From f23d1224dc20fe4e79f47332c6269b2aa8b18d91 Mon Sep 17 00:00:00 2001 From: Maarten van Kessel Date: Tue, 3 Oct 2023 16:30:09 +0200 Subject: [PATCH 1/6] minor check updates --- R/OutcomeModels.R | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/R/OutcomeModels.R b/R/OutcomeModels.R index ab1b403f..00283afd 100644 --- a/R/OutcomeModels.R +++ b/R/OutcomeModels.R @@ -80,6 +80,10 @@ fitOutcomeModel <- function(population, )) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertDataFrame(population, null.ok = TRUE, add = errorMessages) + checkmate::assertNames(names(population), must.include = c("rowId", "outcomeCount", "treatment", "timeAtRisk", "personSeqId"), add = errorMessages) + if (!is.null(cohortMethodData)) { + checkmate::assertNames(names(cohortMethodData), must.include = c("analysisRef", "cohorts", "covariateRef", "covariates", "outcomes"), add = errorMessages) + } checkmate::assertClass(cohortMethodData, "CohortMethodData", null.ok = TRUE, add = errorMessages) checkmate::assertChoice(modelType, c("logistic", "poisson", "cox"), add = errorMessages) checkmate::assertLogical(stratified, len = 1, add = errorMessages) @@ -96,9 +100,6 @@ fitOutcomeModel <- function(population, if (stratified && nrow(population) > 0 && is.null(population$stratumId)) { stop("Requested stratified analysis, but no stratumId column found in population. Please use matchOnPs or stratifyByPs to create strata.") } - if (is.null(population$outcomeCount)) { - stop("No outcome variable found in population object. Use createStudyPopulation to create variable.") - } if (is.null(cohortMethodData) && useCovariates) { stop("Requested all covariates for model, but no cohortMethodData object specified") } From fad7972bf351f661e4560d17f5037ac21521492c Mon Sep 17 00:00:00 2001 From: Maarten van Kessel Date: Tue, 3 Oct 2023 16:30:17 +0200 Subject: [PATCH 2/6] Added setup and tests --- tests/testthat/setup-fitOutcomeModel.R | 44 ++++ tests/testthat/test-fitOutcomeModel.R | 307 +++++++++++++++++++++++++ 2 files changed, 351 insertions(+) create mode 100644 tests/testthat/setup-fitOutcomeModel.R create mode 100644 tests/testthat/test-fitOutcomeModel.R diff --git a/tests/testthat/setup-fitOutcomeModel.R b/tests/testthat/setup-fitOutcomeModel.R new file mode 100644 index 00000000..ab4de846 --- /dev/null +++ b/tests/testthat/setup-fitOutcomeModel.R @@ -0,0 +1,44 @@ +# Study Population ---- +nsaids <- c(1118084, 1124300) + +covSettings <- createDefaultCovariateSettings( + excludedCovariateConceptIds = nsaids, + addDescendantsToExclude = TRUE +) + +sCohortMethodData <- getDbCohortMethodData( + connectionDetails = connectionDetails, + cdmDatabaseSchema = "main", + targetId = 1, + comparatorId = 2, + outcomeIds = c(3, 4), + exposureDatabaseSchema = "main", + outcomeDatabaseSchema = "main", + exposureTable = "cohort", + outcomeTable = "cohort", + covariateSettings = covSettings +) + +studyPop <- createStudyPopulation( + cohortMethodData = sCohortMethodData, + outcomeId = 3, + riskWindowEnd = 99999 +) + +# CohortMethod Data ---- +sCohortMethodData <- getDbCohortMethodData( + connectionDetails = connectionDetails, + cdmDatabaseSchema = "main", + targetId = 1, + comparatorId = 2, + outcomeIds = c(3, 4), + exposureDatabaseSchema = "main", + outcomeDatabaseSchema = "main", + exposureTable = "cohort", + outcomeTable = "cohort", + covariateSettings = covSettings +) + +ps <- createPs(cohortMethodData = sCohortMethodData, population = studyPop) +studyPopStratisfied <- stratifyByPs(ps, 5) +studyPopMatched <- matchOnPs(population = ps) diff --git a/tests/testthat/test-fitOutcomeModel.R b/tests/testthat/test-fitOutcomeModel.R new file mode 100644 index 00000000..c7169d24 --- /dev/null +++ b/tests/testthat/test-fitOutcomeModel.R @@ -0,0 +1,307 @@ +library(CohortMethod) +library(testthat) +library(dplyr) + +test_that("population", { + ## Defaults ---- + mod <- fitOutcomeModel( + population = studyPop + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Wrong data ---- + expect_error( + fitOutcomeModel( + population = iris + ), + "Names must include the elements" + ) + + ## 0 rows ---- + emptyPop <- studyPop %>% + filter(row_number() == 0) + + expect_output( + fitOutcomeModel( + population = emptyPop + ), + "NO SUBJECTS IN POPULATION, CANNOT FIT" + ) + + ## 1 rows ---- + expect_output( + fitOutcomeModel( + population = studyPop[sample(nrow(studyPop), 1), ] + ), + "NO OUTCOMES FOUND FOR POPULATION, CANNOT FIT" + ) + # n >= 2 rows will randomly fit, likeliness increases with greater n rows. + + ## Not data.frame ---- + expect_error( + fitOutcomeModel( + population = "studyPop" + ), + "not 'character'" + ) + + ## Minimal columns ---- + mod <- fitOutcomeModel( + population = studyPop[, c( + "rowId", "outcomeCount", "treatment", "timeAtRisk", "personSeqId")] + ) + + expect_s3_class(mod, "OutcomeModel") + + ## random columns ---- + expect_error( + fitOutcomeModel( + population = studyPop[, c(2:7)] + ), + "Names must include the elements" + ) +}) + +test_that("cohortMethodData", { + ## Defaults ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Wrong data ---- + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = iris + ), + "Must inherit from class 'CohortMethodData'" + ) + + ## Empty CohortMethodData ---- + empty <- list() + class(empty) <- "CohortMethodData" + + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = empty + ), + "Must have names" + ) + + ## Malformed CohortMethodData ---- + malformed <- list( + analysisRef = sCohortMethodData$analysisRef, + cohorts = sCohortMethodData$cohorts + ) + + class(malformed) <- "CohortMethodData" + + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = malformed + ), + "'analysisRef','cohorts','covariateRef','covariates','outcomes'" + ) +}) + +test_that("modelType", { + ## logistic ---- + logistic <- fitOutcomeModel( + population = studyPop, + modelType = "logistic" + ) + + expect_identical(logistic$outcomeModelType, "logistic") + + ## poisson ---- + poisson <- fitOutcomeModel( + population = studyPop, + modelType = "poisson" + ) + + expect_identical(poisson$outcomeModelType, "poisson") + + ## cox ---- + cox <- fitOutcomeModel( + population = studyPop, + modelType = "cox" + ) + + expect_identical(cox$outcomeModelType, "cox") + + ## linear model ---- + expect_error( + fitOutcomeModel( + population = studyPop, + modelType = "linear" + ), + "'logistic','poisson','cox'" + ) + + ## NULL ---- + expect_error( + fitOutcomeModel( + population = studyPop, + modelType = NULL + ), + "'logistic','poisson','cox'" + ) + + ## NA ---- + expect_error( + fitOutcomeModel( + population = studyPop, + modelType = NA + ), + "'logistic','poisson','cox'" + ) +}) + +test_that("stratified", { + ## FALSE ---- + modFalse1 <- fitOutcomeModel( + population = studyPop, + stratified = FALSE + ) + + modFalse2 <- fitOutcomeModel( + population = studyPopStratisfied, + stratified = FALSE + ) + + modFalse3 <- fitOutcomeModel( + population = studyPopMatched, + stratified = FALSE + ) + + expect_s3_class(modFalse1, "OutcomeModel") + expect_s3_class(modFalse2, "OutcomeModel") + expect_s3_class(modFalse3, "OutcomeModel") + + # Estimates + expect_identical( + modFalse1$outcomeModelTreatmentEstimate, + modFalse2$outcomeModelTreatmentEstimate + ) + + expect_false(identical( + modFalse2$outcomeModelTreatmentEstimate, + modFalse3$outcomeModelTreatmentEstimate + )) + + # LogLikelihoodProfile + expect_identical( + modFalse1$logLikelihoodProfile, + modFalse2$logLikelihoodProfile + ) + + expect_false(identical( + modFalse2$logLikelihoodProfile, + modFalse3$logLikelihoodProfile + )) + + # outcomeCounts + expect_identical( + modFalse1$outcomeCounts, + modFalse2$outcomeCounts + ) + + expect_false(identical( + modFalse2$outcomeCounts, + modFalse3$outcomeCounts + )) + + ## TRUE ---- + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + stratified = TRUE + )), + "matchOnPs or stratifyByPs" + ) + + expect_error( + suppressWarnings(fitOutcomeModel( + population = ps, + stratified = TRUE + )), + "matchOnPs or stratifyByPs" + ) + + modTrue1 <- fitOutcomeModel( + population = studyPopStratisfied, + stratified = TRUE + ) + + modTrue2 <- fitOutcomeModel( + population = studyPopMatched, + stratified = TRUE + ) + + expect_s3_class(modTrue1, "OutcomeModel") + expect_s3_class(modTrue2, "OutcomeModel") + + expect_false(identical( + modTrue1$outcomeModelTreatmentEstimate, + modTrue2$outcomeModelTreatmentEstimate + )) + + expect_false(identical( + modTrue1$logLikelihoodProfile, + modTrue2$logLikelihoodProfile + )) + + expect_false(identical( + modTrue1$outcomeCounts, + modTrue2$outcomeCounts + )) +}) + +test_that("useCovariates", { + ## FALSE ---- + modFalse <- fitOutcomeModel( + population = studyPopStratisfied, + useCovariates = FALSE + ) + + expect_s3_class(modFalse, "OutcomeModel") + + ## TRUE ---- + expect_error( + fitOutcomeModel( + population = studyPopStratisfied, + useCovariates = TRUE + ), + "cohortMethodData" + ) + + modTrue <- fitOutcomeModel( + population = studyPopStratisfied, + cohortMethodData = sCohortMethodData, + useCovariates = TRUE + ) + + expect_s3_class(modTrue, "OutcomeModel") + expect_false(identical(modTrue, modFalse)) + + expect_false(identical( + modTrue$logLikelihoodProfile, + modFalse$logLikelihoodProfile + )) + + expect_false(identical( + modTrue$outcomeModelTreatmentEstimate, + modFalse$outcomeModelTreatmentEstimate + )) + + expect_identical( + modTrue$outcomeCounts, + modFalse$outcomeCounts + ) +}) From 331db00dfbac739577a720c3f3e3da0531253d8c Mon Sep 17 00:00:00 2001 From: Maarten van Kessel Date: Wed, 4 Oct 2023 10:35:30 +0200 Subject: [PATCH 3/6] Fixes to pass r check --- tests/testthat/setup-fitOutcomeModel.R | 44 ----- tests/testthat/setup-runCmAnalyses.R | 167 ------------------- tests/testthat/setup.R | 212 +++++++++++++++++++++++++ tests/testthat/test-fitOutcomeModel.R | 6 +- tests/testthat/testthat-problems.rds | Bin 415694 -> 0 bytes 5 files changed, 215 insertions(+), 214 deletions(-) delete mode 100644 tests/testthat/setup-fitOutcomeModel.R delete mode 100644 tests/testthat/setup-runCmAnalyses.R delete mode 100644 tests/testthat/testthat-problems.rds diff --git a/tests/testthat/setup-fitOutcomeModel.R b/tests/testthat/setup-fitOutcomeModel.R deleted file mode 100644 index ab4de846..00000000 --- a/tests/testthat/setup-fitOutcomeModel.R +++ /dev/null @@ -1,44 +0,0 @@ -# Study Population ---- -nsaids <- c(1118084, 1124300) - -covSettings <- createDefaultCovariateSettings( - excludedCovariateConceptIds = nsaids, - addDescendantsToExclude = TRUE -) - -sCohortMethodData <- getDbCohortMethodData( - connectionDetails = connectionDetails, - cdmDatabaseSchema = "main", - targetId = 1, - comparatorId = 2, - outcomeIds = c(3, 4), - exposureDatabaseSchema = "main", - outcomeDatabaseSchema = "main", - exposureTable = "cohort", - outcomeTable = "cohort", - covariateSettings = covSettings -) - -studyPop <- createStudyPopulation( - cohortMethodData = sCohortMethodData, - outcomeId = 3, - riskWindowEnd = 99999 -) - -# CohortMethod Data ---- -sCohortMethodData <- getDbCohortMethodData( - connectionDetails = connectionDetails, - cdmDatabaseSchema = "main", - targetId = 1, - comparatorId = 2, - outcomeIds = c(3, 4), - exposureDatabaseSchema = "main", - outcomeDatabaseSchema = "main", - exposureTable = "cohort", - outcomeTable = "cohort", - covariateSettings = covSettings -) - -ps <- createPs(cohortMethodData = sCohortMethodData, population = studyPop) -studyPopStratisfied <- stratifyByPs(ps, 5) -studyPopMatched <- matchOnPs(population = ps) diff --git a/tests/testthat/setup-runCmAnalyses.R b/tests/testthat/setup-runCmAnalyses.R deleted file mode 100644 index accdc200..00000000 --- a/tests/testthat/setup-runCmAnalyses.R +++ /dev/null @@ -1,167 +0,0 @@ -# Setup ---- -library(CohortMethod) - -outputFolder <- tempfile(pattern = "cmData") - -covarSettings <- createDefaultCovariateSettings(addDescendantsToExclude = TRUE) - -getDbCmDataArgs <- createGetDbCohortMethodDataArgs( - washoutPeriod = 183, - firstExposureOnly = TRUE, - removeDuplicateSubjects = "remove all", - covariateSettings = covarSettings -) - -createPsArgs <- createCreatePsArgs( - prior = createPrior("laplace", variance = 0.01), - estimator = "att" -) - -matchOnPsArgs <- createMatchOnPsArgs(maxRatio = 100) - -computeSharedCovBalArgs <- createComputeCovariateBalanceArgs() - -computeCovBalArgs <- createComputeCovariateBalanceArgs( - covariateFilter = FeatureExtraction::getDefaultTable1Specifications() -) - -truncateIptwArgs <- createTruncateIptwArgs(maxWeight = 10) - -tcos1 <- createTargetComparatorOutcomes( - targetId = 1, - comparatorId = 2, - outcomes = list( - createOutcome( - outcomeId = 3, - priorOutcomeLookback = 30 - ), - createOutcome( - outcomeId = 4, - outcomeOfInterest = FALSE, - trueEffectSize = 1 - ) - ), - excludedCovariateConceptIds = c(1118084, 1124300) -) -# Empty cohorts: -tcos2 <- createTargetComparatorOutcomes( - targetId = 998, - comparatorId = 999, - outcomes = list( - createOutcome( - outcomeId = 3, - priorOutcomeLookback = 30 - ), - createOutcome( - outcomeId = 4, - outcomeOfInterest = FALSE, - trueEffectSize = 1 - ) - ) -) - -targetComparatorOutcomesList <- list(tcos1, tcos2) - -analysesToExclude <- data.frame( - targetId = c(998, 998), - analysisId = c(3, 4) -) - -createStudyPopArgs1 <- createCreateStudyPopulationArgs( - removeSubjectsWithPriorOutcome = TRUE, - firstExposureOnly = TRUE, - restrictToCommonPeriod = TRUE, - removeDuplicateSubjects = "remove all", - washoutPeriod = 183, - censorAtNewRiskWindow = TRUE, - minDaysAtRisk = 1, - riskWindowStart = 0, - startAnchor = "cohort start", - riskWindowEnd = 30, - endAnchor = "cohort end" -) - -createStudyPopArgs2 <- createCreateStudyPopulationArgs( - removeSubjectsWithPriorOutcome = TRUE, - firstExposureOnly = TRUE, - restrictToCommonPeriod = TRUE, - removeDuplicateSubjects = "keep first", - washoutPeriod = 183, - censorAtNewRiskWindow = TRUE, - minDaysAtRisk = 1, - riskWindowStart = 0, - startAnchor = "cohort start", - riskWindowEnd = 30, - endAnchor = "cohort end" -) - -## Analysis 1 ---- -fitOutcomeModelArgs1 <- createFitOutcomeModelArgs( - modelType = "cox" -) - -cmAnalysis1 <- createCmAnalysis( - analysisId = 1, - description = "No matching, simple outcome model", - getDbCohortMethodDataArgs = getDbCmDataArgs, - createStudyPopArgs = createStudyPopArgs1, - fitOutcomeModelArgs = fitOutcomeModelArgs1 -) - -## Analysis 2 ---- -fitOutcomeModelArgs2 <- createFitOutcomeModelArgs( - modelType = "cox", - stratified = TRUE -) - -cmAnalysis2 <- createCmAnalysis( - analysisId = 2, - description = "Matching", - getDbCohortMethodDataArgs = getDbCmDataArgs, - createStudyPopArgs = createStudyPopArgs2, - createPsArgs = createPsArgs, - matchOnPsArgs = matchOnPsArgs, - computeSharedCovariateBalanceArgs = computeSharedCovBalArgs, - computeCovariateBalanceArgs = computeCovBalArgs, - fitOutcomeModelArgs = fitOutcomeModelArgs2 -) - -## Analysis 3 ---- -fitOutcomeModelArgs3 <- createFitOutcomeModelArgs( - modelType = "cox", - inversePtWeighting = TRUE -) -cmAnalysis3 <- createCmAnalysis( - analysisId = 3, - description = "IPTW", - getDbCohortMethodDataArgs = getDbCmDataArgs, - createStudyPopArgs = createStudyPopArgs2, - createPsArgs = createPsArgs, - truncateIptwArgs = truncateIptwArgs, - computeSharedCovariateBalanceArgs = computeSharedCovBalArgs, - fitOutcomeModelArgs = fitOutcomeModelArgs3 -) - -## Analysis 4 ---- -fitOutcomeModelArgs4 <- createFitOutcomeModelArgs( - modelType = "cox", - stratified = TRUE, - interactionCovariateIds = 8532001 -) - -cmAnalysis4 <- createCmAnalysis( - analysisId = 4, - description = "Matching with gender interaction", - getDbCohortMethodDataArgs = getDbCmDataArgs, - createStudyPopArgs = createStudyPopArgs2, - createPsArgs = createPsArgs, - matchOnPsArgs = matchOnPsArgs, - fitOutcomeModelArgs = fitOutcomeModelArgs4 -) - -cmAnalysisList <- list(cmAnalysis1, cmAnalysis2, cmAnalysis3, cmAnalysis4) - -# Clean-up ---- -withr::defer({ - unlink(outputFolder) -}) diff --git a/tests/testthat/setup.R b/tests/testthat/setup.R index 06a92c4c..9da2a3a9 100644 --- a/tests/testthat/setup.R +++ b/tests/testthat/setup.R @@ -1,9 +1,221 @@ library(Eunomia) +library(CohortMethod) + connectionDetails <- getEunomiaConnectionDetails() Eunomia::createCohorts(connectionDetails) +# fitOutcomeModel ---- +## Study Population ---- +nsaids <- c(1118084, 1124300) + +covSettings <- createDefaultCovariateSettings( + excludedCovariateConceptIds = nsaids, + addDescendantsToExclude = TRUE +) + +sCohortMethodData <- getDbCohortMethodData( + connectionDetails = connectionDetails, + cdmDatabaseSchema = "main", + targetId = 1, + comparatorId = 2, + outcomeIds = c(3, 4), + exposureDatabaseSchema = "main", + outcomeDatabaseSchema = "main", + exposureTable = "cohort", + outcomeTable = "cohort", + covariateSettings = covSettings +) + +studyPop <- createStudyPopulation( + cohortMethodData = sCohortMethodData, + outcomeId = 3, + riskWindowEnd = 99999 +) + +## CohortMethod Data ---- +sCohortMethodData <- getDbCohortMethodData( + connectionDetails = connectionDetails, + cdmDatabaseSchema = "main", + targetId = 1, + comparatorId = 2, + outcomeIds = c(3, 4), + exposureDatabaseSchema = "main", + outcomeDatabaseSchema = "main", + exposureTable = "cohort", + outcomeTable = "cohort", + covariateSettings = covSettings +) + +ps <- createPs(cohortMethodData = sCohortMethodData, population = studyPop) +studyPopStratisfied <- stratifyByPs(ps, 5) +studyPopMatched <- matchOnPs(population = ps) + +## runCmAnalyses ---- +outputFolder <- tempfile(pattern = "cmData") + +covarSettings <- createDefaultCovariateSettings(addDescendantsToExclude = TRUE) + +getDbCmDataArgs <- createGetDbCohortMethodDataArgs( + washoutPeriod = 183, + firstExposureOnly = TRUE, + removeDuplicateSubjects = "remove all", + covariateSettings = covarSettings +) + +createPsArgs <- createCreatePsArgs( + prior = createPrior("laplace", variance = 0.01), + estimator = "att" +) + +matchOnPsArgs <- createMatchOnPsArgs(maxRatio = 100) + +computeSharedCovBalArgs <- createComputeCovariateBalanceArgs() + +computeCovBalArgs <- createComputeCovariateBalanceArgs( + covariateFilter = FeatureExtraction::getDefaultTable1Specifications() +) + +truncateIptwArgs <- createTruncateIptwArgs(maxWeight = 10) + +tcos1 <- createTargetComparatorOutcomes( + targetId = 1, + comparatorId = 2, + outcomes = list( + createOutcome( + outcomeId = 3, + priorOutcomeLookback = 30 + ), + createOutcome( + outcomeId = 4, + outcomeOfInterest = FALSE, + trueEffectSize = 1 + ) + ), + excludedCovariateConceptIds = c(1118084, 1124300) +) +# Empty cohorts: +tcos2 <- createTargetComparatorOutcomes( + targetId = 998, + comparatorId = 999, + outcomes = list( + createOutcome( + outcomeId = 3, + priorOutcomeLookback = 30 + ), + createOutcome( + outcomeId = 4, + outcomeOfInterest = FALSE, + trueEffectSize = 1 + ) + ) +) + +targetComparatorOutcomesList <- list(tcos1, tcos2) + +analysesToExclude <- data.frame( + targetId = c(998, 998), + analysisId = c(3, 4) +) + +createStudyPopArgs1 <- createCreateStudyPopulationArgs( + removeSubjectsWithPriorOutcome = TRUE, + firstExposureOnly = TRUE, + restrictToCommonPeriod = TRUE, + removeDuplicateSubjects = "remove all", + washoutPeriod = 183, + censorAtNewRiskWindow = TRUE, + minDaysAtRisk = 1, + riskWindowStart = 0, + startAnchor = "cohort start", + riskWindowEnd = 30, + endAnchor = "cohort end" +) + +createStudyPopArgs2 <- createCreateStudyPopulationArgs( + removeSubjectsWithPriorOutcome = TRUE, + firstExposureOnly = TRUE, + restrictToCommonPeriod = TRUE, + removeDuplicateSubjects = "keep first", + washoutPeriod = 183, + censorAtNewRiskWindow = TRUE, + minDaysAtRisk = 1, + riskWindowStart = 0, + startAnchor = "cohort start", + riskWindowEnd = 30, + endAnchor = "cohort end" +) + +### Analysis 1 ---- +fitOutcomeModelArgs1 <- createFitOutcomeModelArgs( + modelType = "cox" +) + +cmAnalysis1 <- createCmAnalysis( + analysisId = 1, + description = "No matching, simple outcome model", + getDbCohortMethodDataArgs = getDbCmDataArgs, + createStudyPopArgs = createStudyPopArgs1, + fitOutcomeModelArgs = fitOutcomeModelArgs1 +) + +### Analysis 2 ---- +fitOutcomeModelArgs2 <- createFitOutcomeModelArgs( + modelType = "cox", + stratified = TRUE +) + +cmAnalysis2 <- createCmAnalysis( + analysisId = 2, + description = "Matching", + getDbCohortMethodDataArgs = getDbCmDataArgs, + createStudyPopArgs = createStudyPopArgs2, + createPsArgs = createPsArgs, + matchOnPsArgs = matchOnPsArgs, + computeSharedCovariateBalanceArgs = computeSharedCovBalArgs, + computeCovariateBalanceArgs = computeCovBalArgs, + fitOutcomeModelArgs = fitOutcomeModelArgs2 +) + +### Analysis 3 ---- +fitOutcomeModelArgs3 <- createFitOutcomeModelArgs( + modelType = "cox", + inversePtWeighting = TRUE +) +cmAnalysis3 <- createCmAnalysis( + analysisId = 3, + description = "IPTW", + getDbCohortMethodDataArgs = getDbCmDataArgs, + createStudyPopArgs = createStudyPopArgs2, + createPsArgs = createPsArgs, + truncateIptwArgs = truncateIptwArgs, + computeSharedCovariateBalanceArgs = computeSharedCovBalArgs, + fitOutcomeModelArgs = fitOutcomeModelArgs3 +) + +### Analysis 4 ---- +fitOutcomeModelArgs4 <- createFitOutcomeModelArgs( + modelType = "cox", + stratified = TRUE, + interactionCovariateIds = 8532001 +) + +cmAnalysis4 <- createCmAnalysis( + analysisId = 4, + description = "Matching with gender interaction", + getDbCohortMethodDataArgs = getDbCmDataArgs, + createStudyPopArgs = createStudyPopArgs2, + createPsArgs = createPsArgs, + matchOnPsArgs = matchOnPsArgs, + fitOutcomeModelArgs = fitOutcomeModelArgs4 +) + +cmAnalysisList <- list(cmAnalysis1, cmAnalysis2, cmAnalysis3, cmAnalysis4) + +## Clean-up ---- withr::defer( { + unlink(outputFolder) + # Remove the Eunomia database: unlink(connectionDetails$server()) if (getOption("use.devtools.sql_shim", FALSE)) { diff --git a/tests/testthat/test-fitOutcomeModel.R b/tests/testthat/test-fitOutcomeModel.R index c7169d24..1b7c83b0 100644 --- a/tests/testthat/test-fitOutcomeModel.R +++ b/tests/testthat/test-fitOutcomeModel.R @@ -1,4 +1,3 @@ -library(CohortMethod) library(testthat) library(dplyr) @@ -43,7 +42,7 @@ test_that("population", { fitOutcomeModel( population = "studyPop" ), - "not 'character'" + "not 'character'|Must have names" ) ## Minimal columns ---- @@ -73,12 +72,13 @@ test_that("cohortMethodData", { expect_s3_class(mod, "OutcomeModel") ## Wrong data ---- + expect_error( fitOutcomeModel( population = studyPop, cohortMethodData = iris ), - "Must inherit from class 'CohortMethodData'" + "('analysisRef','cohorts','covariateRef','covariates','outcomes'|Must inherit from class 'CohortMethodData')" ) ## Empty CohortMethodData ---- diff --git a/tests/testthat/testthat-problems.rds b/tests/testthat/testthat-problems.rds deleted file mode 100644 index 2836d07412c32e76713635652fed621f16014e15..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 415694 zcmXV1c_38p_pW3KC6(;Pk~B%SBE=RD_mo^ygF3T^rKpTN=;L6qF5^#aG0AC^^q zjXh_x)$8BxtUn@o`POT%fngiNBR?KDWr&Q%?za}&q46>{b=!{J!$R-_xU|^R*nPwI z*G}C#wJHWbpu`>gjt<^j{=W3nCOawb8-YkG&JF&Vs~;Wadvm3KkCMgH?!GL@PW+XN zt=Xf?ar_Qt4p^GS+t^hyYbu@ku^_})A#ufKZTW2NCzX~Slegb~HlFsq*=)3Lx%SkY 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Wrong data ---- - expect_error( fitOutcomeModel( population = studyPop, @@ -305,3 +304,686 @@ test_that("useCovariates", { modFalse$outcomeCounts ) }) + +test_that("inversePtWeighting", { + ## FALSE ---- + modFalse1 <- fitOutcomeModel( + population = studyPop, + inversePtWeighting = FALSE + ) + + modFalse2 <- fitOutcomeModel( + population = ps, + inversePtWeighting = FALSE + ) + + modFalse3 <- fitOutcomeModel( + population = studyPopStratisfied, + inversePtWeighting = FALSE + ) + + modFalse4 <- fitOutcomeModel( + population = studyPopMatched, + inversePtWeighting = FALSE + ) + + expect_s3_class(modFalse1, "OutcomeModel") + expect_s3_class(modFalse2, "OutcomeModel") + expect_s3_class(modFalse3, "OutcomeModel") + expect_s3_class(modFalse4, "OutcomeModel") + + # ps vs stratisfied + expect_identical(modFalse2, modFalse3) + + # studyPop vs ps + expect_false(identical( + modFalse1, modFalse2 + )) + + # ps vs matched + expect_false(identical( + modFalse2, + modFalse4 + )) + + ## TRUE ---- + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + inversePtWeighting = TRUE + )), + "IPTW" + ) + + modTrue2 <- fitOutcomeModel( + population = ps, + inversePtWeighting = TRUE + ) + + modTrue3 <- fitOutcomeModel( + population = studyPopStratisfied, + inversePtWeighting = TRUE + ) + + modTrue4 <- fitOutcomeModel( + population = studyPopMatched, + inversePtWeighting = TRUE + ) + + expect_s3_class(modTrue2, "OutcomeModel") + expect_s3_class(modTrue3, "OutcomeModel") + expect_s3_class(modTrue4, "OutcomeModel") + + # ps vs stratisfied + expect_identical(modTrue2, modTrue3) + + # ps vs matched + expect_false(identical( + modTrue2, + modTrue4 + )) +}) + +test_that("interactionCovariateIds", { + # Hard check on 64bit? + # Warning on empty? + # Allow negatives? + femaleCovId <- 8532001 + + ## Defaults ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = femaleCovId + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Empty ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = c() + ) + + expect_s3_class(mod, "OutcomeModel") + + ## 64-bit int ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = bit64::as.integer64(99999999999) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Negative + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = -femaleCovId + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Negative 64-bit ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = -bit64::as.integer64(99999999999) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## NA ---- + expect_error( + fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = NA + ) + ) +}) + +test_that("interactionCovariateIds", { + # Hard check on 64bit? + # Warning on empty? + # Allow negatives? + femaleCovId <- 8532001 + + ## Defaults ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = femaleCovId + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Empty ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = c() + ) + + expect_s3_class(mod, "OutcomeModel") + + ## 64-bit int ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = bit64::as.integer64(99999999999) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Negative + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = -femaleCovId + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Negative 64-bit ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = -bit64::as.integer64(99999999999) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## NA ---- + expect_error( + fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = NA + ) + ) +}) + +test_that("excludeCovariateIds", { + # Hard check on 64bit? + # Warning on empty? + # Allow negatives? + femaleCovId <- 8532001 + + ## Defaults ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + excludeCovariateIds = femaleCovId + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Empty ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + excludeCovariateIds = c() + ) + + expect_s3_class(mod, "OutcomeModel") + + ## 64-bit int ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + excludeCovariateIds = bit64::as.integer64(99999999999) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Negative + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + excludeCovariateIds = -femaleCovId + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Negative 64-bit ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + excludeCovariateIds = -bit64::as.integer64(99999999999) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## NA ---- + expect_error( + fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + excludeCovariateIds = NA + ) + ) +}) + +test_that("includeCovariateIds", { + # Hard check on 64bit? + # Warning on empty? + # Allow negatives? + femaleCovId <- 8532001 + + ## Defaults ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + includeCovariateIds = femaleCovId + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Empty ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + includeCovariateIds = c() + ) + + expect_s3_class(mod, "OutcomeModel") + + ## 64-bit int ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + includeCovariateIds = bit64::as.integer64(99999999999) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Negative + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + includeCovariateIds = -femaleCovId + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Negative 64-bit ---- + mod <- fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + includeCovariateIds = -bit64::as.integer64(99999999999) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## NA ---- + expect_error( + fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + includeCovariateIds = NA + ) + ) +}) + +test_that("profileGrid", { + # Grid and Bounds allowed to be NULL? + # Grid allowed to be NA while Bounds is NULL? + ## Defaults ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = NULL + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Log scale ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = log(seq(1000)), + profileBounds = NULL + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Neg log scale ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = -log(seq(1000)), + profileBounds = NULL + ) + + expect_s3_class(mod, "OutcomeModel") + + ## 1 - 1000 ---- + expect_output( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = seq(1000), + profileBounds = NULL + ), + "Warning" + ) + + ## 1 - 100 ---- + expect_output( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = seq(100), + profileBounds = NULL + ), + "Warning" + ) + + ## 1 - 10 ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = seq(10), + profileBounds = NULL + ) + + expect_s3_class(mod, "OutcomeModel") + + ## NULL ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = NULL, + profileBounds = NULL + ) + + expect_s3_class(mod, "OutcomeModel") + + ## NA ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = NA, + profileBounds = NULL + ) + + expect_s3_class(mod, "OutcomeModel") + + ## Gird and Bounds ---- + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = log(seq(10)), + profileBounds = c(log(0.1), log(10)) + ), + "grid and bounds" + ) +}) + +test_that("profileBounds", { + # Set lower bounds to >0? + ## Defaults ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = c(log(0.1), log(10)) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## 0.1, 1000 ---- + expect_warning( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = c(0.1, 1000) + ) + ) + + ## 0.1, 100 ---- + expect_warning( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = c(0.1, 100) + ) + ) + + ## 0.1, 10 ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = c(0.1, 10) + ) + + expect_s3_class(mod, "OutcomeModel") + + ## character ---- + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = c("0.1", "10") + ) + ) + + ## 64bit int ---- + expect_warning( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = bit64::as.integer64(c(log(0.1), log(10))) + ) + ) + + ## Logical ---- + ### 0: FALSE, 1: TRUE ---- + mod <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = c(0, 1) + ) + + ### FALSE, TRUE ---- + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = c(FALSE, TRUE) + ) + ) + + ### log(0): inf, log(1) ---- + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = c(log(0), log(1)) + ) + ) + + ### log(FALSE): inf, log(TRUE) ---- + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileBounds = c(log(FALSE), log(TRUE)) + ) + ) +}) + +test_that("prior", { + # No feedback to user when prior is specified, but useCovariates == FALSE + ## laplace ---- + modLaplace <- fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + prior = createPrior("laplace", useCrossValidation = TRUE) + ) + + expect_s3_class(modLaplace, "OutcomeModel") + + ## none ---- + modNone <- suppressWarnings(fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + prior = createPrior("none") + )) + + expect_s3_class(modNone, "OutcomeModel") + + ## normal ---- + modNormal <- fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + prior = createPrior("normal", useCrossValidation = TRUE) + ) + + expect_s3_class(modNormal, "OutcomeModel") + + ## barupdate ---- + modBarupdate <- fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + prior = createPrior("barupdate") + ) + + expect_s3_class(modBarupdate, "OutcomeModel") + + expect_false(identical(modLaplace, modNormal)) + expect_false(identical(modLaplace, modNone)) + expect_false(identical(modLaplace, modBarupdate)) + + ## NULL ---- + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + prior = NULL + ) + ) + + ## Empty prior ---- + emptyPrior <- list() + class(emptyPrior) <- "cyclopsPrior" + + expect_error( + fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + prior = emptyPrior + ) + ) + + ## Malformed prior ---- + priorLaplace <- createPrior("laplace", useCrossValidation = TRUE) + malformedLaplace <- list( + priorType = priorLaplace$priorType, + variance = priorLaplace$variance + ) + class(malformedLaplace) <- "cyclopsPrior" + + expect_error( + fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + prior = malformedLaplace + ) + ) +}) + +test_that("control", { + # No feedback to user when control is specified, but useCovariates == FALSE + ## auto ---- + modAuto <- fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + control = createControl( + cvType = "auto", + seed = 1, + resetCoefficients = TRUE, + startingVariance = 0.01, + tolerance = 2e-07, + cvRepetitions = 10, + noiseLevel = "quiet" + ) + ) + + ## grid ---- + modGrid <- fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + control = createControl( + cvType = "grid", + seed = 1, + resetCoefficients = TRUE, + startingVariance = 0.01, + tolerance = 2e-07, + cvRepetitions = 10, + noiseLevel = "quiet" + ) + ) + + expect_false(identical(modAuto, modGrid)) + + ## NULL ---- + expect_error( + fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + control = NULL + ) + ) + + ## Empty control ---- + controlEmpty <- list() + class(controlEmpty) <- "cyclopsControl" + + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + control = controlEmpty + )) + ) + + ## Malfored control ---- + controlGrid <- createControl( + cvType = "grid", + seed = 1, + resetCoefficients = TRUE, + startingVariance = 0.01, + tolerance = 2e-07, + cvRepetitions = 10, + noiseLevel = "quiet" + ) + + controlMalformed <- list( + maxIterations = controlGrid$maxIterations, + autoSearch = controlGrid$autoSearch, + convergenceType = controlGrid$convergenceType + ) + class(controlMalformed) <- "cyclopsControl" + + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + useCovariates = TRUE, + cohortMethodData = sCohortMethodData, + control = controlMalformed + )) + ) +}) From 976c50c463bf36e89afb550ba136cb1d5c69a1e5 Mon Sep 17 00:00:00 2001 From: Maarten van Kessel Date: Wed, 4 Oct 2023 15:56:24 +0200 Subject: [PATCH 5/6] Removed duplicate tests --- tests/testthat/test-fitOutcomeModel.R | 61 --------------------------- 1 file changed, 61 deletions(-) diff --git a/tests/testthat/test-fitOutcomeModel.R b/tests/testthat/test-fitOutcomeModel.R index 9a7e0785..8ade3022 100644 --- a/tests/testthat/test-fitOutcomeModel.R +++ b/tests/testthat/test-fitOutcomeModel.R @@ -445,67 +445,6 @@ test_that("interactionCovariateIds", { ) }) -test_that("interactionCovariateIds", { - # Hard check on 64bit? - # Warning on empty? - # Allow negatives? - femaleCovId <- 8532001 - - ## Defaults ---- - mod <- fitOutcomeModel( - population = studyPopMatched, - cohortMethodData = sCohortMethodData, - interactionCovariateIds = femaleCovId - ) - - expect_s3_class(mod, "OutcomeModel") - - ## Empty ---- - mod <- fitOutcomeModel( - population = studyPopMatched, - cohortMethodData = sCohortMethodData, - interactionCovariateIds = c() - ) - - expect_s3_class(mod, "OutcomeModel") - - ## 64-bit int ---- - mod <- fitOutcomeModel( - population = studyPopMatched, - cohortMethodData = sCohortMethodData, - interactionCovariateIds = bit64::as.integer64(99999999999) - ) - - expect_s3_class(mod, "OutcomeModel") - - ## Negative - mod <- fitOutcomeModel( - population = studyPopMatched, - cohortMethodData = sCohortMethodData, - interactionCovariateIds = -femaleCovId - ) - - expect_s3_class(mod, "OutcomeModel") - - ## Negative 64-bit ---- - mod <- fitOutcomeModel( - population = studyPopMatched, - cohortMethodData = sCohortMethodData, - interactionCovariateIds = -bit64::as.integer64(99999999999) - ) - - expect_s3_class(mod, "OutcomeModel") - - ## NA ---- - expect_error( - fitOutcomeModel( - population = studyPopMatched, - cohortMethodData = sCohortMethodData, - interactionCovariateIds = NA - ) - ) -}) - test_that("excludeCovariateIds", { # Hard check on 64bit? # Warning on empty? From ba25a6b093332f9ca2f2084a5d7d8b782192b6ba Mon Sep 17 00:00:00 2001 From: Maarten van Kessel Date: Thu, 5 Oct 2023 11:14:50 +0200 Subject: [PATCH 6/6] Minor updates and test additions --- R/OutcomeModels.R | 3 + tests/testthat/test-fitOutcomeModel.R | 125 +++++++++++++++++++++++--- 2 files changed, 117 insertions(+), 11 deletions(-) diff --git a/R/OutcomeModels.R b/R/OutcomeModels.R index 00283afd..b6dbb967 100644 --- a/R/OutcomeModels.R +++ b/R/OutcomeModels.R @@ -109,6 +109,9 @@ fitOutcomeModel <- function(population, if (any(excludeCovariateIds %in% interactionCovariateIds)) { stop("Can't exclude covariates that are to be used for interaction terms") } + if (any(includeCovariateIds %in% excludeCovariateIds)) { + stop("Can't exclude covariates that are to be included") + } if (inversePtWeighting && is.null(population$iptw)) { stop("Requested inverse probability weighting, but no IPTW are provided. Use createPs to generate them") } diff --git a/tests/testthat/test-fitOutcomeModel.R b/tests/testthat/test-fitOutcomeModel.R index 8ade3022..77649d65 100644 --- a/tests/testthat/test-fitOutcomeModel.R +++ b/tests/testthat/test-fitOutcomeModel.R @@ -60,6 +60,17 @@ test_that("population", { ), "Names must include the elements" ) + + ## sum(outcomeCount) == 0 ---- + pop <- studyPop %>% + mutate(outcomeCount = 0) + + expect_output( + fitOutcomeModel( + population = pop + ), + "NO OUTCOMES FOUND FOR POPULATION" + ) }) test_that("cohortMethodData", { @@ -260,6 +271,29 @@ test_that("stratified", { modTrue1$outcomeCounts, modTrue2$outcomeCounts )) + + ## sum(treatment) == 0 ---- + pop0 <- studyPopStratisfied %>% + mutate(treatment = 0) + + pop1 <- studyPopStratisfied %>% + mutate(treatment = 1) + + expect_output( + fitOutcomeModel( + population = pop0, + stratified = TRUE + ), + "NO STRATA WITH BOTH TARGET, COMPARATOR, .+ OUTCOME" + ) + + expect_output( + fitOutcomeModel( + population = pop1, + stratified = TRUE + ), + "NO STRATA WITH BOTH TARGET, COMPARATOR, .+ OUTCOME" + ) }) test_that("useCovariates", { @@ -443,6 +477,16 @@ test_that("interactionCovariateIds", { interactionCovariateIds = NA ) ) + + ## length(interactionCovariateIds) > 1 & useCovariates ---- + mod <- suppressWarnings(fitOutcomeModel( + population = studyPopMatched, + cohortMethodData = sCohortMethodData, + useCovariates = TRUE, + interactionCovariateIds = c(femaleCovId) + )) + + expect_s3_class(mod, "OutcomeModel") }) test_that("excludeCovariateIds", { @@ -650,17 +694,6 @@ test_that("profileGrid", { ) expect_s3_class(mod, "OutcomeModel") - - ## Gird and Bounds ---- - expect_error( - fitOutcomeModel( - population = studyPop, - cohortMethodData = sCohortMethodData, - profileGrid = log(seq(10)), - profileBounds = c(log(0.1), log(10)) - ), - "grid and bounds" - ) }) test_that("profileBounds", { @@ -926,3 +959,73 @@ test_that("control", { )) ) }) + +test_that("Combinations", { + ## stratified && nrow(population) > 0 && is.null(population$stratumId) + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + stratified = TRUE + )), + "matchOnPs or stratifyByPs" + ) + ## is.null(cohortMethodData) && useCovariates + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + useCovariates = TRUE + )), + "covariates .+ no cohortMethodData object specified" + ) + + ## is.null(cohortMethodData) && length(interactionCovariateIds) != 0 + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + interactionCovariateIds = c(8532001) + )), + "interaction terms .+ no cohortMethodData object specified" + ) + + ## any(excludeCovariateIds %in% interactionCovariateIds) + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + interactionCovariateIds = c(8532001), + excludeCovariateIds = c(8532001) + )), + "exclude covariates .+ used for interaction" + ) + + # any(includeCovariateIds %in% excludeCovariateIds) + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + includeCovariateIds = c(8532001), + excludeCovariateIds = c(8532001) + )), + "exclude covariates .+ included" + ) + + ## inversePtWeighting && is.null(population$iptw) + expect_error( + suppressWarnings(fitOutcomeModel( + population = studyPop, + inversePtWeighting = TRUE + )), + "no IPTW are provided" + ) + + ## !is.null(profileGrid) && !is.null(profileBounds) + expect_error( + fitOutcomeModel( + population = studyPop, + cohortMethodData = sCohortMethodData, + profileGrid = log(seq(10)), + profileBounds = c(log(0.1), log(10)) + ), + "grid and bounds" + ) +})