943-11-26 1949-06-21 #> 2 2 4 1949-03-07 1951-05-08 #> 3 1 10 1948-08-08 1952-03-27 #> 4 3 6 1966-09-27 1975-03-05 #> 5 2 5 1966-05-05 1970-11-15 #> 6 2 3 1939-07-24 1944-01-14 #> 7 3 8 1921-10-07 1931-05-01 #> 8 1 9 1940-05-01 1945-06-29 #> 9 3 2 1918-06-14 1921-11-28 #> 10 3 7 1962-05-14 1964-03-08 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"exitAtFirstDate() resets cohort end date based set specified column dates. first date occurs chosen.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"","code":"exitAtFirstDate( cohort, dateColumns, cohortId = NULL, returnReason = TRUE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"cohort cohort table cdm reference. dateColumns Character vector indicating date columns cohort table consider. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. returnReason TRUE return column indicating dateColumns used. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtFirstDate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort end date to the first of a set of column dates — exitAtFirstDate","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(tables = list( \"cohort\" = dplyr::tibble( cohort_definition_id = 1, subject_id = c(1, 2, 3, 4), cohort_start_date = as.Date(c(\"2000-06-03\", \"2000-01-01\", \"2015-01-15\", \"2000-12-09\")), cohort_end_date = as.Date(c(\"2001-09-01\", \"2001-01-12\", \"2015-02-15\", \"2002-12-09\")), date_1 = as.Date(c(\"2001-08-01\", \"2001-01-01\", \"2015-01-15\", \"2002-12-09\")), date_2 = as.Date(c(\"2001-08-01\", NA, \"2015-04-15\", \"2002-12-09\")) ) )) #> Warning: ! 7 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `gender_concept_id` is numeric but expected integer #> • `year_of_birth` is numeric but expected integer #> • `month_of_birth` is numeric but expected integer #> • `race_concept_id` is numeric but expected integer #> • `ethnicity_concept_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 2 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> • `period_type_concept_id` is numeric but expected integer #> Warning: ! 9 column in cdm_source do not match expected column type: #> • `cdm_source_abbreviation` is logical but expected character #> • `cdm_holder` is logical but expected character #> • `source_description` is logical but expected character #> • `source_documentation_reference` is logical but expected character #> • `cdm_etl_reference` is logical but expected character #> • `source_release_date` is logical but expected date #> • `cdm_release_date` is logical but expected date #> • `cdm_version` is numeric but expected character #> • `vocabulary_version` is logical but expected character #> Warning: ! 3 column in concept do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 1 column in vocabulary do not match expected column type: #> • `vocabulary_concept_id` is numeric but expected integer #> Warning: ! 5 column in concept_relationship do not match expected column type: #> • `concept_id_1` is numeric but expected integer #> • `concept_id_2` is numeric but expected integer #> • `valid_start_date` is logical but expected date #> • `valid_end_date` is logical but expected date #> • `invalid_reason` is logical but expected character #> Warning: ! 2 column in concept_synonym do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `language_concept_id` is numeric but expected integer #> Warning: ! 4 column in concept_ancestor do not match expected column type: #> • `ancestor_concept_id` is numeric but expected integer #> • `descendant_concept_id` is numeric but expected integer #> • `min_levels_of_separation` is numeric but expected integer #> • `max_levels_of_separation` is numeric but expected integer #> Warning: ! 9 column in drug_strength do not match expected column type: #> • `drug_concept_id` is numeric but expected integer #> • `ingredient_concept_id` is numeric but expected integer #> • `amount_unit_concept_id` is numeric but expected integer #> • `numerator_unit_concept_id` is numeric but expected integer #> • `denominator_value` is logical but expected numeric #> • `denominator_unit_concept_id` is numeric but expected integer #> • `box_size` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> Warning: ! 2 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 1 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer cdm$cohort |> exitAtFirstDate(dateColumns = c(\"date_1\", \"date_2\")) #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> # Source: table [4 x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date exit_reason #> #> 1 1 4 2000-12-09 2002-12-09 date_1; dat… #> 2 1 1 2000-06-03 2001-08-01 date_1; dat… #> 3 1 2 2000-01-01 2001-01-01 date_1 #> 4 1 3 2015-01-15 2015-01-15 date_1 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort end date to the last of a set of column dates — exitAtLastDate","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"exitAtLastDate() resets cohort end date based set specified column dates. last date occurs chosen.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"","code":"exitAtLastDate( cohort, dateColumns, cohortId = NULL, returnReason = TRUE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"cohort cohort table cdm reference. dateColumns Character vector indicating date columns cohort table consider. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. returnReason TRUE return column indicating dateColumns used. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtLastDate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort end date to the last of a set of column dates — exitAtLastDate","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(tables = list( \"cohort\" = dplyr::tibble( cohort_definition_id = 1, subject_id = c(1, 2, 3, 4), cohort_start_date = as.Date(c(\"2000-06-03\", \"2000-01-01\", \"2015-01-15\", \"2000-12-09\")), cohort_end_date = as.Date(c(\"2001-09-01\", \"2001-01-12\", \"2015-02-15\", \"2002-12-09\")), date_1 = as.Date(c(\"2001-08-01\", \"2001-01-01\", \"2015-01-15\", \"2002-12-09\")), date_2 = as.Date(c(\"2001-08-01\", NA, \"2015-04-15\", \"2002-12-09\")) ) )) #> Warning: ! 7 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `gender_concept_id` is numeric but expected integer #> • `year_of_birth` is numeric but expected integer #> • `month_of_birth` is numeric but expected integer #> • `race_concept_id` is numeric but expected integer #> • `ethnicity_concept_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 2 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> • `period_type_concept_id` is numeric but expected integer #> Warning: ! 9 column in cdm_source do not match expected column type: #> • `cdm_source_abbreviation` is logical but expected character #> • `cdm_holder` is logical but expected character #> • `source_description` is logical but expected character #> • `source_documentation_reference` is logical but expected character #> • `cdm_etl_reference` is logical but expected character #> • `source_release_date` is logical but expected date #> • `cdm_release_date` is logical but expected date #> • `cdm_version` is numeric but expected character #> • `vocabulary_version` is logical but expected character #> Warning: ! 3 column in concept do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 1 column in vocabulary do not match expected column type: #> • `vocabulary_concept_id` is numeric but expected integer #> Warning: ! 5 column in concept_relationship do not match expected column type: #> • `concept_id_1` is numeric but expected integer #> • `concept_id_2` is numeric but expected integer #> • `valid_start_date` is logical but expected date #> • `valid_end_date` is logical but expected date #> • `invalid_reason` is logical but expected character #> Warning: ! 2 column in concept_synonym do not match expected column type: #> • `concept_id` is numeric but expected integer #> • `language_concept_id` is numeric but expected integer #> Warning: ! 4 column in concept_ancestor do not match expected column type: #> • `ancestor_concept_id` is numeric but expected integer #> • `descendant_concept_id` is numeric but expected integer #> • `min_levels_of_separation` is numeric but expected integer #> • `max_levels_of_separation` is numeric but expected integer #> Warning: ! 9 column in drug_strength do not match expected column type: #> • `drug_concept_id` is numeric but expected integer #> • `ingredient_concept_id` is numeric but expected integer #> • `amount_unit_concept_id` is numeric but expected integer #> • `numerator_unit_concept_id` is numeric but expected integer #> • `denominator_value` is logical but expected numeric #> • `denominator_unit_concept_id` is numeric but expected integer #> • `box_size` is numeric but expected integer #> • `valid_start_date` is character but expected date #> • `valid_end_date` is character but expected date #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> Warning: ! 2 column in person do not match expected column type: #> • `person_id` is numeric but expected integer #> • `location_id` is numeric but expected integer #> Warning: ! 1 column in observation_period do not match expected column type: #> • `person_id` is numeric but expected integer #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer cdm$cohort |> exitAtLastDate(dateColumns = c(\"date_1\", \"date_2\")) #> Warning: ! 2 column in cohort do not match expected column type: #> • `cohort_definition_id` is numeric but expected integer #> • `subject_id` is numeric but expected integer #> # Source: table [4 x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date exit_reason #> #> 1 1 4 2000-12-09 2002-12-09 date_2; dat… #> 2 1 2 2000-01-01 2001-01-01 date_1 #> 3 1 3 2015-01-15 2015-04-15 date_2 #> 4 1 1 2000-06-03 2001-08-01 date_2; dat… # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":null,"dir":"Reference","previous_headings":"","what":"Set cohort end date to end of observation — exitAtObservationEnd","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"exitAtObservationEnd() resets cohort end date based set specified column dates. last date occurs chosen. functions changes cohort end date end date observation period corresponding cohort entry. case generates overlapping records cohort, overlapping entries merged.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"","code":"exitAtObservationEnd(cohort, cohortId = NULL, name = tableName(cohort))"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts modify (cohort_definition_id cohort_name). NULL, cohorts used; otherwise, specified cohorts modified, rest remain unchanged. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/exitAtObservationEnd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set cohort end date to end of observation — exitAtObservationEnd","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor() cdm$cohort1 |> exitAtObservationEnd() #> # Source: table [6 x 4] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> #> 1 1 9 2012-01-18 2012-06-30 #> 2 1 6 2003-10-31 2005-11-04 #> 3 1 2 1964-09-18 1968-04-03 #> 4 1 4 1998-06-22 2013-05-12 #> 5 1 5 2007-10-19 2014-09-25 #> 6 1 3 1976-11-28 2000-04-25 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/gapDoc.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for consistent documentation of gap. — gapDoc","title":"Helper for consistent documentation of gap. — gapDoc","text":"Helper consistent documentation gap.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/gapDoc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for consistent documentation of gap. — gapDoc","text":"gap Number days two subsequent cohort entries merged single cohort record.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"intersectCohorts() combines different cohort entries, records overlap combined kept. Cohort entries individual cohorts.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"","code":"intersectCohorts( cohort, cohortId = NULL, gap = 0, returnNonOverlappingCohorts = FALSE, keepOriginalCohorts = FALSE, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts include (cohort_definition_id cohort_name). Cohorts included removed cohort set. gap Number days two subsequent cohort entries merged single cohort record. returnNonOverlappingCohorts Whether generated cohorts mutually exclusive . keepOriginalCohorts TRUE original cohorts newly created intersection cohort returned. FALSE new cohort returned. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/intersectCohorts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a combination cohort set between the intersection of different cohorts. — intersectCohorts","text":"","code":"# \\donttest{ library(CohortConstructor) cdm <- mockCohortConstructor(nPerson = 100) cdm$cohort3 <- intersectCohorts( cohort = cdm$cohort2, name = \"cohort3\", ) settings(cdm$cohort3) #> # A tibble: 1 × 5 #> cohort_definition_id cohort_name gap cohort_1 cohort_2 #> #> 1 1 cohort_1_cohort_2 0 1 1 # }"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a new cohort matched cohort — matchCohorts","title":"Generate a new cohort matched cohort — matchCohorts","text":"matchCohorts() generate new cohort matched individuals existing cohort. Individuals can matched based year birth sex.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a new cohort matched cohort — matchCohorts","text":"","code":"matchCohorts( cohort, cohortId = NULL, matchSex = TRUE, matchYearOfBirth = TRUE, ratio = 1, name = tableName(cohort) )"},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a new cohort matched cohort — matchCohorts","text":"cohort cohort table cdm reference. cohortId Vector identifying cohorts include (cohort_definition_id cohort_name). Cohorts included removed cohort set. matchSex Whether match sex. matchYearOfBirth Whether match year birth. ratio Number allowed matches per individual target cohort. name Name new cohort table created cdm object.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a new cohort matched cohort — matchCohorts","text":"cohort table.","code":""},{"path":"https://ohdsi.github.io/CohortConstructor/reference/matchCohorts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a new cohort matched cohort — matchCohorts","text":"","code":"# \\donttest{ library(CohortConstructor) library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union cdm <- mockCohortConstructor(nPerson = 200) cdm$new_matched_cohort <- cdm$cohort2 |> matchCohorts( name = \"new_matched_cohort\", cohortId = 2, matchSex = TRUE, matchYearOfBirth = TRUE, ratio = 1) #> Starting matching #> Warning: Multiple records per person detected. The matchCohorts() function is designed #> to operate under the assumption that there is only one record per person within #> each cohort. If this assumption is not met, each record will be treated #> independently. As a result, the same individual may be matched multiple times, #> leading to inconsistent and potentially misleading results. #> ℹ Creating copy of target cohort. #> • 1 cohort to be matched. #> ℹ Creating controls cohorts. #> ℹ Excluding cases from controls #> • Matching by gender_concept_id and year_of_birth #> • Removing controls that were not in observation at index date #> • Excluding target records whose pair is not in observation #> • Adjusting ratio #> Binding both cohorts #> ✔ Done cdm$new_matched_cohort #> # Source: table [?? x 5] #> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1/:memory:] #> cohort_definition_id subject_id cohort_start_date cohort_end_date cluster_id #>
The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when restricted to only include individuals who intersect with the GI bleed cohort at least once before the cohort start date. 2,296 individuals and 8,765 records were excluded.
The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals with no intersects with the GI bleed cohort before the cohort start date. 36 individuals and 600 records were excluded.
The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who have had events of GI bleeding at least once before the cohort start date. 2,296 individuals and 8,765 records were excluded.
The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who have not had events of GI bleeding before the cohort start date. 36 individuals and 600 records were excluded.
The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who intersect with the GI bleeding clinical table at least once before the cohort start date. 2,296 individuals and 8,765 records were excluded.
The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who have no intersects with the GI bleeding clinical table before the cohort start date. 36 individuals and 600 records were excluded.
The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who died after the cohort start date. None of the individuals in cohort 1 died and therefore they are all excluded from this cohort.
The flow chart above illustrates the changes to cohort 1 when restricted to only include individuals who did not die after the cohort start date. None of the individuals in cohort 1 died and therefore no one was excluded.
cdm$medications %>% filter(subject_id == 1) #> # Source: SQL [4 x 4] -#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmpQT59lg/file1dcd4337393f.duckdb] +#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmplazfDY/file1e0b2d32d912.duckdb] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> -#> 1 1 1 1976-10-20 1976-11-03 +#> 1 1 1 1980-03-15 1980-03-29 #> 2 1 1 1971-01-04 1971-01-18 #> 3 1 1 1982-09-11 1982-10-02 -#> 4 1 1 1980-03-15 1980-03-29 +#> 4 1 1 1976-10-20 1976-11-03 cdm$medications_collapsed %>% filter(subject_id == 1) #> # Source: SQL [3 x 4] -#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmpQT59lg/file1dcd4337393f.duckdb] +#> # Database: DuckDB v1.1.0 [unknown@Linux 6.8.0-1014-azure:R 4.4.1//tmp/RtmplazfDY/file1e0b2d32d912.duckdb] #> cohort_definition_id subject_id cohort_start_date cohort_end_date #> <int> <int> <date> <date> #> 1 1 1 1976-10-20 1976-11-03 @@ -147,14 +147,14 @@ summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed) plotCohortAttrition(summary_attrition, cohortId = 1) -The flow chart above illustrates the changes to cohort 1 (users of +The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when entries within 3 years of each other are merged. We see that collapsing the cohort has led to 1,390 fewer records. summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed) plotCohortAttrition(summary_attrition, cohortId = 2) -The flow chart above illustrates the changes to cohort 2 (users of +The flow chart above illustrates the changes to cohort 2 (users of diclofenac) when entries within 3 years of each other are merged. Since this cohort only has one record per individual the function collapseCohorts() had no impact on the final number of records. diff --git a/articles/a07_filter_cohorts.html b/articles/a07_filter_cohorts.html index fab5d0c3..d5abc93f 100644 --- a/articles/a07_filter_cohorts.html +++ b/articles/a07_filter_cohorts.html @@ -26,7 +26,7 @@ CohortConstructor - 0.3.0.900 + 0.3.1 @@ -109,7 +109,7 @@ #> # A tibble: 2 × 3 #> cohort_definition_id number_records number_subjects #> <int> <int> <int> -#> 1 1 366 100 +#> 1 1 350 100 #> 2 2 830 830
summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed) plotCohortAttrition(summary_attrition, cohortId = 1)
The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when entries within 3 years of each other are merged. We see that collapsing the cohort has led to 1,390 fewer records.
summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed) plotCohortAttrition(summary_attrition, cohortId = 2)
The flow chart above illustrates the changes to cohort 2 (users of +
The flow chart above illustrates the changes to cohort 2 (users of diclofenac) when entries within 3 years of each other are merged. Since this cohort only has one record per individual the function collapseCohorts() had no impact on the final number of records.
Burn E, Catala M, Mercade-Besora N, Alcalde-Herraiz M, Du M, Guo Y, Chen X, Lopez-Guell K, Rowlands E (2024). CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model. -R package version 0.3.0.900, https://ohdsi.github.io/CohortConstructor/. +R package version 0.3.1, https://ohdsi.github.io/CohortConstructor/.
@Manual{, title = {CohortConstructor: Build and Manipulate Study Cohorts Using a Common Data Model}, author = {Edward Burn and Marti Catala and Nuria Mercade-Besora and Marta Alcalde-Herraiz and Mike Du and Yuchen Guo and Xihang Chen and Kim Lopez-Guell and Elin Rowlands}, year = {2024}, - note = {R package version 0.3.0.900}, + note = {R package version 0.3.1}, url = {https://ohdsi.github.io/CohortConstructor/}, }
Mike Du mike.du@ndorms.ox.ac.uk (ORCID)
Yuchen Guo yuchen.guo@ndorms.ox.ac.uk (ORCID)
Xihang Chen xihang.chen@ndorms.ox.ac.uk (ORCID)
Kim Lopez kim.lopez@spc.ox.ac.uk (ORCID)
Kim Lopez-Guell kim.lopez@spc.ox.ac.uk (ORCID)
Elin Rowlands elin.rowlands@ndorms.ox.ac.uk (ORCID)