From a61cd465126543d0ac38d1e3940db233173da427 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Wed, 16 Jul 2025 12:50:15 -0700 Subject: [PATCH 01/34] Initial code for saving and loading template definitions --- DESCRIPTION | 2 +- R/Module-CohortGenerator.R | 12 +++ R/StrategusModule.R | 160 +++++++++++++++++++++++-------------- 3 files changed, 112 insertions(+), 62 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 365b112a..48ccbd2c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -52,7 +52,7 @@ Suggests: Remotes: ohdsi/Characterization, ohdsi/CohortDiagnostics, - ohdsi/CohortGenerator, + ohdsi/CohortGenerator@cohort_templates, ohdsi/CohortIncidence, ohdsi/CohortMethod, ohdsi/PatientLevelPrediction, diff --git a/R/Module-CohortGenerator.R b/R/Module-CohortGenerator.R index 7f69b9c7..f9a4975a 100644 --- a/R/Module-CohortGenerator.R +++ b/R/Module-CohortGenerator.R @@ -120,7 +120,15 @@ CohortGeneratorModule <- R6::R6Class( stop("cohortDefinitionSet is not properly defined") } + templateDefinitions <- CohortGenerator::getTemplateDefinitions(cohortDefinitionSet) + if (length(templateDefinitions) > 0) { + # Don't save templates as regular cohorts, this is handled entirely in the template definitions + cohortDefinitionSet <- cohortDefinitionSet |> + dplyr::filter(!.data$isTemplatedCohort) + } + subsetDefinitions <- CohortGenerator::getSubsetDefinitions(cohortDefinitionSet) + if (length(subsetDefinitions) > 0) { # Filter the cohort definition set to the "parent" cohorts. parentCohortDefinitionSet <- cohortDefinitionSet[!cohortDefinitionSet$isSubset, ] @@ -153,6 +161,10 @@ CohortGeneratorModule <- R6::R6Class( sharedResource["cohortSubsets"] <- list(subsetIdMapping) } + if (length(templateDefinitions)) { + sharedResource["templateDefs"] <- lapply(templateDefinitions, function(x) { x$toList() }) + } + sharedResource <- super$createSharedResourcesSpecifications( className = self$cohortDefinitionSharedResourcesClassName, sharedResourcesSpecifications = sharedResource diff --git a/R/StrategusModule.R b/R/StrategusModule.R index ce00cde5..e88d03b4 100644 --- a/R/StrategusModule.R +++ b/R/StrategusModule.R @@ -9,13 +9,13 @@ JobContext <- R6::R6Class( classname = "JobContext", public = list( - #' @field sharedResources Shared resources for execution - #' TODO: Revisit to break this into fields for cohorts, subsets, - #' negative controls, + #' @field sharedResources Shared resources for execution + #' TODO: Revisit to break this into fields for cohorts, subsets, + #' negative controls, sharedResources = list(), - #' @field settings Module settings + #' @field settings Module settings settings = list(), - #' @field moduleExecutionSettings Module execution settings + #' @field moduleExecutionSettings Module execution settings moduleExecutionSettings = list() ) ) @@ -31,30 +31,30 @@ JobContext <- R6::R6Class( StrategusModule <- R6::R6Class( classname = "StrategusModule", public = list( - #' @field moduleName The name of the module taken from the class name. - #' This is set in the constructor of the class. + #' @field moduleName The name of the module taken from the class name. + #' This is set in the constructor of the class. moduleName = "", - #' @field moduleClassName The class name that identifies - #' the module specifications in the overall analysis specification. - #' This is set in the constructor of the class. + #' @field moduleClassName The class name that identifies + #' the module specifications in the overall analysis specification. + #' This is set in the constructor of the class. moduleClassName = "", - #' @field internalModuleSpecificationClassName A constant value. - #' The base class name that identifies a module specification - #' in the analysis specification. + #' @field internalModuleSpecificationClassName A constant value. + #' The base class name that identifies a module specification + #' in the analysis specification. internalModuleSpecificationClassName = "ModuleSpecifications", - #' @field internalSharedResourcesClassName A constant value. The class name - #' that identifies the shared resources section in the overall analysis - #' specification. + #' @field internalSharedResourcesClassName A constant value. The class name + #' that identifies the shared resources section in the overall analysis + #' specification. internalSharedResourcesClassName = "SharedResources", - #' @description Initialize the module + #' @description Initialize the module initialize = function() { self$moduleName <- class(self)[[1]] self$moduleClassName <- paste0(self$moduleName, "Specifications") }, - #' @description Executes the module - #' @template connectionDetails - #' @template analysisSpecifications - #' @template executionSettings + #' @description Executes the module + #' @template connectionDetails + #' @template analysisSpecifications + #' @template executionSettings execute = function(connectionDetails, analysisSpecifications, executionSettings) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(connectionDetails, "ConnectionDetails", add = errorMessages) @@ -66,10 +66,10 @@ StrategusModule <- R6::R6Class( private$.createJobContext(analysisSpecifications, executionSettings) private$.message("EXECUTING: ", self$moduleName) }, - #' @description Create the results data model for the module - #' @template resultsConnectionDetails - #' @template resultsDatabaseSchema - #' @template tablePrefix + #' @description Create the results data model for the module + #' @template resultsConnectionDetails + #' @template resultsDatabaseSchema + #' @template tablePrefix createResultsDataModel = function(resultsConnectionDetails, resultsDatabaseSchema, tablePrefix = "") { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(resultsConnectionDetails, "ConnectionDetails", add = errorMessages) @@ -77,14 +77,14 @@ StrategusModule <- R6::R6Class( checkmate::reportAssertions(collection = errorMessages) private$.message("CREATE RESULTS DATA MODEL: ", self$moduleName) }, - #' @description Get the results data model specification for the module - #' @template tablePrefix + #' @description Get the results data model specification for the module + #' @template tablePrefix getResultsDataModelSpecification = function(tablePrefix = "") { }, - #' @description Upload the results for the module - #' @template resultsConnectionDetails - #' @template analysisSpecifications - #' @template resultsDataModelSettings + #' @description Upload the results for the module + #' @template resultsConnectionDetails + #' @template analysisSpecifications + #' @template resultsDataModelSettings uploadResults = function(resultsConnectionDetails, analysisSpecifications, resultsDataModelSettings) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(resultsConnectionDetails, "ConnectionDetails", add = errorMessages) @@ -95,10 +95,10 @@ StrategusModule <- R6::R6Class( private$.createJobContext(analysisSpecifications, resultsDataModelSettings) private$.message("UPLOAD RESULTS: ", self$moduleName) }, - #' @description Base function for creating the module settings object. - #' Each module will have its own implementation and this base class method - #' will be used to ensure the class of the specifications is set properly. - #' @template moduleSpecifications + #' @description Base function for creating the module settings object. + #' Each module will have its own implementation and this base class method + #' will be used to ensure the class of the specifications is set properly. + #' @template moduleSpecifications createModuleSpecifications = function(moduleSpecifications) { moduleSpecifications <- list( module = self$moduleName, @@ -107,32 +107,32 @@ StrategusModule <- R6::R6Class( class(moduleSpecifications) <- c(self$internalModuleSpecificationClassName, self$moduleClassName) return(moduleSpecifications) }, - #' @description Base function for creating the shared resources settings object. - #' Each module will have its own implementation if it needs to create - #' a shared resource. - #' @param className The class name of the shared resources specifications - #' @param sharedResourcesSpecifications The shared resources specifications + #' @description Base function for creating the shared resources settings object. + #' Each module will have its own implementation if it needs to create + #' a shared resource. + #' @param className The class name of the shared resources specifications + #' @param sharedResourcesSpecifications The shared resources specifications createSharedResourcesSpecifications = function(className, sharedResourcesSpecifications) { class(sharedResourcesSpecifications) <- c(className, self$internalSharedResourcesClassName) return(sharedResourcesSpecifications) }, - #' @description Base function for validating the module settings object. - #' Each module will have its own implementation and this base class method - #' will be used to ensure the module specifications are valid ahead of - #' execution - #' @template moduleSpecifications + #' @description Base function for validating the module settings object. + #' Each module will have its own implementation and this base class method + #' will be used to ensure the module specifications are valid ahead of + #' execution + #' @template moduleSpecifications validateModuleSpecifications = function(moduleSpecifications) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(moduleSpecifications, self$internalModuleSpecificationClassName) checkmate::assertClass(moduleSpecifications, self$moduleClassName) checkmate::reportAssertions(collection = errorMessages) }, - #' @description Base function for validating the shared resources - #' specification settings object. Each module will have its own - #' implementation and this base class method will be used to ensure - #' the module specifications are valid ahead of execution - #' @param className The class name of the shared resources specifications - #' @param sharedResourcesSpecifications The shared resources specifications + #' @description Base function for validating the shared resources + #' specification settings object. Each module will have its own + #' implementation and this base class method will be used to ensure + #' the module specifications are valid ahead of execution + #' @param className The class name of the shared resources specifications + #' @param sharedResourcesSpecifications The shared resources specifications validateSharedResourcesSpecifications = function(className, sharedResourcesSpecifications) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(sharedResourcesSpecifications, self$internalSharedResourcesClassName) @@ -173,15 +173,39 @@ StrategusModule <- R6::R6Class( # for the given module. private$jobContext$sharedResources <- analysisSpecifications$sharedResources private$jobContext$moduleExecutionSettings <- executionSettings - private$jobContext$moduleExecutionSettings$resultsSubFolder <- file.path(private$jobContext$moduleExecutionSettings$resultsFolder, self$moduleName) - if (!dir.exists(private$jobContext$moduleExecutionSettings$resultsSubFolder)) { - dir.create(private$jobContext$moduleExecutionSettings$resultsSubFolder, showWarnings = F, recursive = T) + private$ + jobContext$ + moduleExecutionSettings$ + resultsSubFolder <- file.path(private$ + jobContext$ + moduleExecutionSettings$ + resultsFolder, self$moduleName) + if (!dir.exists(private$ + jobContext$ + moduleExecutionSettings$ + resultsSubFolder)) { + dir.create(private$ + jobContext$ + moduleExecutionSettings$ + resultsSubFolder, showWarnings = F, recursive = T) } if (is(private$jobContext$moduleExecutionSettings, "ExecutionSettings")) { - private$jobContext$moduleExecutionSettings$workSubFolder <- file.path(private$jobContext$moduleExecutionSettings$workFolder, self$moduleName) - if (!dir.exists(private$jobContext$moduleExecutionSettings$workSubFolder)) { - dir.create(private$jobContext$moduleExecutionSettings$workSubFolder, showWarnings = F, recursive = T) + private$ + jobContext$ + moduleExecutionSettings$ + workSubFolder <- file.path(private$ + jobContext$ + moduleExecutionSettings$ + workFolder, self$moduleName) + if (!dir.exists(private$ + jobContext$ + moduleExecutionSettings$ + workSubFolder)) { + dir.create(private$ + jobContext$ + moduleExecutionSettings$ + workSubFolder, showWarnings = F, recursive = T) } } }, @@ -257,6 +281,14 @@ StrategusModule <- R6::R6Class( )) } + if (length(cohortDefinitionSharedResource$templateDefs)) { + for (tplDef in cohortDefinitionSharedResource$templateDefs) { + template <- CohortGenerator::CohortTemplateDefinition$new(tplDef) + cohortDefinitionSet <- cohortDefinitionSet |> + CohortGenerator::addCohortTemplateDefintion(template) + } + } + if (length(cohortDefinitionSharedResource$subsetDefs)) { subsetDefinitions <- lapply(cohortDefinitionSharedResource$subsetDefs, CohortGenerator::CohortSubsetDefinition$new) for (subsetDef in subsetDefinitions) { @@ -269,7 +301,6 @@ StrategusModule <- R6::R6Class( ) } } - return(cohortDefinitionSet) }, .createNegativeControlOutcomeSettingsFromJobContext = function() { @@ -279,7 +310,9 @@ StrategusModule <- R6::R6Class( className = "NegativeControlOutcomeSharedResources" ) if (!is.null(negativeControlSharedResource)) { - negativeControlOutcomes <- negativeControlSharedResource$negativeControlOutcomes$negativeControlOutcomeCohortSet + negativeControlOutcomes <- negativeControlSharedResource$ + negativeControlOutcomes$ + negativeControlOutcomeCohortSet if (length(negativeControlOutcomes) <= 0) { stop("Negative control outcome shared resource found but no negative control outcomes were provided.") } @@ -297,8 +330,12 @@ StrategusModule <- R6::R6Class( } invisible(list( cohortSet = negativeControlOutcomeCohortSet, - occurrenceType = negativeControlSharedResource$negativeControlOutcomes$occurrenceType, - detectOnDescendants = negativeControlSharedResource$negativeControlOutcomes$detectOnDescendants + occurrenceType = negativeControlSharedResource$ + negativeControlOutcomes$ + occurrenceType, + detectOnDescendants = negativeControlSharedResource$ + negativeControlOutcomes$ + detectOnDescendants )) } else { invisible(list( @@ -363,6 +400,7 @@ StrategusModule <- R6::R6Class( } return(s) } + if (is.null(names(covariateSettings))) { # List of lists modifiedCovariateSettings <- lapply(covariateSettings, .replaceProperties) From 050e13fae10c7fb768eafcac0d7f496b5acf0d6c Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Wed, 16 Jul 2025 13:52:37 -0700 Subject: [PATCH 02/34] testing changes for saving definitions --- R/Module-CohortGenerator.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/Module-CohortGenerator.R b/R/Module-CohortGenerator.R index f9a4975a..5ccb95aa 100644 --- a/R/Module-CohortGenerator.R +++ b/R/Module-CohortGenerator.R @@ -125,6 +125,10 @@ CohortGeneratorModule <- R6::R6Class( # Don't save templates as regular cohorts, this is handled entirely in the template definitions cohortDefinitionSet <- cohortDefinitionSet |> dplyr::filter(!.data$isTemplatedCohort) + + templateDefs <- lapply(templateDefinitions, function(x) { x$toList(forStrategus = TRUE) }) + sharedResource["templateDefs"] <- templateDefs + browser() } subsetDefinitions <- CohortGenerator::getSubsetDefinitions(cohortDefinitionSet) @@ -161,10 +165,6 @@ CohortGeneratorModule <- R6::R6Class( sharedResource["cohortSubsets"] <- list(subsetIdMapping) } - if (length(templateDefinitions)) { - sharedResource["templateDefs"] <- lapply(templateDefinitions, function(x) { x$toList() }) - } - sharedResource <- super$createSharedResourcesSpecifications( className = self$cohortDefinitionSharedResourcesClassName, sharedResourcesSpecifications = sharedResource From 553aec5f598d5ef6da488d3b6de71bbc988684f9 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Wed, 16 Jul 2025 14:02:06 -0700 Subject: [PATCH 03/34] debug removed --- R/Module-CohortGenerator.R | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/R/Module-CohortGenerator.R b/R/Module-CohortGenerator.R index 5ccb95aa..1fddb61d 100644 --- a/R/Module-CohortGenerator.R +++ b/R/Module-CohortGenerator.R @@ -119,7 +119,7 @@ CohortGeneratorModule <- R6::R6Class( if (!CohortGenerator::isCohortDefinitionSet(cohortDefinitionSet)) { stop("cohortDefinitionSet is not properly defined") } - + sharedResource <- list() templateDefinitions <- CohortGenerator::getTemplateDefinitions(cohortDefinitionSet) if (length(templateDefinitions) > 0) { # Don't save templates as regular cohorts, this is handled entirely in the template definitions @@ -128,7 +128,6 @@ CohortGeneratorModule <- R6::R6Class( templateDefs <- lapply(templateDefinitions, function(x) { x$toList(forStrategus = TRUE) }) sharedResource["templateDefs"] <- templateDefs - browser() } subsetDefinitions <- CohortGenerator::getSubsetDefinitions(cohortDefinitionSet) @@ -140,7 +139,7 @@ CohortGeneratorModule <- R6::R6Class( parentCohortDefinitionSet <- cohortDefinitionSet } - sharedResource <- list() + cohortDefinitionSetFiltered <- private$.listafy(parentCohortDefinitionSet) sharedResource["cohortDefinitions"] <- list(cohortDefinitionSetFiltered) From 75ed861bc2f903534349197e8ca8afb6a2747c98 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Wed, 16 Jul 2025 14:13:22 -0700 Subject: [PATCH 04/34] using lists correctly --- R/Module-CohortGenerator.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/Module-CohortGenerator.R b/R/Module-CohortGenerator.R index 1fddb61d..1a53d0ec 100644 --- a/R/Module-CohortGenerator.R +++ b/R/Module-CohortGenerator.R @@ -127,7 +127,7 @@ CohortGeneratorModule <- R6::R6Class( dplyr::filter(!.data$isTemplatedCohort) templateDefs <- lapply(templateDefinitions, function(x) { x$toList(forStrategus = TRUE) }) - sharedResource["templateDefs"] <- templateDefs + sharedResource$templateDefs <- templateDefs } subsetDefinitions <- CohortGenerator::getSubsetDefinitions(cohortDefinitionSet) @@ -141,14 +141,14 @@ CohortGeneratorModule <- R6::R6Class( cohortDefinitionSetFiltered <- private$.listafy(parentCohortDefinitionSet) - sharedResource["cohortDefinitions"] <- list(cohortDefinitionSetFiltered) + sharedResource$cohortDefinitions <- list(cohortDefinitionSetFiltered) if (length(subsetDefinitions)) { # Subset definitions subsetDefinitionsJson <- lapply(subsetDefinitions, function(x) { x$toJSON() }) - sharedResource["subsetDefs"] <- list(subsetDefinitionsJson) + sharedResource$subsetDefs <- list(subsetDefinitionsJson) # Filter to the subsets subsetCohortDefinitionSet <- cohortDefinitionSet[cohortDefinitionSet$isSubset, ] @@ -161,7 +161,7 @@ CohortGeneratorModule <- R6::R6Class( ) subsetIdMapping[[i]] <- idMapping } - sharedResource["cohortSubsets"] <- list(subsetIdMapping) + sharedResource$cohortSubsets <- list(subsetIdMapping) } sharedResource <- super$createSharedResourcesSpecifications( From c10f9dc8c77839142e20d747be5bd54c9dd57430 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Wed, 16 Jul 2025 14:48:20 -0700 Subject: [PATCH 05/34] Don't store hash names, seems to create an issue --- R/Module-CohortGenerator.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/Module-CohortGenerator.R b/R/Module-CohortGenerator.R index 1a53d0ec..1b4839f1 100644 --- a/R/Module-CohortGenerator.R +++ b/R/Module-CohortGenerator.R @@ -126,7 +126,8 @@ CohortGeneratorModule <- R6::R6Class( cohortDefinitionSet <- cohortDefinitionSet |> dplyr::filter(!.data$isTemplatedCohort) - templateDefs <- lapply(templateDefinitions, function(x) { x$toList(forStrategus = TRUE) }) + templateDefs <- lapply(templateDefinitions, function(x) { x$toList() }) + names(templateDefs) <- NULL sharedResource$templateDefs <- templateDefs } From 3a947968f340b09d1b72b0944d4ad7030ba8909a Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Wed, 16 Jul 2025 17:13:43 -0700 Subject: [PATCH 06/34] Modified storage of values in list to test odd behaviour --- R/Module-CohortGenerator.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/Module-CohortGenerator.R b/R/Module-CohortGenerator.R index 1b4839f1..64fdb7bb 100644 --- a/R/Module-CohortGenerator.R +++ b/R/Module-CohortGenerator.R @@ -142,14 +142,14 @@ CohortGeneratorModule <- R6::R6Class( cohortDefinitionSetFiltered <- private$.listafy(parentCohortDefinitionSet) - sharedResource$cohortDefinitions <- list(cohortDefinitionSetFiltered) + sharedResource$cohortDefinitions <- cohortDefinitionSetFiltered if (length(subsetDefinitions)) { # Subset definitions subsetDefinitionsJson <- lapply(subsetDefinitions, function(x) { x$toJSON() }) - sharedResource$subsetDefs <- list(subsetDefinitionsJson) + sharedResource$subsetDefs <- subsetDefinitionsJson # Filter to the subsets subsetCohortDefinitionSet <- cohortDefinitionSet[cohortDefinitionSet$isSubset, ] @@ -162,7 +162,7 @@ CohortGeneratorModule <- R6::R6Class( ) subsetIdMapping[[i]] <- idMapping } - sharedResource$cohortSubsets <- list(subsetIdMapping) + sharedResource$cohortSubsets <- subsetIdMapping } sharedResource <- super$createSharedResourcesSpecifications( From de09e6e5c2272c7a783faf1096df07aee5b86eed Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Wed, 16 Jul 2025 17:29:26 -0700 Subject: [PATCH 07/34] fixed whitespace changes --- R/StrategusModule.R | 152 ++++++++++++++++++-------------------------- 1 file changed, 61 insertions(+), 91 deletions(-) diff --git a/R/StrategusModule.R b/R/StrategusModule.R index e88d03b4..67f4713b 100644 --- a/R/StrategusModule.R +++ b/R/StrategusModule.R @@ -9,13 +9,13 @@ JobContext <- R6::R6Class( classname = "JobContext", public = list( - #' @field sharedResources Shared resources for execution - #' TODO: Revisit to break this into fields for cohorts, subsets, - #' negative controls, + #' @field sharedResources Shared resources for execution + #' TODO: Revisit to break this into fields for cohorts, subsets, + #' negative controls, sharedResources = list(), - #' @field settings Module settings + #' @field settings Module settings settings = list(), - #' @field moduleExecutionSettings Module execution settings + #' @field moduleExecutionSettings Module execution settings moduleExecutionSettings = list() ) ) @@ -31,30 +31,30 @@ JobContext <- R6::R6Class( StrategusModule <- R6::R6Class( classname = "StrategusModule", public = list( - #' @field moduleName The name of the module taken from the class name. - #' This is set in the constructor of the class. + #' @field moduleName The name of the module taken from the class name. + #' This is set in the constructor of the class. moduleName = "", - #' @field moduleClassName The class name that identifies - #' the module specifications in the overall analysis specification. - #' This is set in the constructor of the class. + #' @field moduleClassName The class name that identifies + #' the module specifications in the overall analysis specification. + #' This is set in the constructor of the class. moduleClassName = "", - #' @field internalModuleSpecificationClassName A constant value. - #' The base class name that identifies a module specification - #' in the analysis specification. + #' @field internalModuleSpecificationClassName A constant value. + #' The base class name that identifies a module specification + #' in the analysis specification. internalModuleSpecificationClassName = "ModuleSpecifications", - #' @field internalSharedResourcesClassName A constant value. The class name - #' that identifies the shared resources section in the overall analysis - #' specification. + #' @field internalSharedResourcesClassName A constant value. The class name + #' that identifies the shared resources section in the overall analysis + #' specification. internalSharedResourcesClassName = "SharedResources", - #' @description Initialize the module + #' @description Initialize the module initialize = function() { self$moduleName <- class(self)[[1]] self$moduleClassName <- paste0(self$moduleName, "Specifications") }, - #' @description Executes the module - #' @template connectionDetails - #' @template analysisSpecifications - #' @template executionSettings + #' @description Executes the module + #' @template connectionDetails + #' @template analysisSpecifications + #' @template executionSettings execute = function(connectionDetails, analysisSpecifications, executionSettings) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(connectionDetails, "ConnectionDetails", add = errorMessages) @@ -66,10 +66,10 @@ StrategusModule <- R6::R6Class( private$.createJobContext(analysisSpecifications, executionSettings) private$.message("EXECUTING: ", self$moduleName) }, - #' @description Create the results data model for the module - #' @template resultsConnectionDetails - #' @template resultsDatabaseSchema - #' @template tablePrefix + #' @description Create the results data model for the module + #' @template resultsConnectionDetails + #' @template resultsDatabaseSchema + #' @template tablePrefix createResultsDataModel = function(resultsConnectionDetails, resultsDatabaseSchema, tablePrefix = "") { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(resultsConnectionDetails, "ConnectionDetails", add = errorMessages) @@ -77,14 +77,14 @@ StrategusModule <- R6::R6Class( checkmate::reportAssertions(collection = errorMessages) private$.message("CREATE RESULTS DATA MODEL: ", self$moduleName) }, - #' @description Get the results data model specification for the module - #' @template tablePrefix + #' @description Get the results data model specification for the module + #' @template tablePrefix getResultsDataModelSpecification = function(tablePrefix = "") { }, - #' @description Upload the results for the module - #' @template resultsConnectionDetails - #' @template analysisSpecifications - #' @template resultsDataModelSettings + #' @description Upload the results for the module + #' @template resultsConnectionDetails + #' @template analysisSpecifications + #' @template resultsDataModelSettings uploadResults = function(resultsConnectionDetails, analysisSpecifications, resultsDataModelSettings) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(resultsConnectionDetails, "ConnectionDetails", add = errorMessages) @@ -95,10 +95,10 @@ StrategusModule <- R6::R6Class( private$.createJobContext(analysisSpecifications, resultsDataModelSettings) private$.message("UPLOAD RESULTS: ", self$moduleName) }, - #' @description Base function for creating the module settings object. - #' Each module will have its own implementation and this base class method - #' will be used to ensure the class of the specifications is set properly. - #' @template moduleSpecifications + #' @description Base function for creating the module settings object. + #' Each module will have its own implementation and this base class method + #' will be used to ensure the class of the specifications is set properly. + #' @template moduleSpecifications createModuleSpecifications = function(moduleSpecifications) { moduleSpecifications <- list( module = self$moduleName, @@ -107,32 +107,32 @@ StrategusModule <- R6::R6Class( class(moduleSpecifications) <- c(self$internalModuleSpecificationClassName, self$moduleClassName) return(moduleSpecifications) }, - #' @description Base function for creating the shared resources settings object. - #' Each module will have its own implementation if it needs to create - #' a shared resource. - #' @param className The class name of the shared resources specifications - #' @param sharedResourcesSpecifications The shared resources specifications + #' @description Base function for creating the shared resources settings object. + #' Each module will have its own implementation if it needs to create + #' a shared resource. + #' @param className The class name of the shared resources specifications + #' @param sharedResourcesSpecifications The shared resources specifications createSharedResourcesSpecifications = function(className, sharedResourcesSpecifications) { class(sharedResourcesSpecifications) <- c(className, self$internalSharedResourcesClassName) return(sharedResourcesSpecifications) }, - #' @description Base function for validating the module settings object. - #' Each module will have its own implementation and this base class method - #' will be used to ensure the module specifications are valid ahead of - #' execution - #' @template moduleSpecifications + #' @description Base function for validating the module settings object. + #' Each module will have its own implementation and this base class method + #' will be used to ensure the module specifications are valid ahead of + #' execution + #' @template moduleSpecifications validateModuleSpecifications = function(moduleSpecifications) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(moduleSpecifications, self$internalModuleSpecificationClassName) checkmate::assertClass(moduleSpecifications, self$moduleClassName) checkmate::reportAssertions(collection = errorMessages) }, - #' @description Base function for validating the shared resources - #' specification settings object. Each module will have its own - #' implementation and this base class method will be used to ensure - #' the module specifications are valid ahead of execution - #' @param className The class name of the shared resources specifications - #' @param sharedResourcesSpecifications The shared resources specifications + #' @description Base function for validating the shared resources + #' specification settings object. Each module will have its own + #' implementation and this base class method will be used to ensure + #' the module specifications are valid ahead of execution + #' @param className The class name of the shared resources specifications + #' @param sharedResourcesSpecifications The shared resources specifications validateSharedResourcesSpecifications = function(className, sharedResourcesSpecifications) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertClass(sharedResourcesSpecifications, self$internalSharedResourcesClassName) @@ -173,39 +173,15 @@ StrategusModule <- R6::R6Class( # for the given module. private$jobContext$sharedResources <- analysisSpecifications$sharedResources private$jobContext$moduleExecutionSettings <- executionSettings - private$ - jobContext$ - moduleExecutionSettings$ - resultsSubFolder <- file.path(private$ - jobContext$ - moduleExecutionSettings$ - resultsFolder, self$moduleName) - if (!dir.exists(private$ - jobContext$ - moduleExecutionSettings$ - resultsSubFolder)) { - dir.create(private$ - jobContext$ - moduleExecutionSettings$ - resultsSubFolder, showWarnings = F, recursive = T) + private$jobContext$moduleExecutionSettings$resultsSubFolder <- file.path(private$jobContext$moduleExecutionSettings$resultsFolder, self$moduleName) + if (!dir.exists(private$jobContext$moduleExecutionSettings$resultsSubFolder)) { + dir.create(private$jobContext$moduleExecutionSettings$resultsSubFolder, showWarnings = F, recursive = T) } if (is(private$jobContext$moduleExecutionSettings, "ExecutionSettings")) { - private$ - jobContext$ - moduleExecutionSettings$ - workSubFolder <- file.path(private$ - jobContext$ - moduleExecutionSettings$ - workFolder, self$moduleName) - if (!dir.exists(private$ - jobContext$ - moduleExecutionSettings$ - workSubFolder)) { - dir.create(private$ - jobContext$ - moduleExecutionSettings$ - workSubFolder, showWarnings = F, recursive = T) + private$jobContext$moduleExecutionSettings$workSubFolder <- file.path(private$jobContext$moduleExecutionSettings$workFolder, self$moduleName) + if (!dir.exists(private$jobContext$moduleExecutionSettings$workSubFolder)) { + dir.create(private$jobContext$moduleExecutionSettings$workSubFolder, showWarnings = F, recursive = T) } } }, @@ -301,6 +277,7 @@ StrategusModule <- R6::R6Class( ) } } + return(cohortDefinitionSet) }, .createNegativeControlOutcomeSettingsFromJobContext = function() { @@ -310,9 +287,7 @@ StrategusModule <- R6::R6Class( className = "NegativeControlOutcomeSharedResources" ) if (!is.null(negativeControlSharedResource)) { - negativeControlOutcomes <- negativeControlSharedResource$ - negativeControlOutcomes$ - negativeControlOutcomeCohortSet + negativeControlOutcomes <- negativeControlSharedResource$negativeControlOutcomes$negativeControlOutcomeCohortSet if (length(negativeControlOutcomes) <= 0) { stop("Negative control outcome shared resource found but no negative control outcomes were provided.") } @@ -330,12 +305,8 @@ StrategusModule <- R6::R6Class( } invisible(list( cohortSet = negativeControlOutcomeCohortSet, - occurrenceType = negativeControlSharedResource$ - negativeControlOutcomes$ - occurrenceType, - detectOnDescendants = negativeControlSharedResource$ - negativeControlOutcomes$ - detectOnDescendants + occurrenceType = negativeControlSharedResource$negativeControlOutcomes$occurrenceType, + detectOnDescendants = negativeControlSharedResource$negativeControlOutcomes$detectOnDescendants )) } else { invisible(list( @@ -400,7 +371,6 @@ StrategusModule <- R6::R6Class( } return(s) } - if (is.null(names(covariateSettings))) { # List of lists modifiedCovariateSettings <- lapply(covariateSettings, .replaceProperties) From c588cd9c24f319e4e3cc34e60b4ba8d3673d1d14 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Mon, 22 Sep 2025 08:24:53 -0700 Subject: [PATCH 08/34] CG branch updated to develop line --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 48ccbd2c..90575263 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -52,7 +52,7 @@ Suggests: Remotes: ohdsi/Characterization, ohdsi/CohortDiagnostics, - ohdsi/CohortGenerator@cohort_templates, + ohdsi/CohortGenerator@develop, ohdsi/CohortIncidence, ohdsi/CohortMethod, ohdsi/PatientLevelPrediction, From b25294d65dcec83657c4388eca48d4ee1148ba59 Mon Sep 17 00:00:00 2001 From: Jamie Gilbert Date: Thu, 13 Nov 2025 10:42:28 -0800 Subject: [PATCH 09/34] CG 1.0.0 --- DESCRIPTION | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f58eca28..47da3e52 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -15,7 +15,7 @@ Description: Coordinate and execute large scale analytics using OHDSI Health modules. Depends: R (>= 4.2.0), - CohortGenerator (>= 0.11.0), + CohortGenerator (>= 1.0.0), DatabaseConnector (>= 6.2.3), R6 Imports: @@ -51,7 +51,6 @@ Suggests: withr Remotes: ohdsi/CohortDiagnostics, - ohdsi/CohortGenerator@develop, ohdsi/CohortIncidence, ohdsi/CohortMethod License: Apache License 2.0 From c6e8bc4806005156564444147dd52318a517f2bf Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Fri, 14 Nov 2025 09:28:38 -0800 Subject: [PATCH 10/34] incrementalFolder parameter removed --- R/Module-CohortGenerator.R | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/R/Module-CohortGenerator.R b/R/Module-CohortGenerator.R index 64fdb7bb..e165b928 100644 --- a/R/Module-CohortGenerator.R +++ b/R/Module-CohortGenerator.R @@ -50,8 +50,7 @@ CohortGeneratorModule <- R6::R6Class( outputFolder = resultsFolder, databaseId = jobContext$moduleExecutionSettings$cdmDatabaseMetaData$databaseId, minCellCount = jobContext$moduleExecutionSettings$minCellCount, - incremental = jobContext$moduleExecutionSettings$incremental, - incrementalFolder = jobContext$moduleExecutionSettings$workSubFolder + incremental = jobContext$moduleExecutionSettings$incremental ) private$.message(paste("Results available at:", resultsFolder)) From 7eedb4d31544101ef83709043b9e8a60b7ec57ed Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Mon, 24 Nov 2025 11:35:20 -0800 Subject: [PATCH 11/34] SCC module started --- R/Module-SelfControlledCohort.R | 111 ++++++++++++++++++++++++++++++++ 1 file changed, 111 insertions(+) create mode 100644 R/Module-SelfControlledCohort.R diff --git a/R/Module-SelfControlledCohort.R b/R/Module-SelfControlledCohort.R new file mode 100644 index 00000000..0a196a3c --- /dev/null +++ b/R/Module-SelfControlledCohort.R @@ -0,0 +1,111 @@ +# SelfControlledCohort ------------- +#' @title Self-Controlled Case Series design with the \href{https://ohdsi.github.io/SelfControlledCaseSeries/}{HADES SelfControlledCaseSeries Package} +#' @export +#' @description +#' Module for performing Self-Controlled Cohort (SCC) analyses +#' against the OMOP Common Data Model. +SelfControlledCohort <- R6::R6Class( + classname = "SelfControlledCohort", + inherit = StrategusModule, + public = list( + #' @field tablePrefix The table prefix for results tables + tablePrefix = "scc_", + #' @description Initialize the module + initialize = function() { + super$initialize() + }, + #' @description Executes the SelfControlledCaseSeries package + #' @template connectionDetails + #' @template analysisSpecifications + #' @template executionSettings + execute = function(connectionDetails, analysisSpecifications, executionSettings) { + super$.validateCdmExecutionSettings(executionSettings) + super$execute(connectionDetails, analysisSpecifications, executionSettings) + + jobContext <- private$jobContext + sccsMultiThreadingSettings <- SelfControlledCaseSeries::createDefaultSccsMultiThreadingSettings(jobContext$moduleExecutionSettings$maxCores) + + # Provide hook to allow for overriding the number of threads + # used for database operations + computeThreads <- as.integer(getOption("strategus.SelfControlledCohort.threads")) + + args <- jobContext$settings + args$connectionDetails <- connectionDetails + args$cdmDatabaseSchema <- jobContext$moduleExecutionSettings$cdmDatabaseSchema + args$tempEmulationSchema <- jobContext$moduleExecutionSettings$tempEmulationSchema + args$exposureDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema + args$exposureTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable + args$outcomeDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema + args$outcomeTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable + args$nestingCohortDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema + args$nestingCohortTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable + args$customCovariateDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema + args$customCovariateTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable + args$outputFolder <- jobContext$moduleExecutionSettings$workSubFolder + args$sccsMultiThreadingSettings <- sccsMultiThreadingSettings + args$sccsAnalysesSpecifications <- SelfControlledCaseSeries::convertUntypedListToSccsAnalysesSpecifications(jobContext$settings$sccsAnalysesSpecifications) + do.call(SelfControlledCaseSeries::execute, args) + exportFolder <- jobContext$moduleExecutionSettings$resultsSubFolder + + resultsDataModel <- self$getResultsDataModelSpecification() + resultsDataModel <- resultsDataModel[file.exists(file.path(exportFolder, paste0(resultsDataModel$tableName, ".csv"))), ] + if (any(!startsWith(resultsDataModel$tableName, self$tablePrefix))) { + stop("Table names do not have required prefix") + } + CohortGenerator::writeCsv( + x = resultsDataModel, + file = file.path(exportFolder, "resultsDataModelSpecification.csv"), + warnOnFileNameCaseMismatch = FALSE + ) + + private$.message(paste("Results available at:", exportFolder)) + }, + #' @description Create the results data model for the module + #' @template resultsConnectionDetails + #' @template resultsDatabaseSchema + #' @template tablePrefix + createResultsDataModel = function(resultsConnectionDetails, resultsDatabaseSchema, tablePrefix = "") { + SelfControlledCohort::createResultsDataModel( + connectionDetails = resultsConnectionDetails, + databaseSchema = resultsDatabaseSchema, + tablePrefix = tablePrefix + ) + }, + #' @description Get the results data model specification for the module + #' @template tablePrefix + getResultsDataModelSpecification = function(tablePrefix = "") { + resultsDataModelSpecification <- SelfControlledCohort::getResultsDataModelSpecifications() + # add the prefix to the tableName column + resultsDataModelSpecification$tableName <- paste0(tablePrefix, resultsDataModelSpecification$tableName) + return(resultsDataModelSpecification) + }, + #' @description Upload the results for the module + #' @template resultsConnectionDetails + #' @template analysisSpecifications + #' @template resultsDataModelSettings + uploadResults = function(resultsConnectionDetails, analysisSpecifications, resultsDataModelSettings, ...) { + super$uploadResults(resultsConnectionDetails, analysisSpecifications, resultsDataModelSettings) + + SelfControlledCohort::uploadResults( + connectionDetails = resultsConnectionDetails, + schema = resultsDataModelSettings$resultsDatabaseSchema, + ... + ) + }, + #' @description Creates the SelfControlledCaseSeries Module Specifications + #' @param sccAnalysesSpecifications An R6 class created by SelfControlledCohort::createSccAnalysis + createModuleSpecifications = function(sccAnalysesSpecifications) { + specifications <- super$createModuleSpecifications( + moduleSpecifications = sccAnalysesSpecifications + ) + return(specifications) + }, + #' @description Validate the module specifications + #' @param moduleSpecifications The SelfControlledCaseSeries module specifications + validateModuleSpecifications = function(moduleSpecifications) { + super$validateModuleSpecifications( + moduleSpecifications = moduleSpecifications + ) + } + ) +) From 736096346559e1929f443dd7937040b9b9a48e3f Mon Sep 17 00:00:00 2001 From: Anthony Sena Date: Mon, 12 Jan 2026 16:20:03 -0500 Subject: [PATCH 12/34] Initial draft of updates to support CM v6 --- DESCRIPTION | 8 +- R/Module-CohortMethod.R | 68 +- extras/PackageMaintenance.R | 25 +- .../cdmModulesAnalysisSpecifications.json | 1129 +++++++++-------- man/CohortMethodModule.Rd | 34 +- tests/testthat/test-CohortMethodModule.R | 79 ++ tests/testthat/test-Settings.R | 21 +- 7 files changed, 774 insertions(+), 590 deletions(-) create mode 100644 tests/testthat/test-CohortMethodModule.R diff --git a/DESCRIPTION b/DESCRIPTION index 2695a82e..931e8166 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -15,7 +15,7 @@ Description: Coordinate and execute large scale analytics using OHDSI Health modules. Depends: R (>= 4.2.0), - CohortGenerator (>= 0.11.0), + CohortGenerator (>= 1.0.0), DatabaseConnector (>= 6.2.3), R6 Imports: @@ -34,7 +34,7 @@ Suggests: CirceR, CohortDiagnostics (>= 3.4.0), CohortIncidence, - CohortMethod, + CohortMethod (>= 6.0.0), Cyclops (>= 3.6.0), Eunomia, EvidenceSynthesis (>= 1.0.0), @@ -52,13 +52,13 @@ Suggests: Remotes: ohdsi/CohortDiagnostics, ohdsi/CohortIncidence, - ohdsi/CohortMethod + ohdsi/CohortMethod@version_6 License: Apache License 2.0 VignetteBuilder: knitr URL: https://ohdsi.github.io/Strategus, https://github.com/OHDSI/Strategus BugReports: https://github.com/OHDSI/Strategus/issues NeedsCompilation: no -RoxygenNote: 7.3.2 +RoxygenNote: 7.3.3 Roxygen: list(markdown = TRUE) Encoding: UTF-8 Language: en-US diff --git a/R/Module-CohortMethod.R b/R/Module-CohortMethod.R index 620288de..5af8e57d 100644 --- a/R/Module-CohortMethod.R +++ b/R/Module-CohortMethod.R @@ -36,6 +36,12 @@ CohortMethodModule <- R6::R6Class( multiThreadingSettings$fitOutcomeModelThreads <- fitOutcomeModelThreads } + # Add a check to ensure that the module specifications conform to the new + # CM v6 approach + if (is.null(jobContext$settings$cmAnalysesSpecifications)) { + stop("The CohortMethodModule specification is missing the required `cmAnalysesSpecifications` setting. Please recreate the CohortMethodModule specification and update the analysis specification.") + } + args <- jobContext$settings args$connectionDetails <- connectionDetails args$cdmDatabaseSchema <- jobContext$moduleExecutionSettings$cdmDatabaseSchema @@ -44,9 +50,11 @@ CohortMethodModule <- R6::R6Class( args$exposureTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable args$outcomeDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema args$outcomeTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable + args$nestingCohortDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema + args$nestingCohortTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable args$outputFolder <- jobContext$moduleExecutionSettings$workSubFolder args$multiThreadingSettings <- multiThreadingSettings - args$cmDiagnosticThresholds <- NULL + args$cmAnalysesSpecifications <- CohortMethod::convertUntypedListToCmAnalysesSpecifications(jobContext$settings$cmAnalysesSpecifications) do.call(CohortMethod::runCmAnalyses, args) exportFolder <- jobContext$moduleExecutionSettings$resultsSubFolder @@ -55,8 +63,7 @@ CohortMethodModule <- R6::R6Class( exportFolder = exportFolder, databaseId = jobContext$moduleExecutionSettings$cdmDatabaseMetaData$databaseId, minCellCount = jobContext$moduleExecutionSettings$minCellCount, - maxCores = jobContext$moduleExecutionSettings$maxCores, - cmDiagnosticThresholds = jobContext$settings$cmDiagnosticThresholds + maxCores = jobContext$moduleExecutionSettings$maxCores ) # TODO: Removing this to make the upload easier # unlink(file.path(exportFolder, sprintf("Results_%s.zip", jobContext$moduleExecutionSettings$cdmDatabaseMetaData$databaseId))) @@ -158,28 +165,41 @@ CohortMethodModule <- R6::R6Class( #' we can remove certain items from the full matrix. This argument should be a data frame with at least #' one of the following columns: #' - #' @param cmAnalysisList A list of objects of type `cmAnalysis` as created using - #' the `[CohortMethod::createCmAnalysis] function. - #' @param targetComparatorOutcomesList A list of objects of type `targetComparatorOutcomes` as - #' created using the [CohortMethod::createTargetComparatorOutcomes] - #' function. - #' @param analysesToExclude Analyses to exclude. See the Analyses to Exclude section for details. - #' @param refitPsForEveryOutcome Should the propensity model be fitted for every outcome (i.e. - #' after people who already had the outcome are removed)? If - #' false, a single propensity model will be fitted, and people - #' who had the outcome previously will be removed afterwards. - #' @param refitPsForEveryStudyPopulation Should the propensity model be fitted for every study population - #' definition? If false, a single propensity model will be fitted, - #' and the study population criteria will be applied afterwards. - #' @param cmDiagnosticThresholds An object of type `CmDiagnosticThresholds` as created using - #' [CohortMethod::createCmDiagnosticThresholds()]. - #' - createModuleSpecifications = function(cmAnalysisList, - targetComparatorOutcomesList, + #' @param cmAnalysesSpecifications An R6 class created by CohortMethod::createCmAnalysesSpecifications + #' @param cmAnalysisList Deprecated with CohortMethod v6 - please use the `cmAnalysesSpecifications` parameter instead. + #' @param targetComparatorOutcomesList Deprecated with CohortMethod v6 - please use the `cmAnalysesSpecifications` parameter instead. + #' @param analysesToExclude Deprecated with CohortMethod v6 - please use the `cmAnalysesSpecifications` parameter instead. + #' @param refitPsForEveryOutcome Deprecated with CohortMethod v6 - please use the `cmAnalysesSpecifications` parameter instead. + #' @param refitPsForEveryStudyPopulation Deprecated with CohortMethod v6 - please use the `cmAnalysesSpecifications` parameter instead. + #' @param cmDiagnosticThresholds Deprecated with CohortMethod v6 - please use the `cmAnalysesSpecifications` parameter instead. + createModuleSpecifications = function(cmAnalysesSpecifications, + cmAnalysisList = NULL, + targetComparatorOutcomesList = NULL, analysesToExclude = NULL, - refitPsForEveryOutcome = FALSE, - refitPsForEveryStudyPopulation = TRUE, - cmDiagnosticThresholds = CohortMethod::createCmDiagnosticThresholds()) { + refitPsForEveryOutcome = NULL, + refitPsForEveryStudyPopulation = NULL, + cmDiagnosticThresholds = NULL) { + paramDeprecatedMessage <- "`%s` is now part of the `cmAnalysesSpecifications` in CohortMethod v6. Please upgrade to CohortMethod v6 and use the `cmAnalysesSpecifications` parameter when specifying the input to this module." + if (!is.null(cmAnalysisList)) { + stop(sprintf(paramDeprecatedMessage, "cmAnalysisList")) + } + + if (!is.null(targetComparatorOutcomesList)) { + stop(sprintf(paramDeprecatedMessage, "targetComparatorOutcomesList")) + } + + if (!is.null(refitPsForEveryOutcome)) { + stop(sprintf(paramDeprecatedMessage, "refitPsForEveryOutcome")) + } + + if (!is.null(refitPsForEveryStudyPopulation)) { + stop(sprintf(paramDeprecatedMessage, "refitPsForEveryStudyPopulation")) + } + + if (!is.null(cmDiagnosticThresholds)) { + stop(sprintf(paramDeprecatedMessage, "cmDiagnosticThresholds")) + } + analysis <- list() for (name in names(formals(self$createModuleSpecifications))) { analysis[[name]] <- get(name) diff --git a/extras/PackageMaintenance.R b/extras/PackageMaintenance.R index 662920fd..ad6b327b 100644 --- a/extras/PackageMaintenance.R +++ b/extras/PackageMaintenance.R @@ -82,7 +82,7 @@ cohortDefinitionSet <- cohortDefinitionSet[, names(CohortGenerator::createEmptyC cohortDefinitionSet <- appendTreatmentPatternsCohorts(cohortDefinitionSet) subsetOperations <- list( - createDemographicSubset( + createDemographicSubsetOperator( name = "Age 18 to 64", ageMin = 18, ageMax = 64 @@ -97,7 +97,7 @@ cohortDefinitionSet <- cohortDefinitionSet |> addCohortSubsetDefinition(subsetDef, targetCohortIds = c(1,2)) -subsetOperations <- list(CohortGenerator::createLimitSubset( +subsetOperations <- list(CohortGenerator::createLimitSubsetOperator( name = 'first event with 365 prior obs', priorTime = 365, limitTo = 'firstEver' @@ -115,12 +115,12 @@ cohortDefinitionSet <- cohortDefinitionSet |> ) subsetOperations <- list( - createDemographicSubset( + createDemographicSubsetOperator( name = "Age 18 to 64", ageMin = 18, ageMax = 64 ), - CohortGenerator::createLimitSubset( + CohortGenerator::createLimitSubsetOperator( name = 'first event with 365 prior obs', priorTime = 365, limitTo = 'firstEver' @@ -279,7 +279,7 @@ getDbCmDataArgs <- CohortMethod::createGetDbCohortMethodDataArgs( covariateSettings = covarSettings ) -createStudyPopArgs <- CohortMethod::createCreateStudyPopulationArgs( +createStudyPopulationArgs <- CohortMethod::createCreateStudyPopulationArgs( minDaysAtRisk = 1, riskWindowStart = 0, startAnchor = "cohort start", @@ -302,7 +302,7 @@ cmAnalysis1 <- CohortMethod::createCmAnalysis( analysisId = 1, description = "No matching, simple outcome model", getDbCohortMethodDataArgs = getDbCmDataArgs, - createStudyPopArgs = createStudyPopArgs, + createStudyPopulationArgs = createStudyPopulationArgs, fitOutcomeModelArgs = fitOutcomeModelArgs ) @@ -310,7 +310,7 @@ cmAnalysis2 <- CohortMethod::createCmAnalysis( analysisId = 2, description = "Matching on ps and covariates, simple outcomeModel", getDbCohortMethodDataArgs = getDbCmDataArgs, - createStudyPopArgs = createStudyPopArgs, + createStudyPopulationArgs = createStudyPopulationArgs, createPsArgs = createPsArgs, matchOnPsArgs = matchOnPsArgs, computeSharedCovariateBalanceArgs = computeSharedCovBalArgs, @@ -322,20 +322,19 @@ cmAnalysisList <- list(cmAnalysis1, cmAnalysis2) analysesToExclude <- NULL -cmModuleSpecifications <- cmModuleSettingsCreator$createModuleSpecifications( +cmAnalysesSpecifications <- CohortMethod::createCmAnalysesSpecifications( cmAnalysisList = cmAnalysisList, targetComparatorOutcomesList = targetComparatorOutcomesList, analysesToExclude = analysesToExclude, refitPsForEveryOutcome = FALSE, refitPsForEveryStudyPopulation = FALSE, cmDiagnosticThresholds = CohortMethod::createCmDiagnosticThresholds( - mdrrThreshold = Inf, - easeThreshold = 0.60, # setting this higher to get passes given Eunomia limitations on neg controls - sdmThreshold = 0.1, - equipoiseThreshold = 0.2, - generalizabilitySdmThreshold = 1 # NOTE using default here + easeThreshold = 0.60 # setting this higher to get passes given Eunomia limitations on neg controls ) +) +cmModuleSpecifications <- cmModuleSettingsCreator$createModuleSpecifications( + cmAnalysesSpecifications = cmAnalysesSpecifications$toList() ) # EvidenceSythesis ------------------ diff --git a/inst/testdata/cdmModulesAnalysisSpecifications.json b/inst/testdata/cdmModulesAnalysisSpecifications.json index 2ce0e5cb..2c243068 100644 --- a/inst/testdata/cdmModulesAnalysisSpecifications.json +++ b/inst/testdata/cdmModulesAnalysisSpecifications.json @@ -59,9 +59,9 @@ } ], "subsetDefs": [ - "{\n \"name\": \"age 18 to 64\",\n \"definitionId\": 1,\n \"subsetOperators\": [\n {\n \"name\": \"Age 18 to 64\",\n \"subsetType\": \"DemographicSubsetOperator\",\n \"ageMin\": 18,\n \"ageMax\": 64\n }\n ],\n \"packageVersion\": \"0.12.0\",\n \"identifierExpression\": \"targetId * 1000 + definitionId\",\n \"operatorNameConcatString\": \", \",\n \"subsetCohortNameTemplate\": \"@baseCohortName - @subsetDefinitionName @operatorNames\"\n}", - "{\n \"name\": \"first event with 365 prior obs\",\n \"definitionId\": 2,\n \"subsetOperators\": [\n {\n \"name\": \"first event with 365 prior obs\",\n \"subsetType\": \"LimitSubsetOperator\",\n \"priorTime\": 365,\n \"followUpTime\": 0,\n \"minimumCohortDuration\": 0,\n \"limitTo\": \"firstEver\"\n }\n ],\n \"packageVersion\": \"0.12.0\",\n \"identifierExpression\": \"targetId * 1000 + definitionId\",\n \"operatorNameConcatString\": \", \",\n \"subsetCohortNameTemplate\": \"@baseCohortName - @subsetDefinitionName @operatorNames\"\n}", - "{\n \"name\": \"age 18 to 64 and first event with 365 prior obs\",\n \"definitionId\": 3,\n \"subsetOperators\": [\n {\n \"name\": \"Age 18 to 64\",\n \"subsetType\": \"DemographicSubsetOperator\",\n \"ageMin\": 18,\n \"ageMax\": 64\n },\n {\n \"name\": \"first event with 365 prior obs\",\n \"subsetType\": \"LimitSubsetOperator\",\n \"priorTime\": 365,\n \"followUpTime\": 0,\n \"minimumCohortDuration\": 0,\n \"limitTo\": \"firstEver\"\n }\n ],\n \"packageVersion\": \"0.12.0\",\n \"identifierExpression\": \"targetId * 1000 + definitionId\",\n \"operatorNameConcatString\": \", \",\n \"subsetCohortNameTemplate\": \"@baseCohortName - @subsetDefinitionName @operatorNames\"\n}" + "{\n \"name\": \"age 18 to 64\",\n \"definitionId\": 1,\n \"subsetOperators\": [\n {\n \"name\": \"Age 18 to 64\",\n \"subsetType\": \"DemographicSubsetOperator\",\n \"ageMin\": 18,\n \"ageMax\": 64\n }\n ],\n \"packageVersion\": \"1.0.1\",\n \"identifierExpression\": \"targetId * 1000 + definitionId\",\n \"operatorNameConcatString\": \", \",\n \"subsetCohortNameTemplate\": \"@baseCohortName - @subsetDefinitionName\"\n}", + "{\n \"name\": \"first event with 365 prior obs\",\n \"definitionId\": 2,\n \"subsetOperators\": [\n {\n \"name\": \"first event with 365 prior obs\",\n \"subsetType\": \"LimitSubsetOperator\",\n \"priorTime\": 365,\n \"followUpTime\": 0,\n \"minimumCohortDuration\": 0,\n \"limitTo\": \"firstEver\"\n }\n ],\n \"packageVersion\": \"1.0.1\",\n \"identifierExpression\": \"targetId * 1000 + definitionId\",\n \"operatorNameConcatString\": \", \",\n \"subsetCohortNameTemplate\": \"@baseCohortName - @subsetDefinitionName\"\n}", + "{\n \"name\": \"age 18 to 64 and first event with 365 prior obs\",\n \"definitionId\": 3,\n \"subsetOperators\": [\n {\n \"name\": \"Age 18 to 64\",\n \"subsetType\": \"DemographicSubsetOperator\",\n \"ageMin\": 18,\n \"ageMax\": 64\n },\n {\n \"name\": \"first event with 365 prior obs\",\n \"subsetType\": \"LimitSubsetOperator\",\n \"priorTime\": 365,\n \"followUpTime\": 0,\n \"minimumCohortDuration\": 0,\n \"limitTo\": \"firstEver\"\n }\n ],\n \"packageVersion\": \"1.0.1\",\n \"identifierExpression\": \"targetId * 1000 + definitionId\",\n \"operatorNameConcatString\": \", \",\n \"subsetCohortNameTemplate\": \"@baseCohortName - @subsetDefinitionName\"\n}" ], "cohortSubsets": [ { @@ -535,32 +535,32 @@ }, { "cohortId": 1001, - "cohortName": "Celecoxib - age 18 to 64 Age 18 to 64", + "cohortName": "Celecoxib - age 18 to 64", "type": "event" }, { "cohortId": 2001, - "cohortName": "Diclofenac - age 18 to 64 Age 18 to 64", + "cohortName": "Diclofenac - age 18 to 64", "type": "event" }, { "cohortId": 1002, - "cohortName": "Celecoxib - first event with 365 prior obs first event with 365 prior obs", + "cohortName": "Celecoxib - first event with 365 prior obs", "type": "event" }, { "cohortId": 2002, - "cohortName": "Diclofenac - first event with 365 prior obs first event with 365 prior obs", + "cohortName": "Diclofenac - first event with 365 prior obs", "type": "event" }, { "cohortId": 1003, - "cohortName": "Celecoxib - age 18 to 64 and first event with 365 prior obs Age 18 to 64, first event with 365 prior obs", + "cohortName": "Celecoxib - age 18 to 64 and first event with 365 prior obs", "type": "event" }, { "cohortId": 2003, - "cohortName": "Diclofenac - age 18 to 64 and first event with 365 prior obs Age 18 to 64, first event with 365 prior obs", + "cohortName": "Diclofenac - age 18 to 64 and first event with 365 prior obs", "type": "event" }, { @@ -590,528 +590,611 @@ { "module": "CohortMethodModule", "settings": { - "cmAnalysisList": [ - { - "analysisId": 1, - "description": "No matching, simple outcome model", - "getDbCohortMethodDataArgs": { - "studyStartDate": "", - "studyEndDate": "", - "firstExposureOnly": true, - "removeDuplicateSubjects": "remove all", - "restrictToCommonPeriod": false, - "washoutPeriod": 183, - "maxCohortSize": 100000, - "covariateSettings": { - "temporal": false, - "temporalSequence": false, - "DemographicsGender": true, - "DemographicsAgeGroup": true, - "DemographicsRace": true, - "DemographicsEthnicity": true, - "DemographicsIndexYear": true, - "DemographicsIndexMonth": true, - "ConditionGroupEraLongTerm": true, - "ConditionGroupEraShortTerm": true, - "DrugGroupEraLongTerm": true, - 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"attr_class": "args" + "description": "No matching, simple outcome model", + "analysisId": 1 }, - "attr_class": "cmAnalysis" - } - ], - "targetComparatorOutcomesList": [ - { - "targetId": 1002, - "comparatorId": 2002, - "outcomes": [ - { - "outcomeId": 10037, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10038, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10039, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10040, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10041, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10042, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10043, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10044, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10046, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10047, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10048, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10049, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 3, - "outcomeOfInterest": true, - "trueEffectSize": null, - "attr_class": "outcome" - } - ], - "excludedCovariateConceptIds": [1118084, 1124300], - "attr_class": "targetComparatorOutcomes" - }, - { - "targetId": 1003, - "comparatorId": 2003, - "outcomes": [ - { - "outcomeId": 10037, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10038, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10039, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10040, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10041, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" - }, - { - "outcomeId": 10042, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" + { + "fitOutcomeModelArgs": { + "control": { + "maxIterations": 1000, + "tolerance": 2e-07, + "convergenceType": "gradient", + "autoSearch": true, + "fold": 10, + "lowerLimit": 0.01, + "upperLimit": 20, + "gridSteps": 10, + "minCVData": 100, + "cvRepetitions": 10, + "noiseLevel": "quiet", + "threads": 1, + "seed": 1, + "resetCoefficients": true, + "startingVariance": 0.01, + "useKKTSwindle": false, + "tuneSwindle": 10, + "selectorType": "auto", + "initialBound": 2, + "maxBoundCount": 5, + "algorithm": "ccd", + "doItAll": true, + "syncCV": false + }, + "prior": { + "priorType": "laplace", + "variance": 1, + "exclude": null, + "graph": null, + "neighborhood": null, + "useCrossValidation": true, + "forceIntercept": false + }, + "profileBounds": [-2.3026, 2.3026], + "profileGrid": null, + "includeCovariateIds": null, + "excludeCovariateIds": null, + "interactionCovariateIds": null, + "bootstrapReplicates": 200, + "bootstrapCi": false, + "inversePtWeighting": false, + "useCovariates": false, + "stratified": false, + "modelType": "cox" }, - { - "outcomeId": 10043, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" + "computeCovariateBalanceArgs": { + "alpha": 0.05, + "threshold": 0.1, + "covariateFilter": { + "label": ["Age group", "Gender: female", "Race", "Ethnicity", "Medical history: General", "Medical history: Cardiovascular disease", "Medical history: Neoplasms", "Medication use", "Charlson comorbidity index", "CHADS2Vasc", "DCSI"], + "analysisId": [3, 1, 4, 5, 210, 210, 210, 410, 901, 904, 902], + "covariateIds": [null, "8532001", null, null, "4006969210,438409210,4212540210,255573210,201606210,4182210210,440383210,201820210,318800210,192671210,439727210,432867210,316866210,4104000210,433736210,80180210,255848210,140168210,4030518210,80809210,435783210,4279309210,81893210,81902210,197494210,4134440210", "313217210,381591210,317576210,321588210,316139210,4185932210,321052210,440417210,444247210", "4044013210,432571210,40481902210,443392210,4112853210,4180790210,443388210,197508210,200962210", "21601782410,21602796410,21604686410,21604389410,21603932410,21601387410,21602028410,21600960410,21601664410,21601744410,21601461410,21600046410,21603248410,21600712410,21603890410,21601853410,21604254410,21604489410,21604752410", "1901", "1904", "1902"], + "attr_row.names": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + }, + "maxCohortSize": 250000, + "subgroupCovariateId": null }, - { - "outcomeId": 10044, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" + "computeSharedCovariateBalanceArgs": { + "alpha": 0.05, + "threshold": 0.1, + "covariateFilter": null, + "maxCohortSize": 250000, + "subgroupCovariateId": null }, - { - "outcomeId": 10046, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" + "stratifyByPsArgs": null, + "matchOnPsArgs": { + "matchCovariateIds": null, + "matchColumns": null, + "allowReverseMatch": false, + "maxRatio": 1, + "caliperScale": "standardized logit", + "caliper": 0.2 }, - { - "outcomeId": 10047, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" + "truncateIptwArgs": null, + "trimByPsArgs": null, + "createPsArgs": { + "estimator": "att", + "control": { + "maxIterations": 1000, + "tolerance": 1e-06, + "convergenceType": "gradient", + "autoSearch": true, + "fold": 10, + "lowerLimit": 0.01, + "upperLimit": 20, + "gridSteps": 10, + "minCVData": 100, + "cvRepetitions": 1, + "noiseLevel": "silent", + "threads": 1, + "seed": null, + "resetCoefficients": false, + "startingVariance": -1, + "useKKTSwindle": false, + "tuneSwindle": 10, + "selectorType": "auto", + "initialBound": 2, + "maxBoundCount": 5, + "algorithm": "ccd", + "doItAll": true, + "syncCV": false + }, + "prior": { + "priorType": "laplace", + "variance": 1, + "exclude": 0, + "graph": null, + "neighborhood": null, + "useCrossValidation": true, + "forceIntercept": false + }, + "stopOnError": false, + "errorOnHighCorrelation": true, + "maxCohortSizeForFitting": 250000, + "includeCovariateIds": null, + "excludeCovariateIds": null }, - { - "outcomeId": 10048, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" + "createStudyPopulationArgs": { + "censorAtNewRiskWindow": false, + "endAnchor": "cohort end", + "riskWindowEnd": 365, + "startAnchor": "cohort start", + "riskWindowStart": 0, + "maxDaysAtRisk": 99999, + "minDaysAtRisk": 1, + "priorOutcomeLookback": 99999, + "removeSubjectsWithPriorOutcome": true }, - { - "outcomeId": 10049, - "outcomeOfInterest": false, - "trueEffectSize": 1, - "priorOutcomeLookback": 5, - "attr_class": "outcome" + "getDbCohortMethodDataArgs": { + "covariateSettings": { + "temporal": false, + "temporalSequence": false, + "DemographicsGender": true, + "DemographicsAgeGroup": true, + "DemographicsRace": true, + "DemographicsEthnicity": true, + "DemographicsIndexYear": true, + "DemographicsIndexMonth": true, + "ConditionGroupEraLongTerm": true, + "ConditionGroupEraShortTerm": true, + "DrugGroupEraLongTerm": true, + "DrugGroupEraShortTerm": true, + "DrugGroupEraOverlapping": true, + "ProcedureOccurrenceLongTerm": true, + "ProcedureOccurrenceShortTerm": true, + "DeviceExposureLongTerm": true, + "DeviceExposureShortTerm": true, + "MeasurementLongTerm": true, + "MeasurementShortTerm": true, + "MeasurementRangeGroupLongTerm": true, + "MeasurementRangeGroupShortTerm": true, + "MeasurementValueAsConceptLongTerm": true, + "MeasurementValueAsConceptShortTerm": true, + "ObservationLongTerm": true, + "ObservationShortTerm": true, + "ObservationValueAsConceptLongTerm": true, + "ObservationValueAsConceptShortTerm": true, + "CharlsonIndex": true, + "Dcsi": true, + "Chads2": true, + "Chads2Vasc": true, + "includedCovariateConceptIds": [], + "includedCovariateIds": [], + "addDescendantsToInclude": false, + "excludedCovariateConceptIds": [], + "addDescendantsToExclude": true, + "shortTermStartDays": -30, + "mediumTermStartDays": -180, + "endDays": 0, + "longTermStartDays": -365, + "attr_class": "covariateSettings", + "attr_fun": "getDbDefaultCovariateData" + }, + "maxCohortSize": 100000, + "nestingCohortId": null, + "washoutPeriod": 183, + "restrictToCommonPeriod": true, + "removeDuplicateSubjects": "remove all", + "firstExposureOnly": true, + "studyEndDate": "", + "studyStartDate": "" }, - { - "outcomeId": 3, - "outcomeOfInterest": true, - "trueEffectSize": null, - "attr_class": "outcome" - } - ], - "excludedCovariateConceptIds": [1118084, 1124300], - "attr_class": "targetComparatorOutcomes" - } - ], - "refitPsForEveryOutcome": false, - "refitPsForEveryStudyPopulation": false, - "cmDiagnosticThresholds": { - "mdrrThreshold": "Inf", - "easeThreshold": 0.6, - "sdmThreshold": 0.1, - "equipoiseThreshold": 0.2, - "generalizabilitySdmThreshold": 1, - "attr_class": "CmDiagnosticThresholds" + "description": "Matching on ps and covariates, simple outcomeModel", + "analysisId": 2 + } + ] } }, "attr_class": ["ModuleSpecifications", "CohortMethodModuleSpecifications"] @@ -1647,7 +1730,7 @@ "modelType": "logistic", "addIntercept": true, "useControl": true, - "seed": 93739120, + "seed": 51154410, "name": "Lasso Logistic Regression", "threads": -1, "tolerance": 2e-06, @@ -1662,7 +1745,7 @@ "splitSettings": { "test": 0.25, "train": 0.75, - "seed": 95791, + "seed": 88450, "nfold": 3, "attr_class": "splitSettings", "attr_fun": "randomSplitter" @@ -1670,7 +1753,7 @@ "executeSettings": { "runSplitData": true, "runSampleData": false, - "runfeatureEngineering": false, + "runFeatureEngineering": false, "runPreprocessData": true, "runModelDevelopment": true, "runCovariateSummary": true, @@ -1789,7 +1872,7 @@ "modelType": "logistic", "addIntercept": true, "useControl": true, - "seed": 46144016, + "seed": 96537540, "name": "Lasso Logistic Regression", "threads": -1, "tolerance": 2e-06, @@ -1804,7 +1887,7 @@ "splitSettings": { "test": 0.25, "train": 0.75, - "seed": 47715, + "seed": 49231, "nfold": 3, "attr_class": "splitSettings", "attr_fun": "randomSplitter" @@ -1812,7 +1895,7 @@ "executeSettings": { "runSplitData": true, "runSampleData": false, - "runfeatureEngineering": false, + "runFeatureEngineering": false, "runPreprocessData": true, "runModelDevelopment": true, "runCovariateSummary": true, diff --git a/man/CohortMethodModule.Rd b/man/CohortMethodModule.Rd index 6b3b7c7c..07493a8b 100644 --- a/man/CohortMethodModule.Rd +++ b/man/CohortMethodModule.Rd @@ -159,38 +159,32 @@ by \code{\link[=createEmptyAnalysisSpecificiations]{createEmptyAnalysisSpecifici Creates the CohortMethod Module Specifications \subsection{Usage}{ \if{html}{\out{
}}\preformatted{CohortMethodModule$createModuleSpecifications( - cmAnalysisList, - targetComparatorOutcomesList, + cmAnalysesSpecifications, + cmAnalysisList = NULL, + targetComparatorOutcomesList = NULL, analysesToExclude = NULL, - refitPsForEveryOutcome = FALSE, - refitPsForEveryStudyPopulation = TRUE, - cmDiagnosticThresholds = CohortMethod::createCmDiagnosticThresholds() + refitPsForEveryOutcome = NULL, + refitPsForEveryStudyPopulation = NULL, + cmDiagnosticThresholds = NULL )}\if{html}{\out{
}} } \subsection{Arguments}{ \if{html}{\out{
}} \describe{ -\item{\code{cmAnalysisList}}{A list of objects of type \code{cmAnalysis} as created using -the `\link[CohortMethod:createCmAnalysis]{CohortMethod::createCmAnalysis} function.} +\item{\code{cmAnalysesSpecifications}}{An R6 class created by CohortMethod::createCmAnalysesSpecifications} -\item{\code{targetComparatorOutcomesList}}{A list of objects of type \code{targetComparatorOutcomes} as -created using the \link[CohortMethod:createTargetComparatorOutcomes]{CohortMethod::createTargetComparatorOutcomes} -function.} +\item{\code{cmAnalysisList}}{Deprecated with CohortMethod v6 - please use the \code{cmAnalysesSpecifications} parameter instead.} -\item{\code{analysesToExclude}}{Analyses to exclude. See the Analyses to Exclude section for details.} +\item{\code{targetComparatorOutcomesList}}{Deprecated with CohortMethod v6 - please use the \code{cmAnalysesSpecifications} parameter instead.} -\item{\code{refitPsForEveryOutcome}}{Should the propensity model be fitted for every outcome (i.e. -after people who already had the outcome are removed)? If -false, a single propensity model will be fitted, and people -who had the outcome previously will be removed afterwards.} +\item{\code{analysesToExclude}}{Deprecated with CohortMethod v6 - please use the \code{cmAnalysesSpecifications} parameter instead.} -\item{\code{refitPsForEveryStudyPopulation}}{Should the propensity model be fitted for every study population -definition? If false, a single propensity model will be fitted, -and the study population criteria will be applied afterwards.} +\item{\code{refitPsForEveryOutcome}}{Deprecated with CohortMethod v6 - please use the \code{cmAnalysesSpecifications} parameter instead.} -\item{\code{cmDiagnosticThresholds}}{An object of type \code{CmDiagnosticThresholds} as created using -\code{\link[CohortMethod:createCmDiagnosticThresholds]{CohortMethod::createCmDiagnosticThresholds()}}.} +\item{\code{refitPsForEveryStudyPopulation}}{Deprecated with CohortMethod v6 - please use the \code{cmAnalysesSpecifications} parameter instead.} + +\item{\code{cmDiagnosticThresholds}}{Deprecated with CohortMethod v6 - please use the \code{cmAnalysesSpecifications} parameter instead.} } \if{html}{\out{
}} } diff --git a/tests/testthat/test-CohortMethodModule.R b/tests/testthat/test-CohortMethodModule.R new file mode 100644 index 00000000..393b469d --- /dev/null +++ b/tests/testthat/test-CohortMethodModule.R @@ -0,0 +1,79 @@ +library(testthat) +library(Strategus) + +test_that("CohortMethodModule - create module settings compatible for CM v6", { + cmModule <- CohortMethodModule$new() + expect_error( + cmModule$createModuleSpecifications( + cmAnalysisList = list() + ) + ) + + expect_error( + cmModule$createModuleSpecifications( + targetComparatorOutcomesList = list() + ) + ) + + expect_error( + cmModule$createModuleSpecifications( + refitPsForEveryOutcome = FALSE + ) + ) + + expect_error( + cmModule$createModuleSpecifications( + refitPsForEveryStudyPopulation = TRUE + ) + ) + + expect_error( + cmModule$createModuleSpecifications( + cmDiagnosticThresholds = list() + ) + ) +}) + +test_that("Verify CM v5 module specs fails to execute with error", { + studyRootFolder <- file.path(tempDir, "cmV5SettingsTest") + workFolder <- file.path(studyRootFolder, "work_folder") + resultsFolder <- file.path(studyRootFolder, "results_folder") + if (!dir.exists(studyRootFolder)) { + dir.create(studyRootFolder, recursive = TRUE) + } + + withr::defer( + { + unlink(studyRootFolder, recursive = TRUE, force = TRUE) + }, + testthat::teardown_env() + ) + + cmModule <- CohortMethodModule$new() + cmModuleSpecifications <- cmModule$createModuleSpecifications( + cmAnalysesSpecifications = list() + ) + # Hack to remove the CM v6 module setting + cmModuleSpecifications$settings$cmAnalysesSpecifications <- NULL + # Hack to add the CM v5 module settings + cmModuleSpecifications$settings$cmAnalysisList <- list() + cmModuleSpecifications$settings$targetComparatorOutcomesList <- list() + + analysisSpecifications <- createEmptyAnalysisSpecifications() |> + addModuleSpecifications(cmModuleSpecifications) + + executionSettings <- createCdmExecutionSettings( + workDatabaseSchema = workDatabaseSchema, + cdmDatabaseSchema = cdmDatabaseSchema, + cohortTableNames = CohortGenerator::getCohortTableNames(cohortTable = "unit_test"), + workFolder = workFolder, + resultsFolder = resultsFolder + ) + expect_error( + cmModule$execute( + analysisSpecifications = analysisSpecifications, + executionSettings = executionSettings, + connectionDetails = connectionDetails + ) + ) +}) diff --git a/tests/testthat/test-Settings.R b/tests/testthat/test-Settings.R index 5309902d..8c1c6490 100644 --- a/tests/testthat/test-Settings.R +++ b/tests/testthat/test-Settings.R @@ -6,7 +6,7 @@ test_that("Test analysis specification creation", { sqlFolder = system.file("testdata/sql", package = "Strategus") ) subsetOperations <- list( - createDemographicSubset( + createDemographicSubsetOperator( name = "Demographic Criteria", ageMin = 18, ageMax = 64 @@ -150,7 +150,7 @@ test_that("Test analysis specification creation", { covariateSettings = covarSettings ) - createStudyPopArgs <- CohortMethod::createCreateStudyPopulationArgs( + createStudyPopulationArgs <- CohortMethod::createCreateStudyPopulationArgs( minDaysAtRisk = 1, riskWindowStart = 0, startAnchor = "cohort start", @@ -173,7 +173,7 @@ test_that("Test analysis specification creation", { analysisId = 1, description = "No matching, simple outcome model", getDbCohortMethodDataArgs = getDbCmDataArgs, - createStudyPopArgs = createStudyPopArgs, + createStudyPopulationArgs = createStudyPopulationArgs, fitOutcomeModelArgs = fitOutcomeModelArgs ) @@ -181,7 +181,7 @@ test_that("Test analysis specification creation", { analysisId = 2, description = "Matching on ps and covariates, simple outcomeModel", getDbCohortMethodDataArgs = getDbCmDataArgs, - createStudyPopArgs = createStudyPopArgs, + createStudyPopulationArgs = createStudyPopulationArgs, createPsArgs = createPsArgs, matchOnPsArgs = matchOnPsArgs, computeSharedCovariateBalanceArgs = computeSharedCovBalArgs, @@ -193,10 +193,19 @@ test_that("Test analysis specification creation", { analysesToExclude <- NULL - cmModuleSpecifications <- cmModuleSettingsCreator$createModuleSpecifications( + cmAnalysesSpecifications <- CohortMethod::createCmAnalysesSpecifications( cmAnalysisList = cmAnalysisList, targetComparatorOutcomesList = targetComparatorOutcomesList, - analysesToExclude = analysesToExclude + analysesToExclude = analysesToExclude, + refitPsForEveryOutcome = FALSE, + refitPsForEveryStudyPopulation = FALSE, + cmDiagnosticThresholds = CohortMethod::createCmDiagnosticThresholds( + easeThreshold = 0.60 # setting this higher to get passes given Eunomia limitations on neg controls + ) + ) + + cmModuleSpecifications <- cmModuleSettingsCreator$createModuleSpecifications( + cmAnalysesSpecifications = cmAnalysesSpecifications$toList() ) # EvidenceSythesis ------------------ From 6610715d1841c8854e4a41796f84c3257882c6bd Mon Sep 17 00:00:00 2001 From: Martijn Schuemie Date: Tue, 13 Jan 2026 16:12:54 +0100 Subject: [PATCH 13/34] Issue 275 balance meta analysis (#278) * Updating evidence synthesis results schema fro balance diagnostic and target_comparator_id * Balance meta-analysis and switching to target_comnparator_id * Fixing balance meta-analysis * Formally importing pnorm from stats to avoid R check note --------- Co-authored-by: Admin_mschuemi --- DESCRIPTION | 3 + NAMESPACE | 1 + R/Module-EvidenceSynthesis.R | 370 ++++++++++++++++-- R/Strategus.R | 1 + extras/ESModule-SimulateResultsForTesting.R | 14 +- extras/ESModule-SimulationFunctions.R | 155 ++++++-- inst/csv/evidenceSynthesisRdms.csv | 199 ++++++---- inst/testdata/esmodule/results.sqlite | Bin 897024 -> 2867200 bytes man/EvidenceSynthesisModule.Rd | 15 +- tests/testthat/test-EvidenceSynthesisModule.R | 79 +++- 10 files changed, 685 insertions(+), 152 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 931e8166..2b89cd87 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -24,6 +24,7 @@ Imports: digest, dplyr, methods, + stats, ParallelLogger (>= 3.1.0), purrr, ResultModelManager (>= 0.5.8), @@ -38,6 +39,8 @@ Suggests: Cyclops (>= 3.6.0), Eunomia, EvidenceSynthesis (>= 1.0.0), + meta, + metafor, FeatureExtraction, fs, knitr, diff --git a/NAMESPACE b/NAMESPACE index f546988b..b99f5e2f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -39,3 +39,4 @@ import(R6) import(dplyr) importFrom(methods,is) importFrom(rlang,.data) +importFrom(stats,pnorm) diff --git a/R/Module-EvidenceSynthesis.R b/R/Module-EvidenceSynthesis.R index 99f3e959..bef7c168 100644 --- a/R/Module-EvidenceSynthesis.R +++ b/R/Module-EvidenceSynthesis.R @@ -239,24 +239,42 @@ EvidenceSynthesisModule <- R6::R6Class( #' heterogeneity in random-effects models)? #' @param tauThreshold What is the maximum allowed tau (measure of between-database #' heterogeneity in Bayesian random-effects models)? + #' @param sdmThreshold What is the maximum allowed standardized difference of mean (SDM) in a meta-analysis + #' of balance statistics? If any covariate has a meta-analysis SDM exceeding this + #' threshold, the diagnostic will fail. If set to NULL, this diagnostic will be + #' ignored. + #' @param sdmAlpha What is the alpha for testing whether the absolute SDM exceeds + #' `sdmThreshold`? If not provided, no significance testing will be + #' performed and any absolute SDM greater than the threshold will be + #' considered imbalance. Note that a Bonferroni adjustment will + #' automatically be applied to adjust for the number of tests performed. #' #' @return #' An object of type `EsDiagnosticThresholds`. createEsDiagnosticThresholds = function(mdrrThreshold = 10, easeThreshold = 0.25, i2Threshold = 0.4, - tauThreshold = log(2)) { + tauThreshold = log(2), + sdmThreshold = NULL, + sdmAlpha = NULL) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertNumeric(mdrrThreshold, len = 1, lower = 0, add = errorMessages) checkmate::assertNumeric(easeThreshold, len = 1, lower = 0, add = errorMessages) checkmate::assertNumeric(i2Threshold, len = 1, lower = 0, add = errorMessages) checkmate::assertNumeric(tauThreshold, len = 1, lower = 0, add = errorMessages) + checkmate::assertNumeric(sdmThreshold, len = 1, lower = 0, null.ok = TRUE, add = errorMessages) + checkmate::assertNumeric(sdmAlpha, len = 1, lower = 0, upper = 1, null.ok = TRUE, add = errorMessages) checkmate::reportAssertions(collection = errorMessages) + if (is.null(sdmThreshold) && !is.null(sdmAlpha)) { + stop("Must specify sdmThreshold when specifying sdmAlpha") + } thresholds <- list( mdrrThreshold = mdrrThreshold, easeThreshold = easeThreshold, i2Threshold = i2Threshold, - tauThreshold = tauThreshold + tauThreshold = tauThreshold, + sdmThreshold = sdmThreshold, + sdmAlpha = sdmAlpha ) class(thresholds) <- "EsDiagnosticThresholds" return(thresholds) @@ -319,6 +337,9 @@ EvidenceSynthesisModule <- R6::R6Class( outputTables <- c( "es_cm_result", "es_cm_diagnostics_summary", + "es_cm_covariate_balance", + "es_cm_shared_covariate_balance", + "es_cm_covariate", "es_sccs_result", "es_sccs_diagnostics_summary" ) @@ -375,7 +396,8 @@ EvidenceSynthesisModule <- R6::R6Class( ParallelLogger::clusterRequire(cluster, "dplyr") on.exit(ParallelLogger::stopCluster(cluster)) - message(sprintf("Performing analysis %s (%s)", analysisSettings$evidenceSynthesisAnalysisId, analysisSettings$evidenceSynthesisDescription)) + message(sprintf("Executing analysis %s (%s)", analysisSettings$evidenceSynthesisAnalysisId, analysisSettings$evidenceSynthesisDescription)) + message("- Performing effect estimate meta-analysis") estimates <- ParallelLogger::clusterApply( cluster = cluster, x = split(fullKeys, seq_len(nrow(fullKeys))), @@ -386,12 +408,41 @@ EvidenceSynthesisModule <- R6::R6Class( ) estimates <- bind_rows(estimates) + if (analysisSettings$evidenceSynthesisSource$sourceMethod == "CohortMethod") { + message("- Performing shared balance meta-analysis") + private$.dedupeCovariates(connection = connection, + databaseSchema = databaseSchema, + evidenceSynthesisSource = analysisSettings$evidenceSynthesisSource, + resultsFolder = resultsFolder) + sharedBalanceDiagnostics <- private$.metaAnalyzeBalance( + connection = connection, + databaseSchema = databaseSchema, + evidenceSynthesisSource = analysisSettings$evidenceSynthesisSource, + evidenceSynthesisAnalysisId = analysisSettings$evidenceSynthesisAnalysisId, + esDiagnosticThresholds = esDiagnosticThresholds, + cluster = cluster, + resultsFolder = resultsFolder, + shared = TRUE + ) + message("- Performing balance meta-analysis") + balanceDiagnostics <- private$.metaAnalyzeBalance( + connection = connection, + databaseSchema = databaseSchema, + evidenceSynthesisSource = analysisSettings$evidenceSynthesisSource, + evidenceSynthesisAnalysisId = analysisSettings$evidenceSynthesisAnalysisId, + esDiagnosticThresholds = esDiagnosticThresholds, + cluster = cluster, + resultsFolder = resultsFolder, + shared = FALSE + ) + } + message("- Calibrating estimates") estimates <- estimates |> inner_join(perDbEstimates$trueEffectSizes, by = intersect(names(estimates), names(perDbEstimates$trueEffectSizes))) if (analysisSettings$controlType == "outcome") { if (analysisSettings$evidenceSynthesisSource$sourceMethod == "CohortMethod") { - controlKey <- c("targetId", "comparatorId", "analysisId") + controlKey <- c("targetComparatorId", "analysisId") } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCaseSeries") { controlKey <- c("exposureId", "nestingCohortId", "covariateId", "analysisId") } @@ -438,10 +489,37 @@ EvidenceSynthesisModule <- R6::R6Class( TRUE ~ "FAIL" )) |> mutate(unblind = ifelse(.data$mdrrDiagnostic != "FAIL" & - .data$easeDiagnostic != "FAIL" & - .data$i2Diagnostic != "FAIL" & - .data$tauDiagnostic != "FAIL", 1, 0)) + .data$easeDiagnostic != "FAIL" & + .data$i2Diagnostic != "FAIL" & + .data$tauDiagnostic != "FAIL", 1, 0)) if (analysisSettings$evidenceSynthesisSource$sourceMethod == "CohortMethod") { + passBalance <- function(maxSdm, sdmFamilyWiseMinP) { + if (is.null(esDiagnosticThresholds$sdmThreshold)) { + return(FALSE) + } + if (is.null(esDiagnosticThresholds$sdmAlpha)) { + return(maxSdm < esDiagnosticThresholds$sdmThreshold) + } else { + return(sdmFamilyWiseMinP > esDiagnosticThresholds$sdmAlpha) + } + } + diagnostics <- diagnostics |> + inner_join(balanceDiagnostics, by = join_by("targetComparatorId", "outcomeId", "analysisId")) |> + inner_join(sharedBalanceDiagnostics, by = join_by("targetComparatorId", "analysisId")) |> + mutate(balanceDiagnostic = case_when( + is.na(.data$maxSdm) | is.null(esDiagnosticThresholds$sdmThreshold) ~ "NOT EVALUATED", + passBalance(.data$maxSdm, .data$sdmFamilyWiseMinP) ~ "PASS", + TRUE ~ "FAIL" + )) |> + mutate(sharedBalanceDiagnostic = case_when( + is.na(.data$sharedMaxSdm) | is.null(esDiagnosticThresholds$sdmThreshold) ~ "NOT EVALUATED", + passBalance(.data$sharedMaxSdm, .data$sharedSdmFamilyWiseMinP) ~ "PASS", + TRUE ~ "FAIL" + )) |> + mutate(unblind = ifelse(.data$unblind == 1 & + .data$balanceDiagnostic != "FAIL" & + .data$sharedBalanceDiagnostic != "FAIL", 1, 0)) + fileName <- file.path(resultsFolder, "es_cm_diagnostics_summary.csv") } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCaseSeries") { fileName <- file.path(resultsFolder, "es_sccs_diagnostics_summary.csv") @@ -735,7 +813,7 @@ EvidenceSynthesisModule <- R6::R6Class( }, .getPerDatabaseEstimates = function(connection, databaseSchema, evidenceSynthesisSource) { if (evidenceSynthesisSource$sourceMethod == "CohortMethod") { - key <- c("targetId", "comparatorId", "outcomeId") + key <- c("targetComparatorId", "outcomeId") databaseIds <- evidenceSynthesisSource$databaseIds analysisIds <- evidenceSynthesisSource$analysisIds sql <- "SELECT cm_result.*, @@ -743,12 +821,10 @@ EvidenceSynthesisModule <- R6::R6Class( unblind_for_evidence_synthesis AS unblind FROM @database_schema.cm_result INNER JOIN @database_schema.cm_target_comparator_outcome - ON cm_result.target_id = cm_target_comparator_outcome.target_id - AND cm_result.comparator_id = cm_target_comparator_outcome.comparator_id + ON cm_result.target_comparator_id = cm_target_comparator_outcome.target_comparator_id AND cm_result.outcome_id = cm_target_comparator_outcome.outcome_id LEFT JOIN @database_schema.cm_diagnostics_summary - ON cm_result.target_id = cm_diagnostics_summary.target_id - AND cm_result.comparator_id = cm_diagnostics_summary.comparator_id + ON cm_result.target_comparator_id = cm_diagnostics_summary.target_comparator_id AND cm_result.outcome_id = cm_diagnostics_summary.outcome_id AND cm_result.analysis_id = cm_diagnostics_summary.analysis_id AND cm_result.database_id = cm_diagnostics_summary.database_id @@ -776,8 +852,7 @@ EvidenceSynthesisModule <- R6::R6Class( llApproximations <- estimates |> filter(.data$unblind == 1) |> select( - "targetId", - "comparatorId", + "targetComparatorId", "outcomeId", "analysisId", "databaseId", @@ -785,8 +860,7 @@ EvidenceSynthesisModule <- R6::R6Class( "seLogRr" ) } else if (evidenceSynthesisSource$likelihoodApproximation %in% c("adaptive grid", "grid with gradients")) { - sql <- "SELECT target_id, - comparator_id, + sql <- "SELECT target_comparator_id, outcome_id, analysis_id, database_id, @@ -811,13 +885,12 @@ EvidenceSynthesisModule <- R6::R6Class( estimates |> filter(.data$unblind == 1) |> select( - "targetId", - "comparatorId", + "targetComparatorId", "outcomeId", "analysisId", "databaseId", ), - by = c("targetId", "comparatorId", "outcomeId", "analysisId", "databaseId") + by = c("targetComparatorId", "outcomeId", "analysisId", "databaseId") ) } else { stop(sprintf("Unknown likelihood approximation '%s'.", evidenceSynthesisSource$likelihoodApproximation)) @@ -833,8 +906,8 @@ EvidenceSynthesisModule <- R6::R6Class( ) trueEffectSizes <- trueEffectSizes |> mutate(trueEffectSize = ifelse(!is.na(.data$trueEffectSize) & .data$trueEffectSize == 0, - NA, - .data$trueEffectSize + NA, + .data$trueEffectSize )) } else if (evidenceSynthesisSource$sourceMethod == "SelfControlledCaseSeries") { key <- c("exposureId", "nestingCohortId", "outcomeId", "exposuresOutcomeSetId", "covariateId") @@ -961,8 +1034,8 @@ EvidenceSynthesisModule <- R6::R6Class( ) trueEffectSizes <- trueEffectSizes |> mutate(trueEffectSize = ifelse(!is.na(.data$trueEffectSize) & .data$trueEffectSize == 0, - NA, - .data$trueEffectSize + NA, + .data$trueEffectSize )) } else { stop(sprintf("Evidence synthesis for source method '%s' hasn't been implemented yet.", evidenceSynthesisSource$sourceMethod)) @@ -974,6 +1047,174 @@ EvidenceSynthesisModule <- R6::R6Class( trueEffectSizes = trueEffectSizes )) }, + .dedupeCovariates = function(connection, databaseSchema, evidenceSynthesisSource, resultsFolder) { + databaseIds <- evidenceSynthesisSource$databaseIds + analysisIds <- evidenceSynthesisSource$analysisIds + sql <- "SELECT covariate_id, + covariate_name, + analysis_id, + covariate_analysis_id + FROM ( + SELECT covariate_id, + covariate_name, + analysis_id, + covariate_analysis_id, + ROW_NUMBER() OVER (PARTITION BY covariate_id ORDER BY database_id) AS rn + FROM @database_schema.cm_covariate + WHERE database_id IN ( + SELECT DISTINCT database_id + FROM @database_schema.cm_diagnostics_summary + WHERE unblind_for_evidence_synthesis = 1 + {@analysis_ids != ''} ? { AND analysis_id IN (@analysis_ids)} + ) + {@database_ids != ''} ? { AND cm_covariate.database_id IN (@database_ids)} + {@analysis_ids != ''} ? { {@database_ids != ''} ? {AND} cm_covariate.analysis_id IN (@analysis_ids)} + ) tmp + WHERE rn = 1; + " + covariates <- DatabaseConnector::renderTranslateQuerySql( + connection = connection, + sql = sql, + database_schema = databaseSchema, + database_ids = if (is.null(databaseIds)) "" else private$.quoteSql(databaseIds), + analysis_ids = if (is.null(analysisIds)) "" else analysisIds, + snakeCaseToCamelCase = TRUE + ) |> + as_tibble() + fileName <- file.path(resultsFolder, "es_cm_covariate.csv") + private$.writeToCsv(covariates, fileName, append = TRUE) + }, + .metaAnalyzeBalance = function(connection, + databaseSchema, + evidenceSynthesisSource, + evidenceSynthesisAnalysisId, + esDiagnosticThresholds, + cluster, + resultsFolder, + shared) { + databaseIds <- evidenceSynthesisSource$databaseIds + analysisIds <- evidenceSynthesisSource$analysisIds + if (shared) { + sql <- "SELECT cm_shared_covariate_balance.database_id, + cm_shared_covariate_balance.target_comparator_id, + cm_shared_covariate_balance.analysis_id, + covariate_id, + std_diff_before, + std_diff_var_before, + std_diff_after, + std_diff_var_after + FROM @database_schema.cm_shared_covariate_balance + INNER JOIN ( + SELECT DISTINCT target_comparator_id, + analysis_id, + database_id + FROM @database_schema.cm_diagnostics_summary + WHERE unblind_for_evidence_synthesis = 1 + ) tmp + ON cm_shared_covariate_balance.target_comparator_id = tmp.target_comparator_id + AND cm_shared_covariate_balance.analysis_id = tmp.analysis_id + AND cm_shared_covariate_balance.database_id = tmp.database_id + {@database_ids != ''| @analysis_ids != ''} ? {WHERE} + {@database_ids != ''} ? { cm_shared_covariate_balance.database_id IN (@database_ids)} + {@analysis_ids != ''} ? { {@database_ids != ''} ? {AND} cm_shared_covariate_balance.analysis_id IN (@analysis_ids)}; + " + } else { + sql <- "SELECT cm_covariate_balance.database_id, + cm_covariate_balance.target_comparator_id, + cm_covariate_balance.outcome_id, + cm_covariate_balance.analysis_id, + covariate_id, + std_diff_before, + std_diff_var_before, + std_diff_after, + std_diff_var_after + FROM @database_schema.cm_covariate_balance + INNER JOIN @database_schema.cm_diagnostics_summary + ON cm_covariate_balance.target_comparator_id = cm_diagnostics_summary.target_comparator_id + AND cm_covariate_balance.outcome_id = cm_diagnostics_summary.outcome_id + AND cm_covariate_balance.analysis_id = cm_diagnostics_summary.analysis_id + AND cm_covariate_balance.database_id = cm_diagnostics_summary.database_id + WHERE unblind_for_evidence_synthesis = 1 + {@database_ids != ''} ? { AND cm_covariate_balance.database_id IN (@database_ids)} + {@analysis_ids != ''} ? { {@database_ids != ''} ? {AND} cm_covariate_balance.analysis_id IN (@analysis_ids)}; + " + } + balance <- DatabaseConnector::renderTranslateQuerySql( + connection = connection, + sql = sql, + database_schema = databaseSchema, + database_ids = if (is.null(databaseIds)) "" else private$.quoteSql(databaseIds), + analysis_ids = if (is.null(analysisIds)) "" else analysisIds, + snakeCaseToCamelCase = TRUE + ) |> + as_tibble() + + if (shared) { + groups <- balance |> + group_by(.data$targetComparatorId, + .data$analysisId, + .data$covariateId) |> + group_split() + } else { + groups <- balance |> + group_by(.data$targetComparatorId, + .data$outcomeId, + .data$analysisId, + .data$covariateId) |> + group_split() + } + balance <- NULL + # There appears to be considerable overhead for every function call by clusterApply. So batching jobs + # to have fewer calls. + batches <- split( + groups, + ceiling(seq_along(groups) / 100) + ) + groups <- NULL + balance <- ParallelLogger::clusterApply(cluster, batches, .metaAnalyzeCovariateBatch, shared = shared) + balance <- bind_rows(balance) |> + mutate(evidenceSynthesisAnalysisId = !!evidenceSynthesisAnalysisId) + threshold <- esDiagnosticThresholds$sdmThreshold + if (is.null(threshold)) { + balance$balancedBefore <- 1 + balance$balancedAfter <- 1 + balance$beforeP <- 1 + balance$afterP <- 1 + } else { + balance$beforeP <- .computeBalanceP(balance$stdDiffBefore, balance$stdDiffVarBefore, threshold) + balance$afterP <- .computeBalanceP(balance$stdDiffAfter, balance$stdDiffVarAfter, threshold) + + alpha <- esDiagnosticThresholds$sdmAlpha + if (is.null(alpha)) { + balance$balancedBefore <- if_else(abs(balance$stdDiffBefore) <= threshold, 1, 0) + balance$balancedAfter <- if_else(abs(balance$stdDiffAfter) <= threshold, 1, 0) + } else { + balance$balancedBefore <- if_else(balance$beforeP > alpha / nrow(balance), 1, 0) + balance$balancedAfter <- if_else(balance$afterP > alpha / nrow(balance), 1, 0) + } + } + if (shared) { + balanceDiagnostic <- balance |> + group_by(.data$targetComparatorId, + .data$analysisId) |> + summarise(sharedMaxSdm = max(abs(.data$stdDiffAfter), na.rm = TRUE), + sharedSdmFamilyWiseMinP = sum(!is.na(.data$stdDiffVarAfter)) * .minOrNa(.data$afterP)) + } else { + balanceDiagnostic <- balance |> + group_by(.data$targetComparatorId, + .data$outcomeId, + .data$analysisId) |> + summarise(maxSdm = max(abs(.data$stdDiffAfter), na.rm = TRUE), + sdmFamilyWiseMinP = sum(!is.na(.data$stdDiffVarAfter)) * .minOrNa(.data$afterP)) + } + balance <- balance |> + select(-"beforeP", -"afterP") + fileName <- file.path(resultsFolder, if_else(shared, + "es_cm_shared_covariate_balance.csv", + "es_cm_covariate_balance.csv")) + private$.writeToCsv(balance, fileName, append = TRUE) + return(balanceDiagnostic) + }, .writeToCsv = function(data, fileName, append) { tableName <- gsub(".csv$", "", basename(fileName)) names <- colnames(private$.createEmptyResult(tableName)) @@ -1009,3 +1250,86 @@ EvidenceSynthesisModule <- R6::R6Class( } ) ) + +.metaAnalyzeCovariateBatch <- function(batch, shared = FALSE) { + balance <- lapply(batch, .metaAnalyzeSingleCovariate, shared = shared) + balance <- bind_rows(balance) + return(balance) +} + +.metaAnalyzeSingleCovariate <- function(group, shared = FALSE) { + if (nrow(group) == 1) { + row <- group |> + select("targetComparatorId", + "analysisId", + "covariateId", + "stdDiffBefore", + "stdDiffBefore", + "stdDiffAfter", + "stdDiffVarAfter") + } else { + + groupBefore <- group |> + filter(!is.na(.data$stdDiffVarBefore) & .data$stdDiffVarBefore != 0) + if (nrow(groupBefore) == 0) { + stdDiffBefore <- NA + stdDiffVarBefore <- NA + } else if (nrow(groupBefore == 1)) { + stdDiffBefore <- groupBefore$stdDiffBefore + stdDiffVarBefore <- groupBefore$stdDiffVarBefore + } else { + metaBefore <- metafor::rma(yi = groupBefore$stdDiffBefore, + vi = groupBefore$stdDiffVarBefore, + control = list(iter.max = 1000)) + stdDiffBefore <- metaBefore$beta + stdDiffVarBefore <- metaBefore$se ^ 2 + } + + groupAfter <- group |> + filter(!is.na(.data$stdDiffVarAfter) & .data$stdDiffVarAfter != 0) + if (nrow(groupAfter) == 0) { + stdDiffAfter <- NA + stdDiffVarAfter <- NA + } else if (nrow(groupAfter == 1)) { + stdDiffAfter <- groupAfter$stdDiffAfter + stdDiffVarAfter <- groupAfter$stdDiffVarAfter + } else { + metaAfter <- metafor::rma(yi = groupAfter$stdDiffAfter, + vi = groupAfter$stdDiffVarAfter, + control = list(iter.max = 1000)) + stdDiffAfter <- metaAfter$beta + stdDiffVarAfter <- metaAfter$se ^ 2 + } + row <- tibble( + targetComparatorId = group$targetComparatorId[1], + analysisId = group$analysisId[1], + covariateId = group$covariateId[1], + stdDiffBefore = stdDiffBefore, + stdDiffVarBefore = stdDiffVarBefore, + stdDiffAfter = stdDiffAfter, + stdDiffVarAfter = stdDiffVarAfter + ) + } + if (!shared) { + row$outcomeId <- group$outcomeId[1] + } + return(row) +} + +.computeBalanceP <- function(sdm, sdmVariance, threshold) { + zUpper <- (abs(sdm) - threshold) / sqrt(sdmVariance) + pUpper <- pnorm(zUpper, lower.tail = FALSE) + zLower <- (-abs(sdm) - threshold) / sqrt(sdmVariance) + pLower <- pnorm(zLower, lower.tail = TRUE) + p <- pUpper + pLower + return(p) +} + +.minOrNa <- function(x) { + x <- x[!is.na(x)] + if (length(x) == 0) { + return(as.numeric(NA)) + } else { + return(as.numeric(min(x))) + } +} diff --git a/R/Strategus.R b/R/Strategus.R index 803e6572..f02f7312 100644 --- a/R/Strategus.R +++ b/R/Strategus.R @@ -18,6 +18,7 @@ "_PACKAGE" #' @importFrom rlang .data +#' @importFrom stats pnorm #' @import dplyr #' @import CohortGenerator #' @import DatabaseConnector diff --git a/extras/ESModule-SimulateResultsForTesting.R b/extras/ESModule-SimulateResultsForTesting.R index eb552ded..6777cdfe 100644 --- a/extras/ESModule-SimulateResultsForTesting.R +++ b/extras/ESModule-SimulateResultsForTesting.R @@ -12,16 +12,15 @@ connection <- DatabaseConnector::connect(dbms = "sqlite", server = databaseFile) # Simulate CohortMethod data --------------------------------------------------- -targetId <- 1 -comparatorId <- 2 +targetComparatorId <- 1 +firstOutcome <- TRUE # outcomeId <- 1 for (outcomeId in 1:26) { message(sprintf("Simulating outcome %d", outcomeId)) outcomeOfInterest <- outcomeId == 1 trueEffectSize <- if_else(outcomeOfInterest, 2, 1) cmTargetComparatorOutcome <- tibble( - targetId = targetId, - comparatorId = comparatorId, + targetComparatorId = targetComparatorId, outcomeId = outcomeId, trueEffectSize = trueEffectSize, outcomeOfInterest = outcomeOfInterest @@ -36,8 +35,13 @@ for (outcomeId in 1:26) { camelCaseToSnakeCase = TRUE ) for (analysisId in 1:4) { - simulateTco(targetId, comparatorId, outcomeId, analysisId, hazardRatio = trueEffectSize) + simulateTco(targetComparatorId = targetComparatorId, + outcomeId = outcomeId, + analysisId = analysisId, + hazardRatio = trueEffectSize, + firstOutcome = firstOutcome) } + firstOutcome <- FALSE } # Simulate SCCS data ----------------------------------------------------------- diff --git a/extras/ESModule-SimulationFunctions.R b/extras/ESModule-SimulationFunctions.R index ce09fa5e..0199269c 100644 --- a/extras/ESModule-SimulationFunctions.R +++ b/extras/ESModule-SimulationFunctions.R @@ -21,7 +21,63 @@ library(dplyr) library(survival) -simulateTco <- function(targetId, comparatorId, outcomeId, analysisId, hazardRatio = 1, nSites = 10) { +MIN_COVARIATE_PREVALENCE <- 0.05 + +simulateBalance <- function(n, + targetComparatorId, + outcomeId = NULL, + analysisId, + databaseId, + shared = FALSE) { + data(cohortMethodDataSimulationProfile, package = "CohortMethod") + covariateIds <- cohortMethodDataSimulationProfile$covariatePrevalence |> + filter(prevalence > MIN_COVARIATE_PREVALENCE) |> + pull(covariateId) + cohortMethodData <- CohortMethod::simulateCohortMethodData(cohortMethodDataSimulationProfile, n = n) + studyPop <- cohortMethodData$cohorts |> + collect() |> + mutate(iptw = 0.1) + computeCovariateBalanceArgs <- CohortMethod::createComputeCovariateBalanceArgs() + if (!shared) { + computeCovariateBalanceArgs$covariateFilter <- FeatureExtraction::getDefaultTable1Specifications() + } + cohortMethodData$covariates <- cohortMethodData$covariates |> + filter(covariateId %in% covariateIds) + balance <- CohortMethod::computeCovariateBalance(population = studyPop, + cohortMethodData = cohortMethodData, + computeCovariateBalanceArgs = computeCovariateBalanceArgs) + cmDiagnosticThresholds <- CohortMethod::createCmDiagnosticThresholds(sdmAlpha = 0.05) + balance <- CohortMethod:::tidyBalance(balance, 5, cmDiagnosticThresholds) + balance <- balance |> + mutate(targetComparatorId = !!targetComparatorId, + analysisId = !!analysisId, + outcomeId = !!outcomeId, + databaseId = !!databaseId) + return(balance) +} + +getCovariates <- function(analysisId, databaseId) { + data(cohortMethodDataSimulationProfile, package = "CohortMethod") + covariateIds <- cohortMethodDataSimulationProfile$covariatePrevalence |> + filter(prevalence > MIN_COVARIATE_PREVALENCE) |> + pull(covariateId) + covariateRef <- cohortMethodDataSimulationProfile$covariateRef |> + filter(covariateId %in% covariateIds) |> + rename(covariateAnalysisId = "analysisId") |> + mutate(covariateName = gsub("^.*index: ", "", covariateName)) |> + mutate(analysisId = !!analysisId, + databaseId = !!databaseId) |> + select(-"conceptId") + return(covariateRef) +} + +# analysisId = 1 +simulateTco <- function(targetComparatorId, + outcomeId, + analysisId, + hazardRatio = 1, + firstOutcome = TRUE, + nSites = 10) { simulationSettings <- EvidenceSynthesis::createSimulationSettings( nSites = nSites, n = 2500, @@ -30,8 +86,7 @@ simulateTco <- function(targetId, comparatorId, outcomeId, analysisId, hazardRat randomEffectSd = if_else(hazardRatio == 1, 0, 0.5) ) cmDiagnosticsSummary <- tibble( - targetId = targetId, - comparatorId = comparatorId, + targetComparatorId = targetComparatorId, outcomeId = outcomeId, analysisId = analysisId, databaseId = seq_len(nSites), @@ -42,6 +97,9 @@ simulateTco <- function(targetId, comparatorId, outcomeId, analysisId, hazardRat populations <- EvidenceSynthesis::simulatePopulations(simulationSettings) cmResult <- list() cmLikelihoodProfile <- list() + cmBalance <- list() + cmSharedBalance <- list() + cmCovariate <- list() # i = 1 for (i in seq_along(populations)) { population <- populations[[i]] @@ -59,13 +117,11 @@ simulateTco <- function(targetId, comparatorId, outcomeId, analysisId, hazardRat } ) normal <- EvidenceSynthesis::approximateLikelihood(cyclopsFit, "x", approximation = "normal") - # nonNormal <- EvidenceSynthesis::approximateLikelihood(cyclopsFit, "x", approximation = "adaptive grid") nonNormal <- EvidenceSynthesis::approximateLikelihood(cyclopsFit, "x", approximation = "grid with gradients") z <- normal$logRr / normal$seLogRr p <- 2 * pmin(pnorm(z), 1 - pnorm(z)) cmResult[[i]] <- tibble( - targetId = targetId, - comparatorId = comparatorId, + targetComparatorId = targetComparatorId, outcomeId = outcomeId, analysisId = analysisId, databaseId = i, @@ -82,22 +138,38 @@ simulateTco <- function(targetId, comparatorId, outcomeId, analysisId, hazardRat logRr = normal$logRr, seLogRr = normal$seLogRr ) - cmLikelihoodProfile[[i]] <- nonNormal %>% + cmLikelihoodProfile[[i]] <- nonNormal |> rename( logRr = "point", logLikelihood = "value", gradient = "derivative" - ) %>% + ) |> mutate( - targetId = targetId, - comparatorId = comparatorId, + targetComparatorId = targetComparatorId, outcomeId = outcomeId, analysisId = analysisId, databaseId = i ) + cmBalance[[i]] <- simulateBalance(n = nrow(population), + targetComparatorId = targetComparatorId, + outcomeId = outcomeId, + analysisId = analysisId, + databaseId = i, + shared = FALSE) + if (firstOutcome) { + cmSharedBalance[[i]] <- simulateBalance(n = nrow(population), + targetComparatorId = targetComparatorId, + analysisId = analysisId, + databaseId = i, + shared = TRUE) + cmCovariate[[i]] <- getCovariates(analysisId = analysisId, + databaseId = i) + } } cmResult <- bind_rows(cmResult) cmLikelihoodProfile <- bind_rows(cmLikelihoodProfile) + cmBalance <- bind_rows(cmBalance) + tablesExist <- DatabaseConnector::existsTable( connection = connection, databaseSchema = "main", @@ -131,6 +203,38 @@ simulateTco <- function(targetId, comparatorId, outcomeId, analysisId, hazardRat dropTableIfExists = FALSE, camelCaseToSnakeCase = TRUE ) + DatabaseConnector::insertTable( + connection = connection, + databaseSchema = "main", + tableName = "cm_covariate_balance", + data = cmBalance, + createTable = !tablesExist, + dropTableIfExists = FALSE, + camelCaseToSnakeCase = TRUE + ) + + if (firstOutcome) { + cmSharedBalance <- bind_rows(cmSharedBalance) + cmCovariate <- bind_rows(cmCovariate) + DatabaseConnector::insertTable( + connection = connection, + databaseSchema = "main", + tableName = "cm_shared_covariate_balance", + data = cmSharedBalance, + createTable = !tablesExist, + dropTableIfExists = FALSE, + camelCaseToSnakeCase = TRUE + ) + DatabaseConnector::insertTable( + connection = connection, + databaseSchema = "main", + tableName = "cm_covariate", + data = cmCovariate, + createTable = !tablesExist, + dropTableIfExists = FALSE, + camelCaseToSnakeCase = TRUE + ) + } } # exposureId = 1; incidenceRateRatio = 1 @@ -207,19 +311,24 @@ simulateEo <- function(exposureId, outcomeId, analysisId, incidenceRateRatio = 1 llr = ifelse(nrow(estimate) == 0, NA, estimate$llr), databaseId = i ) - - sccsLikelihoodProfile[[i]] <- model$logLikelihoodProfiles[[1]] %>% - rename( - logRr = "point", - logLikelihood = "value", - gradient = "derivative" - ) %>% - mutate( - exposuresOutcomeSetId = outcomeId, - covariateId = covariateSettings$outputIds[1], - analysisId = analysisId, - databaseId = i - ) + if (is.null(model$logLikelihoodProfiles) || + length(model$logLikelihoodProfiles) == 0 || + is.null(model$logLikelihoodProfiles[[1]])) { + sccsLikelihoodProfile[[i]] <- NULL + } else { + sccsLikelihoodProfile[[i]] <- model$logLikelihoodProfiles[[1]] |> + rename( + logRr = "point", + logLikelihood = "value", + gradient = "derivative" + ) |> + mutate( + exposuresOutcomeSetId = outcomeId, + covariateId = covariateSettings$outputIds[1], + analysisId = analysisId, + databaseId = i + ) + } } sccsResult <- bind_rows(sccsResult) sccsLikelihoodProfile <- bind_rows(sccsLikelihoodProfile) diff --git a/inst/csv/evidenceSynthesisRdms.csv b/inst/csv/evidenceSynthesisRdms.csv index 11003ed5..6cdcebe2 100644 --- a/inst/csv/evidenceSynthesisRdms.csv +++ b/inst/csv/evidenceSynthesisRdms.csv @@ -1,85 +1,114 @@ -table_name,column_name,data_type,is_required,primary_key,min_cell_count,description -es_analysis,evidence_synthesis_analysis_id,int,Yes,Yes,No,A unique identifier for the evidence synthesis analysis. -es_analysis,evidence_synthesis_description,varchar(255),Yes,No,No,A description of the evidence synthesis analysis. -es_analysis,source_method,varchar(100),Yes,No,No,The method used to produce the source estimates (e.g. 'CohortMethod'). -es_analysis,definition,varchar,Yes,No,No,A JSON string representing the settings of the evidence synthesis analysis. -es_cm_diagnostics_summary,target_id,int,Yes,Yes,No,The identifier for the target cohort. -es_cm_diagnostics_summary,comparator_id,int,Yes,Yes,No,The identifier for the comparator cohort. -es_cm_diagnostics_summary,outcome_id,int,Yes,Yes,No,The identifier for the outcome cohort. -es_cm_diagnostics_summary,analysis_id,int,Yes,Yes,No,A unique identifier for the cohort method analysis. -es_cm_diagnostics_summary,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. -es_cm_diagnostics_summary,mdrr,float,No,No,No,The minimum detectable relative risk. -es_cm_diagnostics_summary,i_2,float,No,No,No,The I2 statistics for heterogeneity. -es_cm_diagnostics_summary,tau,float,No,No,No,The estimated tau (standard deviation of the random-effects distribution). -es_cm_diagnostics_summary,ease,float,No,No,No,The expected absolute systematic error. -es_cm_diagnostics_summary,mdrr_diagnostic,varchar(13),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the MDRR diagnostic. -es_cm_diagnostics_summary,i_2_diagnostic,varchar(13),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the I2 diagnostic. -es_cm_diagnostics_summary,tau_diagnostic,varchar(13),Yes,No,No,PPASS/ NOT EVALUATED / FAIL classification of the tau diagnostic. -es_cm_diagnostics_summary,ease_diagnostic,varchar(13),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the EASE diagnostic. -es_cm_diagnostics_summary,unblind,int,Yes,No,No,"Is unblinding the result recommended? (1 = yes, 0 = no)" -es_cm_result,target_id,int,Yes,Yes,No,The identifier for the target cohort. -es_cm_result,comparator_id,int,Yes,Yes,No,The identifier for the comparator cohort. -es_cm_result,outcome_id,int,Yes,Yes,No,The identifier for the outcome cohort. -es_cm_result,analysis_id,int,Yes,Yes,No,A unique identifier for the cohort method analysis. -es_cm_result,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. -es_cm_result,rr,float,No,No,No,The estimated relative risk (e.g. the hazard ratio). -es_cm_result,ci_95_lb,float,No,No,No,The lower bound of the 95% confidence interval of the relative risk. -es_cm_result,ci_95_ub,float,No,No,No,The upper bound of the 95% confidence interval of the relative risk. -es_cm_result,p,float,No,No,No,The two-sided p-value considering the null hypothesis of no effect. -es_cm_result,one_sided_p,float,No,No,No,The one-sided p-value considering the null hypothesis of RR <= 1. -es_cm_result,log_rr,float,No,No,No,The log of the relative risk. -es_cm_result,se_log_rr,float,No,No,No,The standard error of the log of the relative risk. -es_cm_result,target_subjects,int,No,No,Yes,The number of subject in the target cohort. -es_cm_result,comparator_subjects,int,No,No,Yes,The number of subject in the comparator cohort. -es_cm_result,target_days,bigint,No,No,No,The number of days observed in the target cohort. -es_cm_result,comparator_days,bigint,No,No,No,The number of days observed in the comparator cohort. -es_cm_result,target_outcomes,int,No,No,Yes,The number of outcomes observed in the target cohort. -es_cm_result,comparator_outcomes,int,No,No,Yes,The number of outcomes observed in the comparator cohort. -es_cm_result,n_databases,int,Yes,No,No,The number of databases that contributed to the meta-analytic estimate. -es_cm_result,calibrated_rr,float,No,No,No,The calibrated relative risk. -es_cm_result,calibrated_ci_95_lb,float,No,No,No,The lower bound of the calibrated 95% confidence interval of the relative risk. -es_cm_result,calibrated_ci_95_ub,float,No,No,No,The upper bound of the calibrated 95% confidence interval of the relative risk. -es_cm_result,calibrated_p,float,No,No,No,The calibrated two-sided p-value. -es_cm_result,calibrated_one_sided_p,float,No,No,No,The calibrated one-sided p-value considering the null hypothesis of RR <= 1. -es_cm_result,calibrated_log_rr,float,No,No,No,The log of the calibrated relative risk. -es_cm_result,calibrated_se_log_rr,float,No,No,No,The standard error of the log of the calibrated relative risk. -es_sccs_diagnostics_summary,exposures_outcome_set_id,int,Yes,Yes,No,A foreign key referencing the sccs_exposures_outcome_set table. -es_sccs_diagnostics_summary,covariate_id,int,Yes,Yes,No,A foreign key referencing the sccs_covariate table. The identifier for the covariate of interest. -es_sccs_diagnostics_summary,analysis_id,int,Yes,Yes,No,A unique identifier for the cohort method analysis. -es_sccs_diagnostics_summary,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. -es_sccs_diagnostics_summary,mdrr,float,No,No,No,The minimum detectable relative risk. -es_sccs_diagnostics_summary,i_2,float,No,No,No,The I2 statistics for heterogeneity. -es_sccs_diagnostics_summary,tau,float,No,No,No,The estimated tau (standard deviation of the random-effects distribution). -es_sccs_diagnostics_summary,ease,float,No,No,No,The expected absolute systematic error. -es_sccs_diagnostics_summary,mdrr_diagnostic,varchar(13),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the MDRR diagnostic. -es_sccs_diagnostics_summary,i_2_diagnostic,varchar(13),Yes,No,No,Pass / warning / fail classification of the I2 diagnostic. -es_sccs_diagnostics_summary,tau_diagnostic,varchar(13),Yes,No,No,Pass / warning / fail classification of the tau diagnostic. -es_sccs_diagnostics_summary,ease_diagnostic,varchar(13),Yes,No,No,Pass / warning / fail classification of the EASE diagnostic. -es_sccs_diagnostics_summary,unblind,int,Yes,No,No,"Is unblinding the result recommended? (1 = yes, 0 = no)" -es_sccs_result,analysis_id,int,Yes,Yes,No,A foreign key referencing the sccs_analysis table. -es_sccs_result,exposures_outcome_set_id,int,Yes,Yes,No,A foreign key referencing the sccs_exposures_outcome_set table. -es_sccs_result,covariate_id,int,Yes,Yes,No,A foreign key referencing the sccs_covariate table. The identifier for the covariate of interest. -es_sccs_result,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. -es_sccs_result,rr,float,No,No,No,The estimated relative risk (i.e. the incidence rate ratio). -es_sccs_result,ci_95_lb,float,No,No,No,The lower bound of the 95% confidence interval of the relative risk. -es_sccs_result,ci_95_ub,float,No,No,No,The upper bound of the 95% confidence interval of the relative risk. -es_sccs_result,p,float,No,No,No,The two-sided p-value considering the null hypothesis of no effect. -es_sccs_result,one_sided_p,float,No,No,No,The one-sided p-value considering the null hypothesis of RR <= 1. -es_sccs_result,outcome_subjects,int,Yes,No,Yes,The number of subjects with at least one outcome. -es_sccs_result,outcome_events,int,Yes,No,Yes,The number of outcome events. -es_sccs_result,outcome_observation_periods,int,Yes,No,Yes,The number of observation periods containing at least one outcome. -es_sccs_result,covariate_subjects,int,Yes,No,Yes,The number of subjects having the covariate. -es_sccs_result,covariate_days,bigint,Yes,No,Yes,The total covariate time in days. -es_sccs_result,covariate_eras,int,Yes,No,Yes,The number of continuous eras of the covariate. -es_sccs_result,covariate_outcomes,int,Yes,No,Yes,The number of outcomes observed during the covariate time. -es_sccs_result,observed_days,bigint,Yes,No,Yes,The number of days subjects were observed. -es_sccs_result,n_databases,int,Yes,No,No,The number of databases that contributed to the meta-analytic estimate. -es_sccs_result,log_rr,float,No,No,No,The log of the relative risk. -es_sccs_result,se_log_rr,float,No,No,No,The standard error of the log of the relative risk. -es_sccs_result,calibrated_rr,float,No,No,No,The calibrated relative risk. -es_sccs_result,calibrated_ci_95_lb,float,No,No,No,The lower bound of the calibrated 95% confidence interval of the relative risk. -es_sccs_result,calibrated_ci_95_ub,float,No,No,No,The upper bound of the calibrated 95% confidence interval of the relative risk. -es_sccs_result,calibrated_p,float,No,No,No,The calibrated two-sided p-value. -es_sccs_result,calibrated_one_sided_p,float,No,No,No,The calibrated one-sided p-value considering the null hypothesis of RR <= 1. -es_sccs_result,calibrated_log_rr,float,No,No,No,The log of the calibrated relative risk. -es_sccs_result,calibrated_se_log_rr,float,No,No,No,The standard error of the log of the calibrated relative risk. +table_name,column_name,data_type,is_required,primary_key,min_cell_count,description +es_analysis,evidence_synthesis_analysis_id,int,Yes,Yes,No,A unique identifier for the evidence synthesis analysis. +es_analysis,evidence_synthesis_description,varchar(255),Yes,No,No,A description of the evidence synthesis analysis. +es_analysis,source_method,varchar(100),Yes,No,No,The method used to produce the source estimates (e.g. 'CohortMethod'). +es_analysis,definition,varchar,Yes,No,No,A JSON string representing the settings of the evidence synthesis analysis. +es_cm_diagnostics_summary,target_comparator_id,bigint,Yes,Yes,No,A foreign key referencing the target_comparator table. +es_cm_diagnostics_summary,outcome_id,int,Yes,Yes,No,The identifier for the outcome cohort. +es_cm_diagnostics_summary,analysis_id,int,Yes,Yes,No,A unique identifier for the cohort method analysis. +es_cm_diagnostics_summary,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. +es_cm_diagnostics_summary,mdrr,float,No,No,No,The minimum detectable relative risk. +es_cm_diagnostics_summary,i_2,float,No,No,No,The I2 statistics for heterogeneity. +es_cm_diagnostics_summary,tau,float,No,No,No,The estimated tau (standard deviation of the random-effects distribution). +es_cm_diagnostics_summary,ease,float,No,No,No,The expected absolute systematic error. +es_cm_diagnostics_summary,max_sdm,float,No,No,No,The maximum absolute standardized difference of mean. +es_cm_diagnostics_summary,sdm_family_wise_min_p,float,No,No,No,The minimum family-wise P-value for the test whether the absolute SDM exceeds the diagnostic threshold. +es_cm_diagnostics_summary,shared_max_sdm,float,No,No,No,The maximum absolute standardized difference of mean of the shared balance (shared across outcomes). +es_cm_diagnostics_summary,shared_sdm_family_wise_min_p,float,No,No,No,The minimum family-wise P-value for the test whether the absolute SDM exceeds the diagnostic threshold for the shared balance (across all outcomes). +es_cm_diagnostics_summary,mdrr_diagnostic,varchar(13),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the MDRR diagnostic. +es_cm_diagnostics_summary,i_2_diagnostic,varchar(13),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the I2 diagnostic. +es_cm_diagnostics_summary,tau_diagnostic,varchar(13),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the tau diagnostic. +es_cm_diagnostics_summary,ease_diagnostic,varchar(13),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the EASE diagnostic. +es_cm_diagnostics_summary,balance_diagnostic,varchar(20),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the balance diagnostic (max_sdm). +es_cm_diagnostics_summary,shared_balance_diagnostic,varchar(20),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the shared balance diagnostic (shared_max_sdm). +es_cm_diagnostics_summary,unblind,int,Yes,No,No,"Is unblinding the result recommended? (1 = yes, 0 = no)" +es_cm_result,target_comparator_id,bigint,Yes,Yes,No,A foreign key referencing the target_comparator table. +es_cm_result,outcome_id,int,Yes,Yes,No,The identifier for the outcome cohort. +es_cm_result,analysis_id,int,Yes,Yes,No,A unique identifier for the cohort method analysis. +es_cm_result,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. +es_cm_result,rr,float,No,No,No,The estimated relative risk (e.g. the hazard ratio). +es_cm_result,ci_95_lb,float,No,No,No,The lower bound of the 95% confidence interval of the relative risk. +es_cm_result,ci_95_ub,float,No,No,No,The upper bound of the 95% confidence interval of the relative risk. +es_cm_result,p,float,No,No,No,The two-sided p-value considering the null hypothesis of no effect. +es_cm_result,one_sided_p,float,No,No,No,The one-sided p-value considering the null hypothesis of RR <= 1. +es_cm_result,log_rr,float,No,No,No,The log of the relative risk. +es_cm_result,se_log_rr,float,No,No,No,The standard error of the log of the relative risk. +es_cm_result,target_subjects,int,No,No,Yes,The number of subject in the target cohort. +es_cm_result,comparator_subjects,int,No,No,Yes,The number of subject in the comparator cohort. +es_cm_result,target_days,bigint,No,No,No,The number of days observed in the target cohort. +es_cm_result,comparator_days,bigint,No,No,No,The number of days observed in the comparator cohort. +es_cm_result,target_outcomes,int,No,No,Yes,The number of outcomes observed in the target cohort. +es_cm_result,comparator_outcomes,int,No,No,Yes,The number of outcomes observed in the comparator cohort. +es_cm_result,n_databases,int,Yes,No,No,The number of databases that contributed to the meta-analytic estimate. +es_cm_result,calibrated_rr,float,No,No,No,The calibrated relative risk. +es_cm_result,calibrated_ci_95_lb,float,No,No,No,The lower bound of the calibrated 95% confidence interval of the relative risk. +es_cm_result,calibrated_ci_95_ub,float,No,No,No,The upper bound of the calibrated 95% confidence interval of the relative risk. +es_cm_result,calibrated_p,float,No,No,No,The calibrated two-sided p-value. +es_cm_result,calibrated_one_sided_p,float,No,No,No,The calibrated one-sided p-value considering the null hypothesis of RR <= 1. +es_cm_result,calibrated_log_rr,float,No,No,No,The log of the calibrated relative risk. +es_cm_result,calibrated_se_log_rr,float,No,No,No,The standard error of the log of the calibrated relative risk. +es_cm_covariate_balance,target_comparator_id,bigint,Yes,Yes,No,A foreign key referencing the target_comparator table. +es_cm_covariate_balance,outcome_id,bigint,Yes,Yes,No,The identifier for the outcome cohort. +es_cm_covariate_balance,analysis_id,int,Yes,Yes,No,A foreign key referencing the cm_analysis table. +es_cm_covariate_balance,covariate_id,bigint,Yes,Yes,No,A foreign key referencing the cm_covariate table. +es_cm_covariate_balance,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. +es_cm_covariate_balance,std_diff_before,float,No,No,No,The standardized difference of the means between the target and comparator cohort before propensity score adjustment. +es_cm_covariate_balance,std_diff_var_before,float,No,No,No,The variance of the standardized difference of the means between the target and comparator cohort before propensity score adjustment. +es_cm_covariate_balance,balanced_before,int,No,No,No,"Is the covariate balanced before propensity score adjustment? (1 = yes, 0 = no)" +es_cm_covariate_balance,std_diff_after,float,No,No,No,The standardized difference of the means between the target and comparator cohort after propensity score adjustment. +es_cm_covariate_balance,std_diff_var_after,float,No,No,No,The variance of the standardized difference of the means between the target and comparator cohort after propensity score adjustment. +es_cm_covariate_balance,balanced_after,int,No,No,No,"Is the covariate balanced after propensity score adjustment? (1 = yes, 0 = no)" +es_cm_shared_covariate_balance,target_comparator_id,bigint,Yes,Yes,No,A foreign key referencing the target_comparator table. +es_cm_shared_covariate_balance,analysis_id,int,Yes,Yes,No,A foreign key referencing the cm_analysis table. +es_cm_shared_covariate_balance,covariate_id,bigint,Yes,Yes,No,A foreign key referencing the cm_covariate table. +es_cm_shared_covariate_balance,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. +es_cm_shared_covariate_balance,std_diff_before,float,No,No,No,The standardized difference of the means between the target and comparator cohort before propensity score adjustment. +es_cm_shared_covariate_balance,std_diff_var_before,float,No,No,No,The variance of the standardized difference of the means between the target and comparator cohort before propensity score adjustment. +es_cm_shared_covariate_balance,balanced_before,int,No,No,No,"Is the covariate balanced before propensity score adjustment? (1 = yes, 0 = no)" +es_cm_shared_covariate_balance,std_diff_after,float,No,No,No,The standardized difference of the means between the target and comparator cohort after propensity score adjustment. +es_cm_shared_covariate_balance,std_diff_var_after,float,No,No,No,The variance of the standardized difference of the means between the target and comparator cohort after propensity score adjustment. +es_cm_shared_covariate_balance,balanced_after,int,No,No,No,"Is the covariate balanced after propensity score adjustment? (1 = yes, 0 = no)" +es_cm_covariate,covariate_id,bigint,Yes,Yes,No,A unique identified for a covariate. +es_cm_covariate,covariate_name,varchar,Yes,No,No,"A name for a covariate, e.g. 'Age group: 20-25 years'." +es_cm_covariate,analysis_id,int,Yes,Yes,No,Foreign key referencing the cm_analysis table. +es_cm_covariate,covariate_analysis_id,int,Yes,No,No,Foreign key referencing the cm_covariate_analysis table. +es_sccs_diagnostics_summary,exposures_outcome_set_id,int,Yes,Yes,No,A foreign key referencing the sccs_exposures_outcome_set table. +es_sccs_diagnostics_summary,covariate_id,int,Yes,Yes,No,A foreign key referencing the sccs_covariate table. The identifier for the covariate of interest. +es_sccs_diagnostics_summary,analysis_id,int,Yes,Yes,No,A unique identifier for the cohort method analysis. +es_sccs_diagnostics_summary,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. +es_sccs_diagnostics_summary,mdrr,float,No,No,No,The minimum detectable relative risk. +es_sccs_diagnostics_summary,i_2,float,No,No,No,The I2 statistics for heterogeneity. +es_sccs_diagnostics_summary,tau,float,No,No,No,The estimated tau (standard deviation of the random-effects distribution). +es_sccs_diagnostics_summary,ease,float,No,No,No,The expected absolute systematic error. +es_sccs_diagnostics_summary,mdrr_diagnostic,varchar(13),Yes,No,No,PASS/ NOT EVALUATED / FAIL classification of the MDRR diagnostic. +es_sccs_diagnostics_summary,i_2_diagnostic,varchar(13),Yes,No,No,Pass / warning / fail classification of the I2 diagnostic. +es_sccs_diagnostics_summary,tau_diagnostic,varchar(13),Yes,No,No,Pass / warning / fail classification of the tau diagnostic. +es_sccs_diagnostics_summary,ease_diagnostic,varchar(13),Yes,No,No,Pass / warning / fail classification of the EASE diagnostic. +es_sccs_diagnostics_summary,unblind,int,Yes,No,No,"Is unblinding the result recommended? (1 = yes, 0 = no)" +es_sccs_result,analysis_id,int,Yes,Yes,No,A foreign key referencing the sccs_analysis table. +es_sccs_result,exposures_outcome_set_id,int,Yes,Yes,No,A foreign key referencing the sccs_exposures_outcome_set table. +es_sccs_result,covariate_id,int,Yes,Yes,No,A foreign key referencing the sccs_covariate table. The identifier for the covariate of interest. +es_sccs_result,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. +es_sccs_result,rr,float,No,No,No,The estimated relative risk (i.e. the incidence rate ratio). +es_sccs_result,ci_95_lb,float,No,No,No,The lower bound of the 95% confidence interval of the relative risk. +es_sccs_result,ci_95_ub,float,No,No,No,The upper bound of the 95% confidence interval of the relative risk. +es_sccs_result,p,float,No,No,No,The two-sided p-value considering the null hypothesis of no effect. +es_sccs_result,one_sided_p,float,No,No,No,The one-sided p-value considering the null hypothesis of RR <= 1. +es_sccs_result,outcome_subjects,int,Yes,No,Yes,The number of subjects with at least one outcome. +es_sccs_result,outcome_events,int,Yes,No,Yes,The number of outcome events. +es_sccs_result,outcome_observation_periods,int,Yes,No,Yes,The number of observation periods containing at least one outcome. +es_sccs_result,covariate_subjects,int,Yes,No,Yes,The number of subjects having the covariate. +es_sccs_result,covariate_days,bigint,Yes,No,Yes,The total covariate time in days. +es_sccs_result,covariate_eras,int,Yes,No,Yes,The number of continuous eras of the covariate. +es_sccs_result,covariate_outcomes,int,Yes,No,Yes,The number of outcomes observed during the covariate time. +es_sccs_result,observed_days,bigint,Yes,No,Yes,The number of days subjects were observed. +es_sccs_result,n_databases,int,Yes,No,No,The number of databases that contributed to the meta-analytic estimate. +es_sccs_result,log_rr,float,No,No,No,The log of the relative risk. +es_sccs_result,se_log_rr,float,No,No,No,The standard error of the log of the relative risk. +es_sccs_result,calibrated_rr,float,No,No,No,The calibrated relative risk. +es_sccs_result,calibrated_ci_95_lb,float,No,No,No,The lower bound of the calibrated 95% confidence interval of the relative risk. +es_sccs_result,calibrated_ci_95_ub,float,No,No,No,The upper bound of the calibrated 95% confidence interval of the relative risk. +es_sccs_result,calibrated_p,float,No,No,No,The calibrated two-sided p-value. +es_sccs_result,calibrated_one_sided_p,float,No,No,No,The calibrated one-sided p-value considering the null hypothesis of RR <= 1. +es_sccs_result,calibrated_log_rr,float,No,No,No,The log of the calibrated relative risk. +es_sccs_result,calibrated_se_log_rr,float,No,No,No,The standard error of the log of the calibrated relative risk. \ No newline at end of file diff --git a/inst/testdata/esmodule/results.sqlite b/inst/testdata/esmodule/results.sqlite index d5a9c98ba694e9468a76181a7caa886300e7689c..bfd2e14cc02bad11ff13931e9e3f0c58eefd7516 100644 GIT binary patch literal 2867200 zcmeFa2UJr*(=eRe^g^PdBB)60*ee#)jRm_X(h{NqDi9Ds5$p|A6nn*ry6^S|%;zxO-m|9(enZKFlpVHm+}IyjxvC zA%RdR(7U+_1fneh0kdCV|EGaRu{|pg=6U9Mgm2WCSX2U7q4ozr#!dS})m45$>MNNf 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If any covariate has a meta-analysis SDM exceeding this +threshold, the diagnostic will fail. If set to NULL, this diagnostic will be +ignored.} + +\item{\code{sdmAlpha}}{What is the alpha for testing whether the absolute SDM exceeds +\code{sdmThreshold}? If not provided, no significance testing will be +performed and any absolute SDM greater than the threshold will be +considered imbalance. Note that a Bonferroni adjustment will +automatically be applied to adjust for the number of tests performed.} } \if{html}{\out{}} } diff --git a/tests/testthat/test-EvidenceSynthesisModule.R b/tests/testthat/test-EvidenceSynthesisModule.R index 65c8a73d..0e37ec32 100644 --- a/tests/testthat/test-EvidenceSynthesisModule.R +++ b/tests/testthat/test-EvidenceSynthesisModule.R @@ -131,6 +131,9 @@ test_that("Run module", { expect_true("es_analysis.csv" %in% resultsFiles) expect_true("es_cm_result.csv" %in% resultsFiles) expect_true("es_cm_diagnostics_summary.csv" %in% resultsFiles) + expect_true("es_cm_covariate_balance.csv" %in% resultsFiles) + expect_true("es_cm_shared_covariate_balance.csv" %in% resultsFiles) + expect_true("es_cm_covariate.csv" %in% resultsFiles) expect_true("es_sccs_result.csv" %in% resultsFiles) expect_true("es_sccs_diagnostics_summary.csv" %in% resultsFiles) }) @@ -176,16 +179,14 @@ test_that("Include only allowed CM estimates in meta-analysis", { # Determine if unblinded: sql <- " - SELECT cm_target_comparator_outcome.target_id, - cm_target_comparator_outcome.comparator_id, + SELECT cm_target_comparator_outcome.target_comparator_id, cm_target_comparator_outcome.outcome_id, analysis_id, database_id, unblind_for_evidence_synthesis AS include_1 FROM main.cm_target_comparator_outcome LEFT JOIN main.cm_diagnostics_summary - ON cm_diagnostics_summary.target_id = cm_target_comparator_outcome.target_id - AND cm_diagnostics_summary.comparator_id = cm_target_comparator_outcome.comparator_id + ON cm_diagnostics_summary.target_comparator_id = cm_target_comparator_outcome.target_comparator_id AND cm_diagnostics_summary.outcome_id = cm_target_comparator_outcome.outcome_id; " criterion1 <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE) @@ -195,8 +196,7 @@ test_that("Include only allowed CM estimates in meta-analysis", { # Determine if valid estimate or LL profile: approximations <- bind_rows(lapply(esSettings$evidenceSynthesisAnalysisList, getApproximation)) sql <- " - SELECT cm_result.target_id, - cm_result.comparator_id, + SELECT cm_result.target_comparator_id, cm_result.outcome_id, cm_result.analysis_id, cm_result.database_id, @@ -205,20 +205,18 @@ test_that("Include only allowed CM estimates in meta-analysis", { ELSE 0 END AS has_valid_estimate, CASE - WHEN profiles.target_id IS NOT NULL THEN 1 + WHEN profiles.target_comparator_id IS NOT NULL THEN 1 ELSE 0 END AS has_ll_profile FROM main.cm_result LEFT JOIN ( - SELECT DISTINCT target_id, - comparator_id, + SELECT DISTINCT target_comparator_id, outcome_id, analysis_id, database_id FROM main.cm_likelihood_profile ) profiles - ON cm_result.target_id = profiles.target_id - AND cm_result.comparator_id = profiles.comparator_id + ON cm_result.target_comparator_id = profiles.target_comparator_id AND cm_result.outcome_id = profiles.outcome_id AND cm_result.analysis_id = profiles.analysis_id AND cm_result.database_id = profiles.database_id @@ -238,17 +236,17 @@ test_that("Include only allowed CM estimates in meta-analysis", { # Combine all criteria, and check if agree with results: allowed <- criterion1 %>% inner_join(criterion2, - by = join_by(targetId, comparatorId, outcomeId, analysisId, databaseId), + by = join_by(targetComparatorId, outcomeId, analysisId, databaseId), relationship = "one-to-many" ) %>% inner_join(criterion3, by = join_by(databaseId, evidenceSynthesisAnalysisId)) %>% mutate(include = include1 & include2 & include3) %>% - group_by(targetId, comparatorId, outcomeId, analysisId, evidenceSynthesisAnalysisId) %>% + group_by(targetComparatorId, outcomeId, analysisId, evidenceSynthesisAnalysisId) %>% summarize(nAllowed = sum(include), .groups = "drop") results <- CohortGenerator::readCsv(file.path(testResultsFolder, "EvidenceSynthesisModule", "es_cm_result.csv")) results <- results %>% - left_join(allowed, by = join_by(targetId, comparatorId, outcomeId, analysisId, evidenceSynthesisAnalysisId)) + left_join(allowed, by = join_by(targetComparatorId, outcomeId, analysisId, evidenceSynthesisAnalysisId)) expect_true(all(results$nDatabases == results$nAllowed)) }) @@ -360,7 +358,7 @@ test_that("Check MDRR values", { results <- CohortGenerator::readCsv(file.path(testResultsFolder, "EvidenceSynthesisModule", "es_cm_result.csv")) diagnostics <- CohortGenerator::readCsv(file.path(testResultsFolder, "EvidenceSynthesisModule", "es_cm_diagnostics_summary.csv")) combined <- results %>% - inner_join(diagnostics, by = join_by(targetId, comparatorId, outcomeId, analysisId, evidenceSynthesisAnalysisId)) + inner_join(diagnostics, by = join_by(targetComparatorId, outcomeId, analysisId, evidenceSynthesisAnalysisId)) noDbs <- combined %>% filter(nDatabases == 0) expect_true(all(is.infinite(noDbs$mdrr))) @@ -401,6 +399,57 @@ test_that("Check MDRR values", { expect_true(all(!is.na(multiDbs$mdrr))) }) +test_that("Check covariate balance", { + diagnosticsSummary <- CohortGenerator::readCsv(file.path(testResultsFolder, "EvidenceSynthesisModule", "es_cm_diagnostics_summary.csv")) + + # No threshold was set in main test run, so all balance diagnostics should be "NOT EVALUATED": + expect_true(all(diagnosticsSummary$balanceDiagnostic == "NOT EVALUATED")) + expect_true(all(diagnosticsSummary$sharedBalanceDiagnostic == "NOT EVALUATED")) + + balance <- CohortGenerator::readCsv(file.path(testResultsFolder, "EvidenceSynthesisModule", "es_cm_covariate_balance.csv")) + expect_true(!all(is.na(balance$stdDiffAfter))) + + sharedBalance <- CohortGenerator::readCsv(file.path(testResultsFolder, "EvidenceSynthesisModule", "es_cm_shared_covariate_balance.csv")) + expect_true(!all(is.na(sharedBalance$stdDiffAfter))) + + # Rerun with threshold and alpha set: + testResultsFolder2 <- tempfile("results") + dir.create(testResultsFolder2) + on.exit(unlink(testResultsFolder2)) + esModuleSettingsCreator <- EvidenceSynthesisModule$new() + evidenceSynthesisSourceCmNormal <- esModuleSettingsCreator$createEvidenceSynthesisSource( + sourceMethod = "CohortMethod", + likelihoodApproximation = "normal" + ) + randomEffectsMetaAnalysisCm <- esModuleSettingsCreator$createRandomEffectsMetaAnalysis( + evidenceSynthesisAnalysisId = 1, + evidenceSynthesisSource = evidenceSynthesisSourceCmNormal + ) + evidenceSynthesisModuleSpecs <- esModuleSettingsCreator$createModuleSpecifications( + evidenceSynthesisAnalysisList = list(randomEffectsMetaAnalysisCm), + esDiagnosticThresholds = esModuleSettingsCreator$createEsDiagnosticThresholds( + sdmThreshold = 0.1, + sdmAlpha = 0.05 + ) + ) + esAnalysisSpecifications <- createEmptyAnalysisSpecifications() %>% + addModuleSpecifications(evidenceSynthesisModuleSpecs) + resultsExecutionSettings <- Strategus::createResultsExecutionSettings( + resultsDatabaseSchema = "main", + resultsFolder = testResultsFolder2, + workFolder = workFolder + ) + Strategus::execute( + analysisSpecifications = esAnalysisSpecifications, + executionSettings = resultsExecutionSettings, + connectionDetails = esTestDataConnectionDetails + ) + + diagnosticsSummary <- CohortGenerator::readCsv(file.path(testResultsFolder2, "EvidenceSynthesisModule", "es_cm_diagnostics_summary.csv")) + expect_true(!all(diagnosticsSummary$balanceDiagnostic == "NOT EVALUATED")) + expect_true(!all(diagnosticsSummary$sharedBalanceDiagnostic == "NOT EVALUATED")) +}) + test_that("Don't error when no negative controls present", { # Create dataset without negative controls tempFile <- tempfile(fileext = ".sqlite") From 831c3cd00bc797f6c053cd0e4478d389e89733c0 Mon Sep 17 00:00:00 2001 From: Anthony Sena Date: Thu, 15 Jan 2026 10:32:16 -0500 Subject: [PATCH 14/34] Add logging to results data model creation and upload --- R/ResultDataModel.R | 20 +++++++ extras/PackageMaintenance.R | 49 ++++++++++++----- .../cdmModulesAnalysisSpecifications.json | 53 +++++++------------ .../resultsModulesAnalysisSpecifications.json | 2 + man/TreatmentPatternsModule.Rd | 15 +----- 5 files changed, 77 insertions(+), 62 deletions(-) diff --git a/R/ResultDataModel.R b/R/ResultDataModel.R index 7c422088..1dec8f00 100644 --- a/R/ResultDataModel.R +++ b/R/ResultDataModel.R @@ -35,6 +35,16 @@ createResultDataModel <- function(analysisSpecifications, checkmate::assertClass(resultsConnectionDetails, "ConnectionDetails", add = errorMessages) checkmate::reportAssertions(collection = errorMessages) + # Set up logging + if (!dir.exists(dirname(resultsDataModelSettings$logFileName))) { + dir.create(dirname(resultsDataModelSettings$logFileName), recursive = T) + } + ParallelLogger::addDefaultFileLogger( + name = "STRATEGUS_LOGGER", + fileName = resultsDataModelSettings$logFileName + ) + on.exit(ParallelLogger::unregisterLogger("STRATEGUS_LOGGER")) + # Used to keep track of the execution status executionStatus <- list() @@ -99,6 +109,16 @@ uploadResults <- function(analysisSpecifications, checkmate::assertClass(resultsConnectionDetails, "ConnectionDetails", add = errorMessages) checkmate::reportAssertions(collection = errorMessages) + # Set up logging + if (!dir.exists(dirname(resultsDataModelSettings$logFileName))) { + dir.create(dirname(resultsDataModelSettings$logFileName), recursive = T) + } + ParallelLogger::addDefaultFileLogger( + name = "STRATEGUS_LOGGER", + fileName = resultsDataModelSettings$logFileName + ) + on.exit(ParallelLogger::unregisterLogger("STRATEGUS_LOGGER")) + # Used to keep track of the execution status executionStatus <- list() diff --git a/extras/PackageMaintenance.R b/extras/PackageMaintenance.R index ad6b327b..f36bd66b 100644 --- a/extras/PackageMaintenance.R +++ b/extras/PackageMaintenance.R @@ -216,21 +216,42 @@ ciModuleSpecifications <- ciModuleSettingsCreator$createModuleSpecifications( # Treatment Patterns -------------------- treatmentPatternsCohorts <- getTreatmentPatternsCohorts(cohortDefinitionSet) +treatmentPatternsCohorts <- treatmentPatternsCohorts |> + dplyr::filter(cohortId < 1000) # Remove subgroup cohorts with invalid names tpModuleSettingsCreator <- TreatmentPatternsModule$new() -tpModuleSpecifications <- tpModuleSettingsCreator$createModuleSpecifications( - cohorts = treatmentPatternsCohorts, - startAnchor = "startDate", - windowStart = 0, - endAnchor = "endDate", - windowEnd = 0, - minEraDuration = 7, - splitEventCohorts = NULL, - splitTime = NULL, - eraCollapseSize = 14, - combinationWindow = 7, - minPostCombinationDuration = 7, - filterTreatments = "First", - maxPathLength = 5 +tpModuleSpecifications <- tpModuleSettingsCreator$createMultiAnalysisModuleSpecification( + tpAnalysisList = list( + tpModuleSettingsCreator$createModuleSpecifications( + analysisId = 1, + cohorts = treatmentPatternsCohorts, + startAnchor = "startDate", + windowStart = 0, + endAnchor = "endDate", + windowEnd = 0, + minEraDuration = 7, + splitEventCohorts = NULL, + splitTime = NULL, + eraCollapseSize = 14, + combinationWindow = 7, + minPostCombinationDuration = 7, + filterTreatments = "First", + maxPathLength = 5 + ), + tpModuleSettingsCreator$createModuleSpecifications( + analysisId = 2, + cohorts = treatmentPatternsCohorts, + startAnchor = "startDate", + windowStart = 0, + minEraDuration = 7, + splitEventCohorts = NULL, + splitTime = NULL, + eraCollapseSize = 14, + combinationWindow = 7, + minPostCombinationDuration = 7, + filterTreatments = "First", + maxPathLength = 5 + ) + ) ) # Cohort Method ---------------------- diff --git a/inst/testdata/cdmModulesAnalysisSpecifications.json b/inst/testdata/cdmModulesAnalysisSpecifications.json index 1632172d..aa6daae4 100644 --- a/inst/testdata/cdmModulesAnalysisSpecifications.json +++ b/inst/testdata/cdmModulesAnalysisSpecifications.json @@ -537,39 +537,14 @@ "cohortName": "PenicillinV", "type": "event" }, - { - "cohortId": 1001, - "cohortName": "Celecoxib: age 18 to 64 Age 18 to 64", - "type": "event" - }, - { - "cohortId": 2001, - "cohortName": "Diclofenac: age 18 to 64 Age 18 to 64", - "type": "event" - }, - { - "cohortId": 2002, - "cohortName": "Diclofenac: first event with 365 prior obs first event with 365 prior obs", - "type": "event" - }, - { - "cohortId": 1003, - "cohortName": "Celecoxib: age 18 to 64 and first event with 365 prior obs Age 18 to 64, first event with 365 prior obs", - "type": "event" - }, - { - "cohortId": 2003, - "cohortName": "Diclofenac: age 18 to 64 and first event with 365 prior obs Age 18 to 64, first event with 365 prior obs", - "type": "event" - }, { "cohortId": 8, - "cohortName": "death", + "cohortName": "Death", "type": "exit" } ], "analysisId": 1, - "description": "Analysis 1", + "description": "", "minEraDuration": 7, "eraCollapseSize": 14, "combinationWindow": 7, @@ -633,14 +608,24 @@ "cohortName": "Clavulanate", "type": "event" }, + { + "cohortId": 9, + "cohortName": "Doxylamine", + "type": "event" + }, + { + "cohortId": 10, + "cohortName": "PenicillinV", + "type": "event" + }, { "cohortId": 8, - "cohortName": "death", + "cohortName": "Death", "type": "exit" } ], "analysisId": 2, - "description": "Analysis 1", + "description": "", "minEraDuration": 7, "eraCollapseSize": 14, "combinationWindow": 7, @@ -656,7 +641,7 @@ "windowStart": 0, "endAnchor": "endDate", "windowEnd": 0, - "stratify": true + "stratify": false }, "attr_class": ["ModuleSpecifications", "TreatmentPatternsModuleSpecifications"] } @@ -1807,7 +1792,7 @@ "modelType": "logistic", "addIntercept": true, "useControl": true, - "seed": 51154410, + "seed": 32479502, "name": "Lasso Logistic Regression", "threads": -1, "tolerance": 2e-06, @@ -1822,7 +1807,7 @@ "splitSettings": { "test": 0.25, "train": 0.75, - "seed": 88450, + "seed": 95825, "nfold": 3, "attr_class": "splitSettings", "attr_fun": "randomSplitter" @@ -1949,7 +1934,7 @@ "modelType": "logistic", "addIntercept": true, "useControl": true, - "seed": 96537540, + "seed": 34222411, "name": "Lasso Logistic Regression", "threads": -1, "tolerance": 2e-06, @@ -1964,7 +1949,7 @@ "splitSettings": { "test": 0.25, "train": 0.75, - "seed": 49231, + "seed": 18215, "nfold": 3, "attr_class": "splitSettings", "attr_fun": "randomSplitter" diff --git a/inst/testdata/resultsModulesAnalysisSpecifications.json b/inst/testdata/resultsModulesAnalysisSpecifications.json index 345e036c..d09e7fd6 100644 --- a/inst/testdata/resultsModulesAnalysisSpecifications.json +++ b/inst/testdata/resultsModulesAnalysisSpecifications.json @@ -49,6 +49,8 @@ "easeThreshold": 0.25, "i2Threshold": 0.4, "tauThreshold": 0.6931, + "sdmThreshold": null, + "sdmAlpha": null, "attr_class": "EsDiagnosticThresholds" } }, diff --git a/man/TreatmentPatternsModule.Rd b/man/TreatmentPatternsModule.Rd index 01f495e4..4cb8bddf 100644 --- a/man/TreatmentPatternsModule.Rd +++ b/man/TreatmentPatternsModule.Rd @@ -174,10 +174,7 @@ Creates the TreatmentPatternsnModule Specifications \subsection{Usage}{ \if{html}{\out{

}}\preformatted{TreatmentPatternsModule$createModuleSpecifications( analysisId = 1, - cohorts = NULL, - targetCohorts = NULL, - eventCohorts = NULL, - exitCohorts = NULL, + cohorts, description = "", includeTreatments = NULL, indexDateOffset = NULL, @@ -216,16 +213,6 @@ Data frame containing the following columns and data types: \item{type \code{character(1)} ["target", "event', "exit"]}{Cohort type, describing if the cohort is a target, event, or exit cohort} }} -\item{\code{targetCohorts}}{(\code{list()})\cr -List of target cohorts in list-of-lists form: -e.g. \code{list(list(1, "Hypertension"), list(2, "Diabetes"))}.Each inner list must contain \code{cohortId} (integer) and \code{cohortName} (string)} - -\item{\code{eventCohorts}}{(\code{list()})\cr -Same structure as \code{targetCohorts}. These cohorts are treated as events/treatments} - -\item{\code{exitCohorts}}{(\code{list()})\cr -Same structure; if provided these are treated as exit cohorts} - \item{\code{description}}{(\code{character(1)}) Description for analysis} From b30b393cf9060485f2e4e9681c58a9224dae441b Mon Sep 17 00:00:00 2001 From: schuemie Date: Mon, 2 Feb 2026 08:58:44 +0100 Subject: [PATCH 15/34] Adding tryCatch around metafor::rma. Including changes from PR 282 --- R/Module-EvidenceSynthesis.R | 34 ++++++++++++++++++++++++---------- 1 file changed, 24 insertions(+), 10 deletions(-) diff --git a/R/Module-EvidenceSynthesis.R b/R/Module-EvidenceSynthesis.R index bef7c168..e7f9f7e1 100644 --- a/R/Module-EvidenceSynthesis.R +++ b/R/Module-EvidenceSynthesis.R @@ -1274,14 +1274,21 @@ EvidenceSynthesisModule <- R6::R6Class( if (nrow(groupBefore) == 0) { stdDiffBefore <- NA stdDiffVarBefore <- NA - } else if (nrow(groupBefore == 1)) { + } else if (nrow(groupBefore) == 1) { stdDiffBefore <- groupBefore$stdDiffBefore stdDiffVarBefore <- groupBefore$stdDiffVarBefore } else { - metaBefore <- metafor::rma(yi = groupBefore$stdDiffBefore, - vi = groupBefore$stdDiffVarBefore, - control = list(iter.max = 1000)) - stdDiffBefore <- metaBefore$beta + metaBefore <- tryCatch( + { + metafor::rma(yi = groupBefore$stdDiffBefore, + vi = groupBefore$stdDiffVarBefore, + control = list(iter.max = 2000)) + }, error = function(e) { + warning(e$message) + return(list(beta = matrix(NA), se = NA)) + } + ) + stdDiffBefore <- metaBefore$beta[1, 1] stdDiffVarBefore <- metaBefore$se ^ 2 } @@ -1290,14 +1297,21 @@ EvidenceSynthesisModule <- R6::R6Class( if (nrow(groupAfter) == 0) { stdDiffAfter <- NA stdDiffVarAfter <- NA - } else if (nrow(groupAfter == 1)) { + } else if (nrow(groupAfter) == 1) { stdDiffAfter <- groupAfter$stdDiffAfter stdDiffVarAfter <- groupAfter$stdDiffVarAfter } else { - metaAfter <- metafor::rma(yi = groupAfter$stdDiffAfter, - vi = groupAfter$stdDiffVarAfter, - control = list(iter.max = 1000)) - stdDiffAfter <- metaAfter$beta + metaAfter <- tryCatch( + { + metafor::rma(yi = groupAfter$stdDiffBefore, + vi = groupAfter$stdDiffVarBefore, + control = list(iter.max = 2000)) + }, error = function(e) { + warning(e$message) + return(list(beta = matrix(NA), se = NA)) + } + ) + stdDiffAfter <- metaAfter$beta[1, 1] stdDiffVarAfter <- metaAfter$se ^ 2 } row <- tibble( From 15b06799f57f08ee16852b419bc98d7d6c4939e3 Mon Sep 17 00:00:00 2001 From: Admin_mschuemi Date: Thu, 5 Feb 2026 09:35:15 -0500 Subject: [PATCH 16/34] Fix balance synthesis when no analysis is unblinded --- R/Module-EvidenceSynthesis.R | 131 ++++++++++++++++++++--------------- 1 file changed, 74 insertions(+), 57 deletions(-) diff --git a/R/Module-EvidenceSynthesis.R b/R/Module-EvidenceSynthesis.R index e7f9f7e1..0c060fe2 100644 --- a/R/Module-EvidenceSynthesis.R +++ b/R/Module-EvidenceSynthesis.R @@ -504,8 +504,8 @@ EvidenceSynthesisModule <- R6::R6Class( } } diagnostics <- diagnostics |> - inner_join(balanceDiagnostics, by = join_by("targetComparatorId", "outcomeId", "analysisId")) |> - inner_join(sharedBalanceDiagnostics, by = join_by("targetComparatorId", "analysisId")) |> + left_join(balanceDiagnostics, by = join_by("targetComparatorId", "outcomeId", "analysisId")) |> + left_join(sharedBalanceDiagnostics, by = join_by("targetComparatorId", "analysisId")) |> mutate(balanceDiagnostic = case_when( is.na(.data$maxSdm) | is.null(esDiagnosticThresholds$sdmThreshold) ~ "NOT EVALUATED", passBalance(.data$maxSdm, .data$sdmFamilyWiseMinP) ~ "PASS", @@ -1150,68 +1150,85 @@ EvidenceSynthesisModule <- R6::R6Class( as_tibble() if (shared) { - groups <- balance |> - group_by(.data$targetComparatorId, - .data$analysisId, - .data$covariateId) |> - group_split() + tableName <- "es_cm_shared_covariate_balance" } else { - groups <- balance |> - group_by(.data$targetComparatorId, - .data$outcomeId, - .data$analysisId, - .data$covariateId) |> - group_split() + tableName <- "es_cm_covariate_balance" } - balance <- NULL - # There appears to be considerable overhead for every function call by clusterApply. So batching jobs - # to have fewer calls. - batches <- split( - groups, - ceiling(seq_along(groups) / 100) - ) - groups <- NULL - balance <- ParallelLogger::clusterApply(cluster, batches, .metaAnalyzeCovariateBatch, shared = shared) - balance <- bind_rows(balance) |> - mutate(evidenceSynthesisAnalysisId = !!evidenceSynthesisAnalysisId) - threshold <- esDiagnosticThresholds$sdmThreshold - if (is.null(threshold)) { - balance$balancedBefore <- 1 - balance$balancedAfter <- 1 - balance$beforeP <- 1 - balance$afterP <- 1 - } else { - balance$beforeP <- .computeBalanceP(balance$stdDiffBefore, balance$stdDiffVarBefore, threshold) - balance$afterP <- .computeBalanceP(balance$stdDiffAfter, balance$stdDiffVarAfter, threshold) - alpha <- esDiagnosticThresholds$sdmAlpha - if (is.null(alpha)) { - balance$balancedBefore <- if_else(abs(balance$stdDiffBefore) <= threshold, 1, 0) - balance$balancedAfter <- if_else(abs(balance$stdDiffAfter) <= threshold, 1, 0) + if (nrow(balance) == 0) { + balance <- private$.createEmptyResult(tableName) + if (shared) { + balanceDiagnostic <- balance |> + mutate(sharedMaxSdm = NA, + sharedSdmFamilyWiseMinP = NA) } else { - balance$balancedBefore <- if_else(balance$beforeP > alpha / nrow(balance), 1, 0) - balance$balancedAfter <- if_else(balance$afterP > alpha / nrow(balance), 1, 0) + balanceDiagnostic <- balance |> + mutate(maxSdm = NA, + sdmFamilyWiseMinP = NA) } - } - if (shared) { - balanceDiagnostic <- balance |> - group_by(.data$targetComparatorId, - .data$analysisId) |> - summarise(sharedMaxSdm = max(abs(.data$stdDiffAfter), na.rm = TRUE), - sharedSdmFamilyWiseMinP = sum(!is.na(.data$stdDiffVarAfter)) * .minOrNa(.data$afterP)) } else { - balanceDiagnostic <- balance |> - group_by(.data$targetComparatorId, - .data$outcomeId, - .data$analysisId) |> - summarise(maxSdm = max(abs(.data$stdDiffAfter), na.rm = TRUE), - sdmFamilyWiseMinP = sum(!is.na(.data$stdDiffVarAfter)) * .minOrNa(.data$afterP)) + if (shared) { + groups <- balance |> + group_by(.data$targetComparatorId, + .data$analysisId, + .data$covariateId) |> + group_split() + } else { + groups <- balance |> + group_by(.data$targetComparatorId, + .data$outcomeId, + .data$analysisId, + .data$covariateId) |> + group_split() + } + balance <- NULL + # There appears to be considerable overhead for every function call by clusterApply. So batching jobs + # to have fewer calls. + batches <- split( + groups, + ceiling(seq_along(groups) / 100) + ) + groups <- NULL + balance <- ParallelLogger::clusterApply(cluster, batches, .metaAnalyzeCovariateBatch, shared = shared) + balance <- bind_rows(balance) |> + mutate(evidenceSynthesisAnalysisId = !!evidenceSynthesisAnalysisId) + threshold <- esDiagnosticThresholds$sdmThreshold + if (is.null(threshold)) { + balance$balancedBefore <- 1 + balance$balancedAfter <- 1 + balance$beforeP <- 1 + balance$afterP <- 1 + } else { + balance$beforeP <- .computeBalanceP(balance$stdDiffBefore, balance$stdDiffVarBefore, threshold) + balance$afterP <- .computeBalanceP(balance$stdDiffAfter, balance$stdDiffVarAfter, threshold) + + alpha <- esDiagnosticThresholds$sdmAlpha + if (is.null(alpha)) { + balance$balancedBefore <- if_else(abs(balance$stdDiffBefore) <= threshold, 1, 0) + balance$balancedAfter <- if_else(abs(balance$stdDiffAfter) <= threshold, 1, 0) + } else { + balance$balancedBefore <- if_else(balance$beforeP > alpha / nrow(balance), 1, 0) + balance$balancedAfter <- if_else(balance$afterP > alpha / nrow(balance), 1, 0) + } + } + if (shared) { + balanceDiagnostic <- balance |> + group_by(.data$targetComparatorId, + .data$analysisId) |> + summarise(sharedMaxSdm = max(abs(.data$stdDiffAfter), na.rm = TRUE), + sharedSdmFamilyWiseMinP = sum(!is.na(.data$stdDiffVarAfter)) * .minOrNa(.data$afterP)) + } else { + balanceDiagnostic <- balance |> + group_by(.data$targetComparatorId, + .data$outcomeId, + .data$analysisId) |> + summarise(maxSdm = max(abs(.data$stdDiffAfter), na.rm = TRUE), + sdmFamilyWiseMinP = sum(!is.na(.data$stdDiffVarAfter)) * .minOrNa(.data$afterP)) + } + balance <- balance |> + select(-"beforeP", -"afterP") } - balance <- balance |> - select(-"beforeP", -"afterP") - fileName <- file.path(resultsFolder, if_else(shared, - "es_cm_shared_covariate_balance.csv", - "es_cm_covariate_balance.csv")) + fileName <- file.path(resultsFolder, paste(tableName, "csv", sep = ".")) private$.writeToCsv(balance, fileName, append = TRUE) return(balanceDiagnostic) }, From 2bbd2f05f8483807620cc281c166adef63b95c8f Mon Sep 17 00:00:00 2001 From: Martijn Schuemie Date: Fri, 6 Feb 2026 16:10:26 +0100 Subject: [PATCH 17/34] Implementing prediction intervals in evidence synthesis (#284) --- R/Module-EvidenceSynthesis.R | 34 ++++++++++++++++--- inst/csv/evidenceSynthesisRdms.csv | 10 +++++- tests/testthat/test-EvidenceSynthesisModule.R | 21 ++++++++++++ 3 files changed, 60 insertions(+), 5 deletions(-) diff --git a/R/Module-EvidenceSynthesis.R b/R/Module-EvidenceSynthesis.R index 0c060fe2..0ebb7265 100644 --- a/R/Module-EvidenceSynthesis.R +++ b/R/Module-EvidenceSynthesis.R @@ -591,6 +591,22 @@ EvidenceSynthesisModule <- R6::R6Class( group$calibratedSeLogRr <- NA group$ease <- NA } + ncsPi <- group[!is.na(group$trueEffectSize) & group$trueEffectSize == 1 & !is.na(group$seLogPi), ] + if (nrow(ncs) >= 5) { + null <- EmpiricalCalibration::fitMcmcNull(logRr = (log(ncs$pi95Lb) + log(ncs$pi95Ub)) / 2.0, seLogRr = ncs$seLogPi) + model <- EmpiricalCalibration::convertNullToErrorModel(null) + calibratedPi <- EmpiricalCalibration::calibrateConfidenceInterval( + logRr = (log(group$pi95Lb) + log(group$pi95Ub)) / 2.0, + seLogRr = group$seLogPi, + model = model + ) + group$calibratedPi95Lb <- exp(calibratedPi$logLb95Rr) + group$calibratedPi95Ub <- exp(calibratedPi$logUb95Rr) + } else { + group$calibratedPi95Lb <- NA + group$calibratedPi95Ub <- NA + } + group$seLogPi <- NULL return(group) }, # row <- split(fullKeys, seq_len(nrow(fullKeys)))[[2]] @@ -740,7 +756,10 @@ EvidenceSynthesisModule <- R6::R6Class( tau = NA, mdrr = computeMdrrFromSe(estimate$seLogRr), p = !!p, - oneSidedP = !!oneSidedP + oneSidedP = !!oneSidedP, + pi95Lb = .data$ci95Lb, + pi95Ub = .data$ci95Ub, + seLogPi = .data$seLogRr ) } else if (is(analysisSettings, "RandomEffectsMetaAnalysis")) { m <- meta::metagen( @@ -752,7 +771,8 @@ EvidenceSynthesisModule <- R6::R6Class( sm = "RR", level.comb = 1 - analysisSettings$alpha ) - rfx <- summary(m)$random + s <- summary(m) + rfx <- s$random oneSidedP <- EmpiricalCalibration::computeTraditionalP( logRr = rfx$TE, seLogRr = rfx$seTE, @@ -769,7 +789,10 @@ EvidenceSynthesisModule <- R6::R6Class( seLogRr = rfx$seTE, i2 = m$I2, tau = NA, - mdrr = computeMdrrFromSe(rfx$seTE) + mdrr = computeMdrrFromSe(rfx$seTE), + pi95Lb = exp(s$predict$lower), + pi95Ub = exp(s$predict$upper), + seLogPi = (s$predict$upper - s$predict$lower) / (2 * qnorm(0.975)) ) } else if (is(analysisSettings, "BayesianMetaAnalysis")) { args <- analysisSettings @@ -803,7 +826,10 @@ EvidenceSynthesisModule <- R6::R6Class( seLogRr = .data$muSe, tau = .data$tau, i2 = NA, - mdrr = computeMdrrFromSe(estimate$seLogRr) + mdrr = computeMdrrFromSe(estimate$seLogRr), + pi95Lb = exp(.data$predictionInterval95Lb), + pi95Ub = exp(.data$predictionInterval95Ub), + seLogPi = (.data$predictionInterval95Ub - .data$predictionInterval95Lb) / (2 * qnorm(0.975)) ) } } diff --git a/inst/csv/evidenceSynthesisRdms.csv b/inst/csv/evidenceSynthesisRdms.csv index 6cdcebe2..5dd57be0 100644 --- a/inst/csv/evidenceSynthesisRdms.csv +++ b/inst/csv/evidenceSynthesisRdms.csv @@ -47,6 +47,10 @@ es_cm_result,calibrated_p,float,No,No,No,The calibrated two-sided p-value. es_cm_result,calibrated_one_sided_p,float,No,No,No,The calibrated one-sided p-value considering the null hypothesis of RR <= 1. es_cm_result,calibrated_log_rr,float,No,No,No,The log of the calibrated relative risk. es_cm_result,calibrated_se_log_rr,float,No,No,No,The standard error of the log of the calibrated relative risk. +es_cm_result,pi_95_lb,float,No,No,No,The lower bound of the 95% prediction interval of the relative risk. +es_cm_result,pi_95_ub,float,No,No,No,The upper bound of the 95% prediction interval of the relative risk. +es_cm_result,calibrated_pi_95_lb,float,No,No,No,The lower bound of the calibrated 95% prediction interval of the relative risk. +es_cm_result,calibrated_pi_95_ub,float,No,No,No,The upper bound of the calibrated 95% prediction interval of the relative risk. es_cm_covariate_balance,target_comparator_id,bigint,Yes,Yes,No,A foreign key referencing the target_comparator table. es_cm_covariate_balance,outcome_id,bigint,Yes,Yes,No,The identifier for the outcome cohort. es_cm_covariate_balance,analysis_id,int,Yes,Yes,No,A foreign key referencing the cm_analysis table. @@ -111,4 +115,8 @@ es_sccs_result,calibrated_ci_95_ub,float,No,No,No,The upper bound of the calibra es_sccs_result,calibrated_p,float,No,No,No,The calibrated two-sided p-value. es_sccs_result,calibrated_one_sided_p,float,No,No,No,The calibrated one-sided p-value considering the null hypothesis of RR <= 1. es_sccs_result,calibrated_log_rr,float,No,No,No,The log of the calibrated relative risk. -es_sccs_result,calibrated_se_log_rr,float,No,No,No,The standard error of the log of the calibrated relative risk. \ No newline at end of file +es_sccs_result,calibrated_se_log_rr,float,No,No,No,The standard error of the log of the calibrated relative risk. +es_sccs_result,pi_95_lb,float,No,No,No,The lower bound of the 95% prediction interval of the relative risk. +es_sccs_result,pi_95_ub,float,No,No,No,The upper bound of the 95% prediction interval of the relative risk. +es_sccs_result,calibrated_pi_95_lb,float,No,No,No,The lower bound of the calibrated 95% prediction interval of the relative risk. +es_sccs_result,calibrated_pi_95_ub,float,No,No,No,The upper bound of the calibrated 95% prediction interval of the relative risk. \ No newline at end of file diff --git a/tests/testthat/test-EvidenceSynthesisModule.R b/tests/testthat/test-EvidenceSynthesisModule.R index 0e37ec32..6adf6dbe 100644 --- a/tests/testthat/test-EvidenceSynthesisModule.R +++ b/tests/testthat/test-EvidenceSynthesisModule.R @@ -399,6 +399,27 @@ test_that("Check MDRR values", { expect_true(all(!is.na(multiDbs$mdrr))) }) +test_that("Check prediction intervals", { + tables <- c("es_cm_result.csv", "es_sccs_result.csv") + for (table in tables) { + data <- readr::read_csv(file.path(testResultsFolder, "EvidenceSynthesisModule", table), show_col_types = FALSE) |> + SqlRender::snakeCaseToCamelCaseNames() + data <- data |> + filter(!is.na(pi95Lb) & !is.na(pi95Ub)) |> + mutate(seLogPi = (log(pi95Ub) - log(pi95Lb)) / 2 * qnorm(0.975), + seLogCalPi = (log(calibratedPi95Ub) - log(calibratedPi95Lb)) / 2 * qnorm(0.975)) + + expect_true(nrow(data) > 0) + + # Prediction interval should be wider than meta-analysis: + expect_true(all(data$ci95Lb >= data$pi95Lb)) + expect_true(all(data$ci95Ub <= data$pi95Ub)) + + # Calibrated PI should be wider than uncalibrated PI (but may be shifted, so using SE): + expect_true(all(data$seLogCalPi >= data$seLogPi)) + } +}) + test_that("Check covariate balance", { diagnosticsSummary <- CohortGenerator::readCsv(file.path(testResultsFolder, "EvidenceSynthesisModule", "es_cm_diagnostics_summary.csv")) From 3a961a62013ca98d67b4c97530b6094b074bd905 Mon Sep 17 00:00:00 2001 From: Admin_mschuemi Date: Tue, 10 Feb 2026 02:45:18 -0500 Subject: [PATCH 18/34] Fixed error when calibrating prediction intervals when there is no data --- R/Module-EvidenceSynthesis.R | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/R/Module-EvidenceSynthesis.R b/R/Module-EvidenceSynthesis.R index 0ebb7265..091f0cb3 100644 --- a/R/Module-EvidenceSynthesis.R +++ b/R/Module-EvidenceSynthesis.R @@ -710,7 +710,10 @@ EvidenceSynthesisModule <- R6::R6Class( seLogRr = as.numeric(NA), i2 = as.numeric(NA), tau = as.numeric(NA), - mdrr = as.numeric(Inf) + mdrr = as.numeric(Inf), + pi95Lb = as.numeric(NA), + pi95Ub = as.numeric(NA), + seLogPi = as.numeric(NA) ) } else if (nDatabases == 1) { estimate <- tibble( @@ -723,7 +726,10 @@ EvidenceSynthesisModule <- R6::R6Class( seLogRr = subset$seLogRr, i2 = NA, tau = NA, - mdrr = subset$mdrr + mdrr = subset$mdrr, + pi95Lb = if (is(analysisSettings, "FixedEffectsMetaAnalysis")) subset$ci95Lb else as.numeric(NA), + pi95Ub = if (is(analysisSettings, "FixedEffectsMetaAnalysis")) subset$ci95Ub else as.numeric(NA), + seLogPi = if (is(analysisSettings, "FixedEffectsMetaAnalysis")) subset$seLogRr else as.numeric(NA) ) } else { if (is(analysisSettings, "FixedEffectsMetaAnalysis")) { From 16790edcc63cf7820eb3212a19c073a6d8cfe0e4 Mon Sep 17 00:00:00 2001 From: Admin_mschuemi Date: Wed, 11 Feb 2026 01:04:56 -0500 Subject: [PATCH 19/34] Minor fix to prediction interval calibration --- R/Module-EvidenceSynthesis.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/Module-EvidenceSynthesis.R b/R/Module-EvidenceSynthesis.R index 091f0cb3..274c655e 100644 --- a/R/Module-EvidenceSynthesis.R +++ b/R/Module-EvidenceSynthesis.R @@ -592,8 +592,8 @@ EvidenceSynthesisModule <- R6::R6Class( group$ease <- NA } ncsPi <- group[!is.na(group$trueEffectSize) & group$trueEffectSize == 1 & !is.na(group$seLogPi), ] - if (nrow(ncs) >= 5) { - null <- EmpiricalCalibration::fitMcmcNull(logRr = (log(ncs$pi95Lb) + log(ncs$pi95Ub)) / 2.0, seLogRr = ncs$seLogPi) + if (nrow(ncsPi) >= 5) { + null <- EmpiricalCalibration::fitMcmcNull(logRr = (log(ncsPi$pi95Lb) + log(ncsPi$pi95Ub)) / 2.0, seLogRr = ncsPi$seLogPi) model <- EmpiricalCalibration::convertNullToErrorModel(null) calibratedPi <- EmpiricalCalibration::calibrateConfidenceInterval( logRr = (log(group$pi95Lb) + log(group$pi95Ub)) / 2.0, From c2161c8f706532d3b20994ce85e0bb664f6e155c Mon Sep 17 00:00:00 2001 From: Admin_mschuemi Date: Thu, 12 Feb 2026 10:20:58 -0500 Subject: [PATCH 20/34] Adding missing join variable to ES --- R/Module-EvidenceSynthesis.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/Module-EvidenceSynthesis.R b/R/Module-EvidenceSynthesis.R index 274c655e..c9efbb8b 100644 --- a/R/Module-EvidenceSynthesis.R +++ b/R/Module-EvidenceSynthesis.R @@ -504,8 +504,8 @@ EvidenceSynthesisModule <- R6::R6Class( } } diagnostics <- diagnostics |> - left_join(balanceDiagnostics, by = join_by("targetComparatorId", "outcomeId", "analysisId")) |> - left_join(sharedBalanceDiagnostics, by = join_by("targetComparatorId", "analysisId")) |> + left_join(balanceDiagnostics, by = join_by("targetComparatorId", "outcomeId", "analysisId", "evidenceSynthesisAnalysisId")) |> + left_join(sharedBalanceDiagnostics, by = join_by("targetComparatorId", "analysisId", "evidenceSynthesisAnalysisId")) |> mutate(balanceDiagnostic = case_when( is.na(.data$maxSdm) | is.null(esDiagnosticThresholds$sdmThreshold) ~ "NOT EVALUATED", passBalance(.data$maxSdm, .data$sdmFamilyWiseMinP) ~ "PASS", From 04b10ffedfdf73870e71f79ca0fae260008e4d49 Mon Sep 17 00:00:00 2001 From: Admin_mschuemi Date: Thu, 12 Feb 2026 10:22:25 -0500 Subject: [PATCH 21/34] Setting required EvidenceSynthesis version to 1.1.0 --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2b89cd87..da47056e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -38,7 +38,7 @@ Suggests: CohortMethod (>= 6.0.0), Cyclops (>= 3.6.0), Eunomia, - EvidenceSynthesis (>= 1.0.0), + EvidenceSynthesis (>= 1.1.0), meta, metafor, FeatureExtraction, From c1645204ce7bdadd93da33a1019765c2fad03f3f Mon Sep 17 00:00:00 2001 From: Admin_mschuemi Date: Fri, 13 Feb 2026 04:56:43 -0500 Subject: [PATCH 22/34] Handling some more edge cases in ES --- R/Module-EvidenceSynthesis.R | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/R/Module-EvidenceSynthesis.R b/R/Module-EvidenceSynthesis.R index c9efbb8b..fa36b9c9 100644 --- a/R/Module-EvidenceSynthesis.R +++ b/R/Module-EvidenceSynthesis.R @@ -1246,14 +1246,16 @@ EvidenceSynthesisModule <- R6::R6Class( if (shared) { balanceDiagnostic <- balance |> group_by(.data$targetComparatorId, - .data$analysisId) |> + .data$analysisId, + .data$evidenceSynthesisAnalysisId) |> summarise(sharedMaxSdm = max(abs(.data$stdDiffAfter), na.rm = TRUE), sharedSdmFamilyWiseMinP = sum(!is.na(.data$stdDiffVarAfter)) * .minOrNa(.data$afterP)) } else { balanceDiagnostic <- balance |> group_by(.data$targetComparatorId, .data$outcomeId, - .data$analysisId) |> + .data$analysisId, + .data$evidenceSynthesisAnalysisId) |> summarise(maxSdm = max(abs(.data$stdDiffAfter), na.rm = TRUE), sdmFamilyWiseMinP = sum(!is.na(.data$stdDiffVarAfter)) * .minOrNa(.data$afterP)) } @@ -1313,7 +1315,7 @@ EvidenceSynthesisModule <- R6::R6Class( "analysisId", "covariateId", "stdDiffBefore", - "stdDiffBefore", + "stdDiffVarBefore", "stdDiffAfter", "stdDiffVarAfter") } else { From d30d1b40a43ab1b2a9d951f1f36f7ec1643679a8 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Tue, 17 Feb 2026 08:55:45 -0800 Subject: [PATCH 23/34] SCC module designed to separate all execution concerns from strategus and place them within the package --- DESCRIPTION | 1 + R/Module-SelfControlledCohort.R | 102 +++++++++++++++++++++++--------- 2 files changed, 75 insertions(+), 28 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 23ab63c1..9d14c3d2 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -46,6 +46,7 @@ Suggests: rmarkdown, RSQLite, SelfControlledCaseSeries (>= 6.0.0), + SelfControlledCohort (>= 2.0.0), testthat (>= 3.0.0), TreatmentPatterns (>= 3.1.0), withr diff --git a/R/Module-SelfControlledCohort.R b/R/Module-SelfControlledCohort.R index 0a196a3c..1e65147c 100644 --- a/R/Module-SelfControlledCohort.R +++ b/R/Module-SelfControlledCohort.R @@ -1,52 +1,62 @@ # SelfControlledCohort ------------- -#' @title Self-Controlled Case Series design with the \href{https://ohdsi.github.io/SelfControlledCaseSeries/}{HADES SelfControlledCaseSeries Package} +#' @title Self-Controlled Cohort design with the \href{https://ohdsi.github.io/SelfControlledCohort/}{HADES SelfControlledCohort Package} #' @export #' @description #' Module for performing Self-Controlled Cohort (SCC) analyses #' against the OMOP Common Data Model. +#' +#' @details +#' This R6 class acts as a thin wrapper around the SelfControlledCohort package functions. +#' The design pattern used here delegates all implementation logic to the package itself, +#' which means this wrapper should rarely (ideally never) need to change when the underlying +#' package evolves. This approach reduces tight coupling between Strategus and analysis modules, +#' making the system more maintainable and allowing module developers to update their packages +#' independently without requiring changes to the Strategus framework. SelfControlledCohort <- R6::R6Class( classname = "SelfControlledCohort", inherit = StrategusModule, public = list( #' @field tablePrefix The table prefix for results tables tablePrefix = "scc_", + #' @description Initialize the module initialize = function() { super$initialize() }, - #' @description Executes the SelfControlledCaseSeries package + + #' @description Executes the SelfControlledCohort package #' @template connectionDetails #' @template analysisSpecifications #' @template executionSettings + #' + #' @details + #' This method simply constructs a jobContext object and delegates execution + #' to the package's own execute() function. By keeping this wrapper minimal, + #' we avoid the need to update Strategus every time the underlying analysis + #' logic changes. The package itself owns its execution logic, version checking, + #' and parameter handling - this wrapper just provides the interface contract + #' that Strategus expects. execute = function(connectionDetails, analysisSpecifications, executionSettings) { super$.validateCdmExecutionSettings(executionSettings) super$execute(connectionDetails, analysisSpecifications, executionSettings) - jobContext <- private$jobContext - sccsMultiThreadingSettings <- SelfControlledCaseSeries::createDefaultSccsMultiThreadingSettings(jobContext$moduleExecutionSettings$maxCores) + # Construct jobContext in the format expected by the package's execute() function + # This is the only coupling point - the jobContext structure. As long as we + # maintain this interface, the package can change its internal implementation freely. + jobContext <- list( + connectionDetails = connectionDetails, + executionSettings = private$jobContext$moduleExecutionSettings, + moduleExecutionSettings = analysisSpecifications + ) - # Provide hook to allow for overriding the number of threads - # used for database operations - computeThreads <- as.integer(getOption("strategus.SelfControlledCohort.threads")) + # Delegate to the package's execute function + # The package handles all logic including version checking, parameter validation, + # and actual analysis execution. This wrapper doesn't need to know about those details. + SelfControlledCohort::execute(jobContext) - args <- jobContext$settings - args$connectionDetails <- connectionDetails - args$cdmDatabaseSchema <- jobContext$moduleExecutionSettings$cdmDatabaseSchema - args$tempEmulationSchema <- jobContext$moduleExecutionSettings$tempEmulationSchema - args$exposureDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema - args$exposureTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable - args$outcomeDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema - args$outcomeTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable - args$nestingCohortDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema - args$nestingCohortTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable - args$customCovariateDatabaseSchema <- jobContext$moduleExecutionSettings$workDatabaseSchema - args$customCovariateTable <- jobContext$moduleExecutionSettings$cohortTableNames$cohortTable - args$outputFolder <- jobContext$moduleExecutionSettings$workSubFolder - args$sccsMultiThreadingSettings <- sccsMultiThreadingSettings - args$sccsAnalysesSpecifications <- SelfControlledCaseSeries::convertUntypedListToSccsAnalysesSpecifications(jobContext$settings$sccsAnalysesSpecifications) - do.call(SelfControlledCaseSeries::execute, args) - exportFolder <- jobContext$moduleExecutionSettings$resultsSubFolder + exportFolder <- private$jobContext$moduleExecutionSettings$resultsSubFolder + # Handle results metadata - this is framework-level concern, not package concern resultsDataModel <- self$getResultsDataModelSpecification() resultsDataModel <- resultsDataModel[file.exists(file.path(exportFolder, paste0(resultsDataModel$tableName, ".csv"))), ] if (any(!startsWith(resultsDataModel$tableName, self$tablePrefix))) { @@ -60,49 +70,85 @@ SelfControlledCohort <- R6::R6Class( private$.message(paste("Results available at:", exportFolder)) }, + #' @description Create the results data model for the module #' @template resultsConnectionDetails #' @template resultsDatabaseSchema #' @template tablePrefix + #' + #' @details + #' Delegates to the package's own createResultsDataModel function. + #' The package knows its own schema requirements - Strategus just needs to + #' call the function with the appropriate database connection details. createResultsDataModel = function(resultsConnectionDetails, resultsDatabaseSchema, tablePrefix = "") { + # Direct delegation - no need to replicate schema logic here SelfControlledCohort::createResultsDataModel( connectionDetails = resultsConnectionDetails, databaseSchema = resultsDatabaseSchema, tablePrefix = tablePrefix ) }, + #' @description Get the results data model specification for the module #' @template tablePrefix + #' + #' @details + #' Retrieves the data model specification from the package itself. + #' Only framework-specific modification is adding the table prefix. getResultsDataModelSpecification = function(tablePrefix = "") { + # Get specification from package - it knows its own schema resultsDataModelSpecification <- SelfControlledCohort::getResultsDataModelSpecifications() - # add the prefix to the tableName column + # Apply framework-level prefix requirement resultsDataModelSpecification$tableName <- paste0(tablePrefix, resultsDataModelSpecification$tableName) return(resultsDataModelSpecification) }, + #' @description Upload the results for the module #' @template resultsConnectionDetails #' @template analysisSpecifications #' @template resultsDataModelSettings + #' + #' @details + #' Delegates upload logic to the package's uploadResults function. + #' The package handles the specifics of what data needs to be uploaded and how. uploadResults = function(resultsConnectionDetails, analysisSpecifications, resultsDataModelSettings, ...) { super$uploadResults(resultsConnectionDetails, analysisSpecifications, resultsDataModelSettings) + # Direct delegation to package function SelfControlledCohort::uploadResults( connectionDetails = resultsConnectionDetails, schema = resultsDataModelSettings$resultsDatabaseSchema, ... ) }, - #' @description Creates the SelfControlledCaseSeries Module Specifications - #' @param sccAnalysesSpecifications An R6 class created by SelfControlledCohort::createSccAnalysis + + #' @description Creates the SelfControlledCohort Module Specifications + #' @param sccAnalysesSpecifications Module specifications created by + #' SelfControlledCohort::createSelfControlledCohortModuleSpecifications() + #' + #' @details + #' Wraps the package's createModuleSpecifications output in Strategus format. + #' The package function already handles versioning, parameter validation, etc. + #' This wrapper just ensures compatibility with Strategus conventions. createModuleSpecifications = function(sccAnalysesSpecifications) { + # The package creates its own specifications with proper version metadata + # We just need to ensure it's wrapped in the parent class structure specifications <- super$createModuleSpecifications( moduleSpecifications = sccAnalysesSpecifications ) return(specifications) }, + #' @description Validate the module specifications - #' @param moduleSpecifications The SelfControlledCaseSeries module specifications + #' @param moduleSpecifications The SelfControlledCohort module specifications + #' + #' @details + #' Validation happens within the package's execute() function (including version checking). + #' This method provides the Strategus interface but doesn't duplicate validation logic. validateModuleSpecifications = function(moduleSpecifications) { + # Parent class validation handles framework-level requirements + # Package-specific validation (including version compatibility) happens + # in the package's execute() function via checkModuleVersion() super$validateModuleSpecifications( moduleSpecifications = moduleSpecifications ) From 98f933f74f56d2b71556c2a197ddd15eb2635c5b Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Thu, 19 Feb 2026 08:17:00 -0800 Subject: [PATCH 24/34] SCC module development build added to description --- DESCRIPTION | 1 + 1 file changed, 1 insertion(+) diff --git a/DESCRIPTION b/DESCRIPTION index 9d14c3d2..5d08fe11 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -54,6 +54,7 @@ Remotes: ohdsi/CohortDiagnostics, ohdsi/CohortIncidence, ohdsi/CohortMethod + ohdsi/SelfControlledCohort@version-2.0.0 License: Apache License 2.0 VignetteBuilder: knitr URL: https://ohdsi.github.io/Strategus, https://github.com/OHDSI/Strategus From bb2f80a8b86c039dcc5504df87a1c01e23ac9a31 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Thu, 19 Feb 2026 08:20:43 -0800 Subject: [PATCH 25/34] Missing comma --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 5d08fe11..31b4e259 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -53,7 +53,7 @@ Suggests: Remotes: ohdsi/CohortDiagnostics, ohdsi/CohortIncidence, - ohdsi/CohortMethod + ohdsi/CohortMethod, ohdsi/SelfControlledCohort@version-2.0.0 License: Apache License 2.0 VignetteBuilder: knitr From 7b81da77780cb7c1eac81e0ace51c039958fd074 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Mon, 23 Feb 2026 08:07:23 -0800 Subject: [PATCH 26/34] SCC module docs added --- NAMESPACE | 1 + R/Module-SelfControlledCohort.R | 4 +- man/SelfControlledCohortModule.Rd | 270 ++++++++++++++++++++++++++++++ 3 files changed, 273 insertions(+), 2 deletions(-) create mode 100644 man/SelfControlledCohortModule.Rd diff --git a/NAMESPACE b/NAMESPACE index f546988b..f9a0a038 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -9,6 +9,7 @@ export(EvidenceSynthesisModule) export(PatientLevelPredictionModule) export(PatientLevelPredictionValidationModule) export(SelfControlledCaseSeriesModule) +export(SelfControlledCohortModule) export(StrategusModule) export(TreatmentPatternsModule) export(addCharacterizationModuleSpecifications) diff --git a/R/Module-SelfControlledCohort.R b/R/Module-SelfControlledCohort.R index 1e65147c..2f601a8a 100644 --- a/R/Module-SelfControlledCohort.R +++ b/R/Module-SelfControlledCohort.R @@ -12,7 +12,7 @@ #' package evolves. This approach reduces tight coupling between Strategus and analysis modules, #' making the system more maintainable and allowing module developers to update their packages #' independently without requiring changes to the Strategus framework. -SelfControlledCohort <- R6::R6Class( +SelfControlledCohortModule <- R6::R6Class( classname = "SelfControlledCohort", inherit = StrategusModule, public = list( @@ -107,7 +107,7 @@ SelfControlledCohort <- R6::R6Class( #' @template resultsConnectionDetails #' @template analysisSpecifications #' @template resultsDataModelSettings - #' + #' @param ... Additional parameters passed to the package's uploadResults function #' @details #' Delegates upload logic to the package's uploadResults function. #' The package handles the specifics of what data needs to be uploaded and how. diff --git a/man/SelfControlledCohortModule.Rd b/man/SelfControlledCohortModule.Rd new file mode 100644 index 00000000..58b48115 --- /dev/null +++ b/man/SelfControlledCohortModule.Rd @@ -0,0 +1,270 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Module-SelfControlledCohort.R +\name{SelfControlledCohortModule} +\alias{SelfControlledCohortModule} +\title{Self-Controlled Cohort design with the \href{https://ohdsi.github.io/SelfControlledCohort/}{HADES SelfControlledCohort Package}} +\description{ +Module for performing Self-Controlled Cohort (SCC) analyses +against the OMOP Common Data Model. +} +\details{ +This R6 class acts as a thin wrapper around the SelfControlledCohort package functions. +The design pattern used here delegates all implementation logic to the package itself, +which means this wrapper should rarely (ideally never) need to change when the underlying +package evolves. This approach reduces tight coupling between Strategus and analysis modules, +making the system more maintainable and allowing module developers to update their packages +independently without requiring changes to the Strategus framework. +} +\section{Super class}{ +\code{\link[Strategus:StrategusModule]{Strategus::StrategusModule}} -> \code{SelfControlledCohort} +} +\section{Public fields}{ +\if{html}{\out{
}} +\describe{ +\item{\code{tablePrefix}}{The table prefix for results tables} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-SelfControlledCohort-new}{\code{SelfControlledCohortModule$new()}} +\item \href{#method-SelfControlledCohort-execute}{\code{SelfControlledCohortModule$execute()}} +\item \href{#method-SelfControlledCohort-createResultsDataModel}{\code{SelfControlledCohortModule$createResultsDataModel()}} +\item \href{#method-SelfControlledCohort-getResultsDataModelSpecification}{\code{SelfControlledCohortModule$getResultsDataModelSpecification()}} +\item \href{#method-SelfControlledCohort-uploadResults}{\code{SelfControlledCohortModule$uploadResults()}} +\item \href{#method-SelfControlledCohort-createModuleSpecifications}{\code{SelfControlledCohortModule$createModuleSpecifications()}} +\item \href{#method-SelfControlledCohort-validateModuleSpecifications}{\code{SelfControlledCohortModule$validateModuleSpecifications()}} +\item \href{#method-SelfControlledCohort-clone}{\code{SelfControlledCohortModule$clone()}} +} +} +\if{html}{\out{ + +}} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-SelfControlledCohort-new}{}}} +\subsection{Method \code{new()}}{ +Initialize the module +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{SelfControlledCohortModule$new()}\if{html}{\out{
}} +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-SelfControlledCohort-execute}{}}} +\subsection{Method \code{execute()}}{ +Executes the SelfControlledCohort package +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{SelfControlledCohortModule$execute( + connectionDetails, + analysisSpecifications, + executionSettings +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{connectionDetails}}{An object of class \code{connectionDetails} as created by the +\code{\link[DatabaseConnector:createConnectionDetails]{DatabaseConnector::createConnectionDetails()}} function.} + +\item{\code{analysisSpecifications}}{An object of type \code{AnalysisSpecifications} as created +by \code{\link[=createEmptyAnalysisSpecificiations]{createEmptyAnalysisSpecificiations()}}.} + +\item{\code{analysisSpecifications}}{An object of type \code{AnalysisSpecifications} as created +by \code{\link[=createEmptyAnalysisSpecificiations]{createEmptyAnalysisSpecificiations()}}.} + +\item{\code{executionSettings}}{An object of type \code{ExecutionSettings} as created +by \code{\link[=createCdmExecutionSettings]{createCdmExecutionSettings()}} or \code{\link[=createResultsExecutionSettings]{createResultsExecutionSettings()}}.} +} +\if{html}{\out{
}} +} +\subsection{Details}{ +This method simply constructs a jobContext object and delegates execution +to the package's own execute() function. By keeping this wrapper minimal, +we avoid the need to update Strategus every time the underlying analysis +logic changes. The package itself owns its execution logic, version checking, +and parameter handling - this wrapper just provides the interface contract +that Strategus expects. +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-SelfControlledCohort-createResultsDataModel}{}}} +\subsection{Method \code{createResultsDataModel()}}{ +Create the results data model for the module +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{SelfControlledCohortModule$createResultsDataModel( + resultsConnectionDetails, + resultsDatabaseSchema, + tablePrefix = "" +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{resultsConnectionDetails}}{The connection details to the results database which +is an object of class \code{connectionDetails} as created by the +\code{\link[DatabaseConnector:createConnectionDetails]{DatabaseConnector::createConnectionDetails()}} function.} + +\item{\code{resultsConnectionDetails}}{The connection details to the results database which +is an object of class \code{connectionDetails} as created by the +\code{\link[DatabaseConnector:createConnectionDetails]{DatabaseConnector::createConnectionDetails()}} function.} + +\item{\code{resultsDatabaseSchema}}{The schema in the results database that holds the results data model.} + +\item{\code{tablePrefix}}{A prefix to apply to the database table names (optional).} + +\item{\code{tablePrefix}}{A prefix to apply to the database table names (optional).} +} +\if{html}{\out{
}} +} +\subsection{Details}{ +Delegates to the package's own createResultsDataModel function. +The package knows its own schema requirements - Strategus just needs to +call the function with the appropriate database connection details. +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-SelfControlledCohort-getResultsDataModelSpecification}{}}} +\subsection{Method \code{getResultsDataModelSpecification()}}{ +Get the results data model specification for the module +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{SelfControlledCohortModule$getResultsDataModelSpecification(tablePrefix = "")}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{tablePrefix}}{A prefix to apply to the database table names (optional).} + +\item{\code{tablePrefix}}{A prefix to apply to the database table names (optional).} +} +\if{html}{\out{
}} +} +\subsection{Details}{ +Retrieves the data model specification from the package itself. +Only framework-specific modification is adding the table prefix. +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-SelfControlledCohort-uploadResults}{}}} +\subsection{Method \code{uploadResults()}}{ +Upload the results for the module +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{SelfControlledCohortModule$uploadResults( + resultsConnectionDetails, + analysisSpecifications, + resultsDataModelSettings, + ... +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{resultsConnectionDetails}}{The connection details to the results database which +is an object of class \code{connectionDetails} as created by the +\code{\link[DatabaseConnector:createConnectionDetails]{DatabaseConnector::createConnectionDetails()}} function.} + +\item{\code{resultsConnectionDetails}}{The connection details to the results database which +is an object of class \code{connectionDetails} as created by the +\code{\link[DatabaseConnector:createConnectionDetails]{DatabaseConnector::createConnectionDetails()}} function.} + +\item{\code{analysisSpecifications}}{An object of type \code{AnalysisSpecifications} as created +by \code{\link[=createEmptyAnalysisSpecificiations]{createEmptyAnalysisSpecificiations()}}.} + +\item{\code{analysisSpecifications}}{An object of type \code{AnalysisSpecifications} as created +by \code{\link[=createEmptyAnalysisSpecificiations]{createEmptyAnalysisSpecificiations()}}.} + +\item{\code{resultsDataModelSettings}}{The results data model settings as created using [@seealso \code{\link[=createResultsDataModelSettings]{createResultsDataModelSettings()}}]} + +\item{\code{...}}{Additional parameters passed to the package's uploadResults function} +} +\if{html}{\out{
}} +} +\subsection{Details}{ +Delegates upload logic to the package's uploadResults function. +The package handles the specifics of what data needs to be uploaded and how. +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-SelfControlledCohort-createModuleSpecifications}{}}} +\subsection{Method \code{createModuleSpecifications()}}{ +Creates the SelfControlledCohort Module Specifications +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{SelfControlledCohortModule$createModuleSpecifications( + sccAnalysesSpecifications +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{sccAnalysesSpecifications}}{Module specifications created by +SelfControlledCohort::createSelfControlledCohortModuleSpecifications()} +} +\if{html}{\out{
}} +} +\subsection{Details}{ +Wraps the package's createModuleSpecifications output in Strategus format. +The package function already handles versioning, parameter validation, etc. +This wrapper just ensures compatibility with Strategus conventions. +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-SelfControlledCohort-validateModuleSpecifications}{}}} +\subsection{Method \code{validateModuleSpecifications()}}{ +Validate the module specifications +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{SelfControlledCohortModule$validateModuleSpecifications(moduleSpecifications)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{moduleSpecifications}}{The SelfControlledCohort module specifications} +} +\if{html}{\out{
}} +} +\subsection{Details}{ +Validation happens within the package's execute() function (including version checking). +This method provides the Strategus interface but doesn't duplicate validation logic. +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-SelfControlledCohort-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{SelfControlledCohortModule$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} From 920cc61940d27368c78f1f2cb60d9c16b00286cc Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Thu, 26 Feb 2026 07:48:32 -0800 Subject: [PATCH 27/34] R6 class fix --- R/Module-SelfControlledCohort.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/Module-SelfControlledCohort.R b/R/Module-SelfControlledCohort.R index 2f601a8a..d3df5abd 100644 --- a/R/Module-SelfControlledCohort.R +++ b/R/Module-SelfControlledCohort.R @@ -13,7 +13,7 @@ #' making the system more maintainable and allowing module developers to update their packages #' independently without requiring changes to the Strategus framework. SelfControlledCohortModule <- R6::R6Class( - classname = "SelfControlledCohort", + classname = "SelfControlledCohortModule", inherit = StrategusModule, public = list( #' @field tablePrefix The table prefix for results tables From 23942f913ac42fdbd33488a0ac26653211e1facf Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Thu, 26 Feb 2026 16:19:01 -0800 Subject: [PATCH 28/34] refactor reading settings from SCC saved state --- R/Module-SelfControlledCohort.R | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/R/Module-SelfControlledCohort.R b/R/Module-SelfControlledCohort.R index d3df5abd..86c3d7d3 100644 --- a/R/Module-SelfControlledCohort.R +++ b/R/Module-SelfControlledCohort.R @@ -40,19 +40,16 @@ SelfControlledCohortModule <- R6::R6Class( super$.validateCdmExecutionSettings(executionSettings) super$execute(connectionDetails, analysisSpecifications, executionSettings) - # Construct jobContext in the format expected by the package's execute() function - # This is the only coupling point - the jobContext structure. As long as we - # maintain this interface, the package can change its internal implementation freely. - jobContext <- list( - connectionDetails = connectionDetails, - executionSettings = private$jobContext$moduleExecutionSettings, - moduleExecutionSettings = analysisSpecifications - ) - + jobContext <- private$jobContext # Delegate to the package's execute function # The package handles all logic including version checking, parameter validation, # and actual analysis execution. This wrapper doesn't need to know about those details. - SelfControlledCohort::execute(jobContext) + SelfControlledCohort::execute( + connectionDetails = connectionDetails, + executionSettings = executionSettings, + analysisSpecifications = jobContext$settings$parameters, + databaseId = jobContext$moduleExecutionSettings$cdmDatabaseMetaData$databaseId + ) exportFolder <- private$jobContext$moduleExecutionSettings$resultsSubFolder From ac9adfae6a425294a1389943a2d1f0d2b27132be Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Fri, 27 Feb 2026 12:32:23 -0800 Subject: [PATCH 29/34] Upload folder set correctly --- R/Module-SelfControlledCohort.R | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/R/Module-SelfControlledCohort.R b/R/Module-SelfControlledCohort.R index 86c3d7d3..ec259cba 100644 --- a/R/Module-SelfControlledCohort.R +++ b/R/Module-SelfControlledCohort.R @@ -41,6 +41,7 @@ SelfControlledCohortModule <- R6::R6Class( super$execute(connectionDetails, analysisSpecifications, executionSettings) jobContext <- private$jobContext + exportFolder <- private$jobContext$moduleExecutionSettings$resultsSubFolder # Delegate to the package's execute function # The package handles all logic including version checking, parameter validation, # and actual analysis execution. This wrapper doesn't need to know about those details. @@ -48,11 +49,10 @@ SelfControlledCohortModule <- R6::R6Class( connectionDetails = connectionDetails, executionSettings = executionSettings, analysisSpecifications = jobContext$settings$parameters, - databaseId = jobContext$moduleExecutionSettings$cdmDatabaseMetaData$databaseId + databaseId = jobContext$moduleExecutionSettings$cdmDatabaseMetaData$databaseId, + exportFolder = exportFolder ) - exportFolder <- private$jobContext$moduleExecutionSettings$resultsSubFolder - # Handle results metadata - this is framework-level concern, not package concern resultsDataModel <- self$getResultsDataModelSpecification() resultsDataModel <- resultsDataModel[file.exists(file.path(exportFolder, paste0(resultsDataModel$tableName, ".csv"))), ] @@ -114,6 +114,7 @@ SelfControlledCohortModule <- R6::R6Class( # Direct delegation to package function SelfControlledCohort::uploadResults( connectionDetails = resultsConnectionDetails, + resultsFolder = private$jobContext$moduleExecutionSettings$resultsSubFolder, schema = resultsDataModelSettings$resultsDatabaseSchema, ... ) From 204aaf96614f5d06f54f9505d10bf84be45c8621 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Fri, 27 Feb 2026 13:27:35 -0800 Subject: [PATCH 30/34] Upload sub folders for each analysis id consistently handled from SCC package --- R/Module-SelfControlledCohort.R | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/R/Module-SelfControlledCohort.R b/R/Module-SelfControlledCohort.R index ec259cba..f930c84a 100644 --- a/R/Module-SelfControlledCohort.R +++ b/R/Module-SelfControlledCohort.R @@ -110,14 +110,19 @@ SelfControlledCohortModule <- R6::R6Class( #' The package handles the specifics of what data needs to be uploaded and how. uploadResults = function(resultsConnectionDetails, analysisSpecifications, resultsDataModelSettings, ...) { super$uploadResults(resultsConnectionDetails, analysisSpecifications, resultsDataModelSettings) + jobContext <- private$jobContext + exportFolder <- private$jobContext$moduleExecutionSettings$resultsSubFolder - # Direct delegation to package function - SelfControlledCohort::uploadResults( - connectionDetails = resultsConnectionDetails, - resultsFolder = private$jobContext$moduleExecutionSettings$resultsSubFolder, - schema = resultsDataModelSettings$resultsDatabaseSchema, - ... + analysisSettings <- jobContext$settings$parameters$analysisSettings + resultsFolders <- SelfControlledCohort::getResultsFolders(analysisSettings, exportFolder) + lapply(resultsFolders, function(resultsFolder) { + SelfControlledCohort::uploadResults(connectionDetails = resultsConnectionDetails, + resultsFolder = resultsFolder, + schema = resultsDataModelSettings$resultsDatabaseSchema, + ...) + } ) + }, #' @description Creates the SelfControlledCohort Module Specifications From cadeae02303753fa0fb3447b935daebcb7e51527 Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Mon, 2 Mar 2026 08:25:26 -0800 Subject: [PATCH 31/34] added SCC module to random CSV file --- inst/csv/hadesModuleList.csv | 1 + 1 file changed, 1 insertion(+) diff --git a/inst/csv/hadesModuleList.csv b/inst/csv/hadesModuleList.csv index c02beeae..2ecf951b 100644 --- a/inst/csv/hadesModuleList.csv +++ b/inst/csv/hadesModuleList.csv @@ -7,4 +7,5 @@ CohortMethodModule EvidenceSynthesisModule PatientLevelPredictionModule SelfControlledCaseSeriesModule +SelfControlledCohortModule TreatmentPatternsModule From 96abd21c14151b23058a5cb4c420c6295d44dd3e Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Mon, 2 Mar 2026 12:48:50 -0800 Subject: [PATCH 32/34] Added SCC to meta-analysis types --- R/Module-EvidenceSynthesis.R | 106 +++++++++++++++++++++++++++-- inst/csv/evidenceSynthesisRdms.csv | 40 ++++++++++- 2 files changed, 141 insertions(+), 5 deletions(-) diff --git a/R/Module-EvidenceSynthesis.R b/R/Module-EvidenceSynthesis.R index fa36b9c9..b1d097cb 100644 --- a/R/Module-EvidenceSynthesis.R +++ b/R/Module-EvidenceSynthesis.R @@ -109,12 +109,12 @@ EvidenceSynthesisModule <- R6::R6Class( #' Create an evidence synthesis source #' #' @param sourceMethod The source method generating the estimates to synthesize. Can be "CohortMethod" or - #' "SelfControlledCaseSeries" + #' "SelfControlledCaseSeries", or "SelfControlledCohort" #' @param databaseIds The database IDs to include. Use `databaseIds = NULL` to include all database IDs. #' @param analysisIds The source method analysis IDs to include. Use `analysisIds = NULL` to include all #' analysis IDs. #' @param likelihoodApproximation The type of likelihood approximation. Can be "adaptive grid", "normal", or "grid - #' with gradients". + #' with gradients". Note: "SelfControlledCohort" only supports "normal". #' #' @return #' An object of type `EvidenceSynthesisSource`. @@ -123,7 +123,7 @@ EvidenceSynthesisModule <- R6::R6Class( analysisIds = NULL, likelihoodApproximation = "grid with gradients") { errorMessages <- checkmate::makeAssertCollection() - checkmate::assertChoice(sourceMethod, c("CohortMethod", "SelfControlledCaseSeries"), add = errorMessages) + checkmate::assertChoice(sourceMethod, c("CohortMethod", "SelfControlledCaseSeries", "SelfControlledCohort"), add = errorMessages) if (is.character(databaseIds)) { checkmate::assertCharacter(databaseIds, null.ok = TRUE, add = errorMessages) } else { @@ -132,6 +132,9 @@ EvidenceSynthesisModule <- R6::R6Class( checkmate::assertIntegerish(analysisIds, null.ok = TRUE, add = errorMessages) checkmate::assertChoice(likelihoodApproximation, c("adaptive grid", "normal", "grid with gradients"), add = errorMessages) checkmate::reportAssertions(collection = errorMessages) + if (sourceMethod == "SelfControlledCohort" && likelihoodApproximation != "normal") { + stop("SelfControlledCohort only supports likelihoodApproximation = 'normal'.") + } analysis <- list() for (name in names(formals(self$createEvidenceSynthesisSource))) { @@ -341,7 +344,9 @@ EvidenceSynthesisModule <- R6::R6Class( "es_cm_shared_covariate_balance", "es_cm_covariate", "es_sccs_result", - "es_sccs_diagnostics_summary" + "es_sccs_diagnostics_summary", + "es_scc_result", + "es_scc_diagnostics_summary" ) invisible(lapply(outputTables, function(x) { private$.ensureEmptyAndExists(x, resultsFolder) @@ -445,12 +450,16 @@ EvidenceSynthesisModule <- R6::R6Class( controlKey <- c("targetComparatorId", "analysisId") } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCaseSeries") { controlKey <- c("exposureId", "nestingCohortId", "covariateId", "analysisId") + } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCohort") { + controlKey <- c("targetCohortId", "analysisId") } } else if (analysisSettings$controlType == "exposure") { if (analysisSettings$evidenceSynthesisSource$sourceMethod == "CohortMethod") { controlKey <- c("outcomeId", "analysisId") } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCaseSeries") { controlKey <- c("outcomeId", "analysisId") + } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCohort") { + controlKey <- c("outcomeCohortId", "analysisId") } } else { stop(sprintf("Unknown control type '%s'", analysisSettings$controlType)) @@ -523,6 +532,8 @@ EvidenceSynthesisModule <- R6::R6Class( fileName <- file.path(resultsFolder, "es_cm_diagnostics_summary.csv") } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCaseSeries") { fileName <- file.path(resultsFolder, "es_sccs_diagnostics_summary.csv") + } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCohort") { + fileName <- file.path(resultsFolder, "es_scc_diagnostics_summary.csv") } else { stop(sprintf("Saving diagnostics summary not implemented for source method '%s'", analysisSettings$evidenceSynthesisSource$sourceMethod)) } @@ -537,6 +548,8 @@ EvidenceSynthesisModule <- R6::R6Class( fileName <- file.path(resultsFolder, "es_cm_result.csv") } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCaseSeries") { fileName <- file.path(resultsFolder, "es_sccs_result.csv") + } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCohort") { + fileName <- file.path(resultsFolder, "es_scc_result.csv") } else { stop(sprintf("Saving results not implemented for source method '%s'", analysisSettings$evidenceSynthesisSource$sourceMethod)) } @@ -695,6 +708,15 @@ EvidenceSynthesisModule <- R6::R6Class( covariateEras = sumMinCellCount(subset$covariateEras, minCellCount), covariateOutcomes = sumMinCellCount(subset$covariateOutcomes, minCellCount) ) + } else if (analysisSettings$evidenceSynthesisSource$sourceMethod == "SelfControlledCohort") { + counts <- tibble( + numPersons = sumMinCellCount(subset$numPersons, minCellCount), + timeAtRiskExposed = sumMinCellCount(subset$timeAtRiskExposed, 0), + timeAtRiskUnexposed = sumMinCellCount(subset$timeAtRiskUnexposed, 0), + numOutcomesExposed = sumMinCellCount(subset$numOutcomesExposed, minCellCount), + numOutcomesUnexposed = sumMinCellCount(subset$numOutcomesUnexposed, minCellCount), + numExposures = sumMinCellCount(subset$numExposures, minCellCount) + ) } else { stop(sprintf("Aggregating counts not implemented for source method '%s'", analysisSettings$evidenceSynthesisSource$sourceMethod)) } @@ -1057,6 +1079,82 @@ EvidenceSynthesisModule <- R6::R6Class( ON sccs_covariate.analysis_id = sccs_covariate_analysis.analysis_id AND sccs_covariate.covariate_analysis_id = sccs_covariate_analysis.covariate_analysis_id WHERE sccs_covariate_analysis.variable_of_interest = 1; + " + trueEffectSizes <- DatabaseConnector::renderTranslateQuerySql( + connection = connection, + sql = sql, + database_schema = databaseSchema, + snakeCaseToCamelCase = TRUE + ) + trueEffectSizes <- trueEffectSizes |> + mutate(trueEffectSize = ifelse(!is.na(.data$trueEffectSize) & .data$trueEffectSize == 0, + NA, + .data$trueEffectSize + )) + } else if (evidenceSynthesisSource$sourceMethod == "SelfControlledCohort") { + key <- c("targetCohortId", "outcomeCohortId") + databaseIds <- evidenceSynthesisSource$databaseIds + analysisIds <- evidenceSynthesisSource$analysisIds + if (evidenceSynthesisSource$likelihoodApproximation != "normal") { + stop("SelfControlledCohort only supports 'normal' likelihood approximation for evidence synthesis.") + } + sql <- "SELECT scc_result.*, + diag.unblind_for_evidence_synthesis AS unblind + FROM @database_schema.scc_result + LEFT JOIN ( + SELECT database_id, + analysis_id, + target_cohort_id, + outcome_cohort_id, + pass AS unblind_for_evidence_synthesis + FROM @database_schema.scc_diagnostics_summary + WHERE diagnostic_name = 'UNBLIND' + ) diag + ON scc_result.database_id = diag.database_id + AND scc_result.analysis_id = diag.analysis_id + AND scc_result.target_cohort_id = diag.target_cohort_id + AND scc_result.outcome_cohort_id = diag.outcome_cohort_id + {@database_ids != ''| @analysis_ids != ''} ? {WHERE} + {@database_ids != ''} ? { scc_result.database_id IN (@database_ids)} + {@analysis_ids != ''} ? { {@database_ids != ''} ? {AND} scc_result.analysis_id IN (@analysis_ids)}; + " + estimates <- DatabaseConnector::renderTranslateQuerySql( + connection = connection, + sql = sql, + database_schema = databaseSchema, + database_ids = if (is.null(databaseIds)) "" else private$.quoteSql(databaseIds), + analysis_ids = if (is.null(analysisIds)) "" else analysisIds, + snakeCaseToCamelCase = TRUE + ) |> + as_tibble() + + # Temp hack: detect NA values that have been converted to 0 in the DB: + idx <- estimates$seLogRr == 0 + estimates$logRr[idx] <- NA + estimates$seLogRr[idx] <- NA + estimates$pValue[idx] <- NA + + # Rename columns to match the expected names used downstream + estimates <- estimates |> + rename( + ci95Lb = .data$lb95, + ci95Ub = .data$ub95, + p = .data$pValue + ) + + llApproximations <- estimates |> + filter(!is.na(.data$unblind) & .data$unblind == 1) |> + select( + "targetCohortId", + "outcomeCohortId", + "analysisId", + "databaseId", + "logRr", + "seLogRr" + ) + + sql <- "SELECT * + FROM @database_schema.scc_outcome_exposure; " trueEffectSizes <- DatabaseConnector::renderTranslateQuerySql( connection = connection, diff --git a/inst/csv/evidenceSynthesisRdms.csv b/inst/csv/evidenceSynthesisRdms.csv index 5dd57be0..8645c60f 100644 --- a/inst/csv/evidenceSynthesisRdms.csv +++ b/inst/csv/evidenceSynthesisRdms.csv @@ -119,4 +119,42 @@ es_sccs_result,calibrated_se_log_rr,float,No,No,No,The standard error of the log es_sccs_result,pi_95_lb,float,No,No,No,The lower bound of the 95% prediction interval of the relative risk. es_sccs_result,pi_95_ub,float,No,No,No,The upper bound of the 95% prediction interval of the relative risk. es_sccs_result,calibrated_pi_95_lb,float,No,No,No,The lower bound of the calibrated 95% prediction interval of the relative risk. -es_sccs_result,calibrated_pi_95_ub,float,No,No,No,The upper bound of the calibrated 95% prediction interval of the relative risk. \ No newline at end of file +es_sccs_result,calibrated_pi_95_ub,float,No,No,No,The upper bound of the calibrated 95% prediction interval of the relative risk. +es_scc_diagnostics_summary,target_cohort_id,bigint,Yes,Yes,No,A foreign key referencing the target cohort. +es_scc_diagnostics_summary,outcome_cohort_id,bigint,Yes,Yes,No,The identifier for the outcome cohort. +es_scc_diagnostics_summary,analysis_id,int,Yes,Yes,No,A unique identifier for the self-controlled cohort analysis. +es_scc_diagnostics_summary,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. +es_scc_diagnostics_summary,mdrr,float,No,No,No,The minimum detectable relative risk. +es_scc_diagnostics_summary,i_2,float,No,No,No,The I2 statistic for heterogeneity. +es_scc_diagnostics_summary,tau,float,No,No,No,The estimated tau (standard deviation of the random-effects distribution). +es_scc_diagnostics_summary,ease,float,No,No,No,The expected absolute systematic error. +es_scc_diagnostics_summary,mdrr_diagnostic,varchar(13),Yes,No,No,PASS / NOT EVALUATED / FAIL classification of the MDRR diagnostic. +es_scc_diagnostics_summary,i_2_diagnostic,varchar(13),Yes,No,No,PASS / NOT EVALUATED / FAIL classification of the I2 diagnostic. +es_scc_diagnostics_summary,tau_diagnostic,varchar(13),Yes,No,No,PASS / NOT EVALUATED / FAIL classification of the tau diagnostic. +es_scc_diagnostics_summary,ease_diagnostic,varchar(13),Yes,No,No,PASS / NOT EVALUATED / FAIL classification of the EASE diagnostic. +es_scc_diagnostics_summary,unblind,int,Yes,No,No,"Is unblinding the result recommended? (1 = yes, 0 = no)" +es_scc_result,target_cohort_id,bigint,Yes,Yes,No,A foreign key referencing the target cohort. +es_scc_result,outcome_cohort_id,bigint,Yes,Yes,No,The identifier for the outcome cohort. +es_scc_result,analysis_id,int,Yes,Yes,No,A unique identifier for the self-controlled cohort analysis. +es_scc_result,evidence_synthesis_analysis_id,int,Yes,Yes,No,A foreign key referencing the es_analysis table. +es_scc_result,rr,float,No,No,No,The estimated relative risk (i.e. the incidence rate ratio). +es_scc_result,ci_95_lb,float,No,No,No,The lower bound of the 95% confidence interval of the relative risk. +es_scc_result,ci_95_ub,float,No,No,No,The upper bound of the 95% confidence interval of the relative risk. +es_scc_result,p,float,No,No,No,The two-sided p-value considering the null hypothesis of no effect. +es_scc_result,one_sided_p,float,No,No,No,The one-sided p-value considering the null hypothesis of RR <= 1. +es_scc_result,log_rr,float,No,No,No,The log of the relative risk. +es_scc_result,se_log_rr,float,No,No,No,The standard error of the log of the relative risk. +es_scc_result,num_persons,int,No,No,Yes,The number of persons in the analysis. +es_scc_result,time_at_risk_exposed,bigint,No,No,No,Time at risk while exposed. +es_scc_result,time_at_risk_unexposed,bigint,No,No,No,Time at risk while unexposed. +es_scc_result,num_outcomes_exposed,int,No,No,Yes,The number of outcomes observed during exposed time. +es_scc_result,num_outcomes_unexposed,int,No,No,Yes,The number of outcomes observed during unexposed time. +es_scc_result,num_exposures,int,No,No,Yes,The number of exposures. +es_scc_result,n_databases,int,Yes,No,No,The number of databases that contributed to the meta-analytic estimate. +es_scc_result,calibrated_rr,float,No,No,No,The calibrated relative risk. +es_scc_result,calibrated_ci_95_lb,float,No,No,No,The lower bound of the calibrated 95% confidence interval of the relative risk. +es_scc_result,calibrated_ci_95_ub,float,No,No,No,The upper bound of the calibrated 95% confidence interval of the relative risk. +es_scc_result,calibrated_p,float,No,No,No,The calibrated two-sided p-value. +es_scc_result,calibrated_one_sided_p,float,No,No,No,The calibrated one-sided p-value considering the null hypothesis of RR <= 1. +es_scc_result,calibrated_log_rr,float,No,No,No,The log of the calibrated relative risk. +es_scc_result,calibrated_se_log_rr,float,No,No,No,The standard error of the log of the calibrated relative risk. From 4b5a4dbbd051f007b456444d369434319b890e2c Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Thu, 28 May 2026 08:31:34 -0700 Subject: [PATCH 33/34] Updated DESCRIPTION to remove remote dependency (package added to CRAN) --- DESCRIPTION | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 82c2cd59..11d95ee6 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -61,9 +61,7 @@ Suggests: Remotes: ohdsi/Characterization, ohdsi/CohortDiagnostics, - ohdsi/CohortIncidence, - ohdsi/CohortMethod@version_6, - ohdsi/SelfControlledCohort@version-2.0.0 + ohdsi/CohortIncidence License: Apache License 2.0 VignetteBuilder: knitr URL: https://ohdsi.github.io/Strategus, https://github.com/OHDSI/Strategus From b690e08f1c91a5c82fcf7d3e402e7febdc02536f Mon Sep 17 00:00:00 2001 From: jgilber2 Date: Thu, 18 Jun 2026 08:41:21 -0700 Subject: [PATCH 34/34] Add unit tests for SCC --- .../test-SelfControlledCohortModule.R | 88 +++++++++++++++++++ 1 file changed, 88 insertions(+) create mode 100644 tests/testthat/test-SelfControlledCohortModule.R diff --git a/tests/testthat/test-SelfControlledCohortModule.R b/tests/testthat/test-SelfControlledCohortModule.R new file mode 100644 index 00000000..1c0c4037 --- /dev/null +++ b/tests/testthat/test-SelfControlledCohortModule.R @@ -0,0 +1,88 @@ +library(testthat) +library(Strategus) + +test_that("SelfControlledCohortModule can be instantiated", { + sccModule <- SelfControlledCohortModule$new() + expect_s3_class(sccModule, "SelfControlledCohortModule") + expect_equal(sccModule$tablePrefix, "scc_") +}) + +test_that("SelfControlledCohortModule - create module settings requires sccAnalysesSpecifications", { + sccModule <- SelfControlledCohortModule$new() + + # Missing sccAnalysesSpecifications parameter should cause an error + expect_error( + sccModule$createModuleSpecifications() + ) +}) + +test_that("SelfControlledCohortModule - create module settings with empty list", { + sccModule <- SelfControlledCohortModule$new() + + # Empty list should be accepted as valid input + # (The actual validation happens in the SelfControlledCohort package) + specs <- sccModule$createModuleSpecifications( + sccAnalysesSpecifications = list() + ) + expect_s3_class(specs, "SelfControlledCohortModuleSpecifications") + expect_true("settings" %in% names(specs)) + expect_true("module" %in% names(specs)) +}) + +test_that("SelfControlledCohortModule - module specifications have correct structure", { + sccModule <- SelfControlledCohortModule$new() + + specs <- sccModule$createModuleSpecifications( + sccAnalysesSpecifications = list() + ) + + # Verify the specification structure + expect_s3_class(specs, "SelfControlledCohortModuleSpecifications") + expect_true("settings" %in% names(specs)) + expect_true("module" %in% names(specs)) + expect_equal(specs$module, "SelfControlledCohortModule") +}) + +test_that("SelfControlledCohortModule - validateModuleSpecifications works", { + sccModule <- SelfControlledCohortModule$new() + + specs <- sccModule$createModuleSpecifications( + sccAnalysesSpecifications = list() + ) + + # Validation should complete without error for valid specs + expect_no_error( + sccModule$validateModuleSpecifications( + moduleSpecifications = specs + ) + ) +}) + +test_that("SelfControlledCohortModule - getResultsDataModelSpecification returns data model", { + sccModule <- SelfControlledCohortModule$new() + + # Get the data model specification + dataModel <- sccModule$getResultsDataModelSpecification() + + # Verify it is a data frame with required columns + expect_s3_class(dataModel, "data.frame") + expect_true("tableName" %in% names(dataModel)) + expect_true("columnName" %in% names(dataModel)) + + # Verify table names have the correct prefix + expect_true(all(startsWith(unique(dataModel$tableName), "scc_"))) +}) + +test_that("SelfControlledCohortModule - getResultsDataModelSpecification with custom prefix", { + sccModule <- SelfControlledCohortModule$new() + + # Get the data model specification with a custom prefix + dataModel <- sccModule$getResultsDataModelSpecification(tablePrefix = "custom_") + + # Verify it is a data frame + expect_s3_class(dataModel, "data.frame") + + # Verify table names have the custom prefix combined with scc_ prefix + expect_true(all(startsWith(unique(dataModel$tableName), "custom_scc_"))) +}) +
Inherited methods + +

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