R/main.R
In alondhe/PPMD: Postpartum Depression and Mood Disorders Study
#' Create Target and Comparator Cohorts
#' @param cdmDb
#' @param scratchDatabaseSchema
#' @param connectionDetails
#'
#' @export
createCohorts <- function(cdmDb, scratchDatabaseSchema, connectionDetails)
{
cohorts <- list(
list(cohortId = 5441, clause = "and A.episode_length >= 161 and A.episode_length < 259"),
list(cohortId = 5442, clause = "and A.episode_length >= 259 and A.episode_length <= 294")
)
connection <- DatabaseConnector::connect(connectionDetails = connectionDetails)
tablePrefix <- paste("al", "ppmd", strsplit(x = cdmDb$key, split = "_")[[1]][1], sep = "_")
for (cohort in cohorts)
{
sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "createCohort.sql",
packageName = "PPMD",
dbms = connectionDetails$dbms,
cdm_database_schema = cdmDb$cdmDatabaseSchema,
target_database_schema = scratchDatabaseSchema,
target_cohort_table = paste(tablePrefix, "cohort", sep = "_"),
resultsDatabaseSchema = cdmDb$resultsDatabaseSchema,
whereClause = cohort$clause,
target_cohort_id = cohort$cohortId)
DatabaseConnector::executeSql(connection = connection, sql = sql)
}
DatabaseConnector::disconnect(connection = connection)
}
#' @export
createDeliveryCovariateSettings <- function(useDelivery = TRUE) {
covariateSettings <- list(useDelivery = useDelivery)
attr(covariateSettings, "fun") <- "getDeliveryCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' @export
getDeliveryCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE)
{
if (covariateSettings$useDelivery == FALSE) {
return(NULL)
}
if (aggregated)
{
stop("Aggregation not supported")
}
writeLines("***Creating custom delivery covariates***")
sql <- loadRenderTranslateSql(sqlFilename = "createDeliveryCovariates.sql",
packageName = "PPMD",
dbms = attr(connection, "dbms"),
cohortTable = cohortTable,
resultsDatabaseSchema = "cdm_optum_extended_ses_v675.ohdsi_results")
sql <- SqlRender::translateSql(sql, targetDialect = attr(connection, "dbms"))$sql
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql.ffdf(connection, sql)
# Convert colum names to camelCase:
colnames(covariates) <- SqlRender::snakeCaseToCamelCase(colnames(covariates))
# Construct covariate reference:
covariateRef_1 <- data.frame(covariateId = 1,
covariateName = "SB",
analysisId = 1,
conceptId = 0)
covariateRef_2 <- data.frame(covariateId = 2,
covariateName = "ECT",
analysisId = 1,
conceptId = 0)
covariateRef_3 <- data.frame(covariateId = 3,
covariateName = "LB or DELIV",
analysisId = 1,
conceptId = 0)
covariateRef <- rbind(covariateRef_1, covariateRef_2, covariateRef_3)
covariateRef <- ff::as.ffdf(covariateRef)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 1,
analysisName = "Delivery Type",
domainId = "Procedure",
startDay = 0,
endDay = 0,
isBinary = "Y",
missingMeansZero = "Y")
analysisRef <- ff::as.ffdf(analysisRef)
#Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- list(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef,
metaData = metaData)
class(result) <- "covariateData"
writeLines("***Custom Delivery covariates created***")
return(result)
}
#' Run the analysis for a CDM
#'
#' @param cdmDb
#' @param scratchDatabaseSchema
#' @param connectionDetails
#' @param negativeControlConcepts
#'
#' @export
run <- function(cdmDb, scratchDatabaseSchema, connectionDetails, excludedConcepts,
negativeControlConcepts, blindOutcomeModel = TRUE, outputFolder)
{
# Data extraction ----
tablePrefix <- paste("al", "ppmd", strsplit(x = cdmDb$key, split = "_")[[1]][1], sep = "_")
# cdmDatabaseSchema <- cdmDb$cdmDatabaseSchema
# resultsDatabaseSchema <- scratchDatabaseSchema
exposureTable <- paste(tablePrefix, "cohort", sep = "_")
# outcomeTable <- "cohort"
cdmVersion <- "5"
maxCores <- parallel::detectCores()
# if(!dir.exists(outputFolder)){
# dir.create(outputFolder, recursive = TRUE)
# }
#setwd(outputFolder)
targetCohortId <- 5441
comparatorCohortId <- 5442
outcomeCohortId <- 5440
outcomeList <- c(outcomeCohortId)
# Default Prior & Control settings ----
defaultPrior <- Cyclops::createPrior("laplace",
exclude = c(0),
useCrossValidation = TRUE)
defaultControl <- Cyclops::createControl(cvType = "auto",
startingVariance = 0.01,
noiseLevel = "quiet",
tolerance = 2e-07,
cvRepetitions = 10,
threads = 1)
# PLEASE NOTE ----
# If you want to use your code in a distributed network study
# you will need to create a temporary cohort table with common cohort IDs.
# The code below ASSUMES you are only running in your local network
# where common cohort IDs have already been assigned in the cohort table.
# Get all Concept IDs for exclusion ----
# Get all Concept IDs for inclusion ----
includedConcepts <- c()
# Get all Concept IDs for exclusion in the outcome model ----
omExcludedConcepts <- c()
# Get all Concept IDs for inclusion exclusion in the outcome model ----
omIncludedConcepts <- c()
# Create drug comparator and outcome arguments by combining target + comparator + outcome + negative controls ----
dcos <- CohortMethod::createDrugComparatorOutcomes(targetId = targetCohortId,
comparatorId = comparatorCohortId,
excludedCovariateConceptIds = excludedConcepts,
includedCovariateConceptIds = includedConcepts,
outcomeIds = c(outcomeList, negativeControlConcepts))
drugComparatorOutcomesList <- list(dcos)
# Define which types of covariates must be constructed ----
covariateSettings <- FeatureExtraction::createCovariateSettings(useDemographicsGender = TRUE,
useDemographicsAge = TRUE,
useDemographicsAgeGroup = TRUE,
useDemographicsRace = TRUE,
useDemographicsEthnicity = TRUE,
useDemographicsIndexYear = TRUE,
useDemographicsIndexMonth = TRUE,
useDemographicsPriorObservationTime = TRUE,
useDemographicsPostObservationTime = TRUE,
useDemographicsTimeInCohort = FALSE,
useDemographicsIndexYearMonth = TRUE,
useConditionOccurrenceAnyTimePrior = TRUE,
useConditionOccurrenceLongTerm = TRUE,
useConditionOccurrenceMediumTerm = TRUE,
useConditionOccurrenceShortTerm = TRUE,
useConditionOccurrenceInpatientAnyTimePrior = TRUE,
useConditionOccurrenceInpatientLongTerm = TRUE,
useConditionOccurrenceInpatientMediumTerm = TRUE,
useConditionOccurrenceInpatientShortTerm = TRUE,
useConditionEraAnyTimePrior = TRUE,
useConditionEraLongTerm = TRUE,
useConditionEraMediumTerm = TRUE,
useConditionEraShortTerm = TRUE,
useConditionEraOverlapping = FALSE,
useConditionEraStartLongTerm = TRUE,
useConditionEraStartMediumTerm = TRUE,
useConditionEraStartShortTerm = TRUE,
useConditionGroupEraAnyTimePrior = TRUE,
useConditionGroupEraLongTerm = TRUE,
useConditionGroupEraMediumTerm = TRUE,
useConditionGroupEraShortTerm = TRUE,
useConditionGroupEraOverlapping = FALSE,
useConditionGroupEraStartLongTerm = TRUE,
useConditionGroupEraStartMediumTerm = TRUE,
useConditionGroupEraStartShortTerm = TRUE,
useDrugExposureAnyTimePrior = TRUE,
useDrugExposureLongTerm = TRUE,
useDrugExposureMediumTerm = TRUE,
useDrugExposureShortTerm = TRUE,
useDrugEraAnyTimePrior = TRUE,
useDrugEraLongTerm = TRUE,
useDrugEraMediumTerm = TRUE,
useDrugEraShortTerm = TRUE,
useDrugEraOverlapping = FALSE,
useDrugEraStartLongTerm = TRUE,
useDrugEraStartMediumTerm = TRUE,
useDrugEraStartShortTerm = TRUE,
useDrugGroupEraAnyTimePrior = TRUE,
useDrugGroupEraLongTerm = TRUE,
useDrugGroupEraMediumTerm = TRUE,
useDrugGroupEraShortTerm = TRUE,
useDrugGroupEraOverlapping = FALSE,
useDrugGroupEraStartLongTerm = TRUE,
useDrugGroupEraStartMediumTerm = TRUE,
useDrugGroupEraStartShortTerm = TRUE,
useProcedureOccurrenceAnyTimePrior = TRUE,
useProcedureOccurrenceLongTerm = TRUE,
useProcedureOccurrenceMediumTerm = TRUE,
useProcedureOccurrenceShortTerm = TRUE,
useDeviceExposureAnyTimePrior = TRUE,
useDeviceExposureLongTerm = TRUE,
useDeviceExposureMediumTerm = TRUE,
useDeviceExposureShortTerm = TRUE,
useMeasurementAnyTimePrior = TRUE,
useMeasurementLongTerm = TRUE,
useMeasurementMediumTerm = TRUE,
useMeasurementShortTerm = TRUE,
useMeasurementValueAnyTimePrior = TRUE,
useMeasurementValueLongTerm = TRUE,
useMeasurementValueMediumTerm = TRUE,
useMeasurementValueShortTerm = TRUE,
useMeasurementRangeGroupAnyTimePrior = TRUE,
useMeasurementRangeGroupLongTerm = TRUE,
useMeasurementRangeGroupMediumTerm = TRUE,
useMeasurementRangeGroupShortTerm = TRUE,
useObservationAnyTimePrior = TRUE,
useObservationLongTerm = TRUE,
useObservationMediumTerm = TRUE,
useObservationShortTerm = TRUE,
useCharlsonIndex = TRUE,
useDcsi = TRUE,
useChads2 = TRUE,
useChads2Vasc = TRUE,
useDistinctConditionCountLongTerm = TRUE,
useDistinctConditionCountMediumTerm = TRUE,
useDistinctConditionCountShortTerm = TRUE,
useDistinctIngredientCountLongTerm = TRUE,
useDistinctIngredientCountMediumTerm = TRUE,
useDistinctIngredientCountShortTerm = TRUE,
useDistinctProcedureCountLongTerm = TRUE,
useDistinctProcedureCountMediumTerm = TRUE,
useDistinctProcedureCountShortTerm = TRUE,
useDistinctMeasurementCountLongTerm = TRUE,
useDistinctMeasurementCountMediumTerm = TRUE,
useDistinctMeasurementCountShortTerm = TRUE,
useVisitCountLongTerm = TRUE,
useVisitCountMediumTerm = TRUE,
useVisitCountShortTerm = TRUE,
longTermStartDays = -365,
mediumTermStartDays = -180,
shortTermStartDays = -30,
endDays = -1,
includedCovariateConceptIds = c(),
addDescendantsToInclude = TRUE,
excludedCovariateConceptIds = excludedConcepts,
addDescendantsToExclude = TRUE,
includedCovariateIds = c())
covariateSettingsList <- list(covariateSettings, createDeliveryCovariateSettings(useDelivery = TRUE))
getDbCmDataArgs <- CohortMethod::createGetDbCohortMethodDataArgs(washoutPeriod = 365,
firstExposureOnly = TRUE,
removeDuplicateSubjects = TRUE,
studyStartDate = "20090101",
studyEndDate = "20121231",
excludeDrugsFromCovariates = FALSE,
covariateSettings = covariateSettingsList)
createStudyPopArgs <- CohortMethod::createCreateStudyPopulationArgs(removeSubjectsWithPriorOutcome = TRUE,
firstExposureOnly = TRUE,
washoutPeriod = 365,
removeDuplicateSubjects = TRUE,
minDaysAtRisk = 1,
riskWindowStart = 0,
addExposureDaysToStart = FALSE,
riskWindowEnd = 180,
addExposureDaysToEnd = TRUE)
fitOutcomeModelArgs1 <- CohortMethod::createFitOutcomeModelArgs(useCovariates = FALSE,
modelType = "logistic",
stratified = TRUE,
includeCovariateIds = omIncludedConcepts,
excludeCovariateIds = omExcludedConcepts,
prior = defaultPrior,
control = defaultControl)
createPsArgs1 <- CohortMethod::createCreatePsArgs(control = defaultControl) # Using only defaults
trimByPsArgs1 <- CohortMethod::createTrimByPsArgs() # Using only defaults
trimByPsToEquipoiseArgs1 <- CohortMethod::createTrimByPsToEquipoiseArgs() # Using only defaults
matchOnPsArgs1 <- CohortMethod::createMatchOnPsArgs(caliper = 0.25, caliperScale = "standardized", maxRatio = 10)
stratifyByPsArgs1 <- CohortMethod::createStratifyByPsArgs() # Using only defaults
cmAnalysis1 <- CohortMethod::createCmAnalysis(analysisId = 1,
description = "Early Pregnancy vs On-Time Pregnancy, PPMD",
getDbCohortMethodDataArgs = getDbCmDataArgs,
createStudyPopArgs = createStudyPopArgs,
createPs = TRUE,
createPsArgs = createPsArgs1,
trimByPs = FALSE,
trimByPsArgs = trimByPsArgs1,
trimByPsToEquipoise = FALSE,
trimByPsToEquipoiseArgs = trimByPsToEquipoiseArgs1,
matchOnPs = TRUE,
matchOnPsArgs = matchOnPsArgs1,
stratifyByPs = FALSE,
stratifyByPsArgs = stratifyByPsArgs1,
computeCovariateBalance = TRUE,
fitOutcomeModel = TRUE,
fitOutcomeModelArgs = fitOutcomeModelArgs1)
cmAnalysisList <- list(cmAnalysis1)
# Run the analysis ----
result <- CohortMethod::runCmAnalyses(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDb$cdmDatabaseSchema,
exposureDatabaseSchema = scratchDatabaseSchema,
exposureTable = exposureTable,
outcomeDatabaseSchema = cdmDb$resultsDatabaseSchema,
outcomeTable = "cohort",
cdmVersion = cdmVersion,
outputFolder = outputFolder,
cmAnalysisList = cmAnalysisList,
drugComparatorOutcomesList = drugComparatorOutcomesList,
getDbCohortMethodDataThreads = 1,
createPsThreads = 2,
psCvThreads = min(20, maxCores),
computeCovarBalThreads = min(3, maxCores),
createStudyPopThreads = min(3, maxCores),
trimMatchStratifyThreads = min(10, maxCores),
fitOutcomeModelThreads = max(1, round(maxCores/4)),
outcomeCvThreads = min(4, maxCores),
refitPsForEveryOutcome = FALSE)
## Summarize the results
analysisSummary <- CohortMethod::summarizeAnalyses(result)
head(analysisSummary)
# Perform Empirical Calibration ----
newSummary <- data.frame()
# Calibrate p-values:
drugComparatorOutcome <- drugComparatorOutcomesList[[1]]
for (drugComparatorOutcome in drugComparatorOutcomesList) {
for (analysisId in unique(analysisSummary$analysisId)) {
subset <- analysisSummary[analysisSummary$analysisId == analysisId &
analysisSummary$targetId == drugComparatorOutcome$targetId &
analysisSummary$comparatorId == drugComparatorOutcome$comparatorId, ]
negControlSubset <- subset[analysisSummary$outcomeId %in% negativeControlConcepts, ]
negControlSubset <- negControlSubset[!is.na(negControlSubset$logRr) & negControlSubset$logRr != 0, ]
hoiSubset <- subset[!(analysisSummary$outcomeId %in% negativeControlConcepts), ]
hoiSubset <- hoiSubset[!is.na(hoiSubset$logRr) & hoiSubset$logRr != 0, ]
if (nrow(negControlSubset) > 10) {
null <- EmpiricalCalibration::fitMcmcNull(negControlSubset$logRr, negControlSubset$seLogRr)
# View the empirical calibration plot with only negative controls
EmpiricalCalibration::plotCalibrationEffect(negControlSubset$logRr,
negControlSubset$seLogRr)
# Save the empirical calibration plot with only negative controls
plotName <- paste("calEffectNoHois_a",analysisId, "_t", drugComparatorOutcome$targetId, "_c", drugComparatorOutcome$comparatorId, ".png", sep = "")
EmpiricalCalibration::plotCalibrationEffect(negControlSubset$logRr,
negControlSubset$seLogRr,
fileName = file.path(outputFolder, plotName))
# View the empirical calibration plot with negative controls and HOIs plotted
EmpiricalCalibration::plotCalibrationEffect(negControlSubset$logRr,
negControlSubset$seLogRr,
hoiSubset$logRr,
hoiSubset$seLogRr)
# Save the empirical calibration plot with negative controls and HOIs plotted
plotName <- paste("calEffect_a",analysisId, "_t", drugComparatorOutcome$targetId, "_c", drugComparatorOutcome$comparatorId, ".png", sep = "")
EmpiricalCalibration::plotCalibrationEffect(negControlSubset$logRr,
negControlSubset$seLogRr,
hoiSubset$logRr,
hoiSubset$seLogRr,
fileName = file.path(outputFolder, plotName))
calibratedP <- calibrateP(null, subset$logRr, subset$seLogRr)
subset$calibratedP <- calibratedP$p
subset$calibratedP_lb95ci <- calibratedP$lb95ci
subset$calibratedP_ub95ci <- calibratedP$ub95ci
mcmc <- attr(null, "mcmc")
subset$null_mean <- mean(mcmc$chain[, 1])
subset$null_sd <- 1/sqrt(mean(mcmc$chain[, 2]))
} else {
subset$calibratedP <- NA
subset$calibratedP_lb95ci <- NA
subset$calibratedP_ub95ci <- NA
subset$null_mean <- NA
subset$null_sd <- NA
}
newSummary <- rbind(newSummary, subset)
}
}
# Results ----
drugComparatorOutcome <- drugComparatorOutcomesList[[1]]
for (drugComparatorOutcome in drugComparatorOutcomesList) {
for (analysisId in unique(analysisSummary$analysisId)) {
currentAnalysisSubset <- analysisSummary[analysisSummary$analysisId == analysisId &
analysisSummary$targetId == drugComparatorOutcome$targetId &
analysisSummary$comparatorId == drugComparatorOutcome$comparatorId &
analysisSummary$outcomeId %in% outcomeList, ]
for(currentOutcomeId in unique(currentAnalysisSubset$outcomeId)) {
outputImageSuffix <- paste0("_a",analysisId, "_t", currentAnalysisSubset$targetId, "_c", currentAnalysisSubset$comparatorId, "_o", currentOutcomeId, ".png")
cohortMethodFile <- result$cohortMethodDataFolder[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
cohortMethodData <- loadCohortMethodData(cohortMethodFile)
studyPopFile <- result$studyPopFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
# Return the attrition table for the study population ----
studyPop <- readRDS(studyPopFile)
getAttritionTable(studyPop)
# View the attrition diagram
drawAttritionDiagram(studyPop,
treatmentLabel = "Target",
comparatorLabel = "Comparator")
# Save the attrition diagram ----
plotName <- paste0("attritionDiagram", outputImageSuffix);
drawAttritionDiagram(studyPop,
treatmentLabel = "Target",
comparatorLabel = "Comparator",
fileName = file.path(outputFolder, plotName))
psFile <- result$psFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
ps <- readRDS(psFile)
# Compute the area under the receiver-operator curve (AUC) for the propensity score model ----
CohortMethod::computePsAuc(ps)
# Plot the propensity score distribution ----
CohortMethod::plotPs(ps,
scale = "preference")
# Save the propensity score distribution ----
plotName <- paste0("propensityScorePlot", outputImageSuffix);
CohortMethod::plotPs(ps,
scale = "preference",
fileName = file.path(outputFolder, plotName))
# Inspect the propensity model ----
propensityModel <- CohortMethod::getPsModel(ps, cohortMethodData)
head(propensityModel)
strataFile <- result$strataFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
strataPop <- readRDS(strataFile)
# View PS With Population Trimmed By Percentile ----
CohortMethod::plotPs(strataPop,
ps,
scale = "preference")
# Save PS With Population Trimmed By Percentile ----
plotName <- paste0("propensityScorePlotStrata", outputImageSuffix);
CohortMethod::plotPs(strataPop,
ps,
scale = "preference",
fileName = file.path(outputFolder, plotName))
# Get the attrition table and diagram for the strata pop ----
CohortMethod::getAttritionTable(strataPop)
# View the attrition diagram for the strata pop ----
CohortMethod::drawAttritionDiagram(strataPop)
# Save the attrition diagram for the strata pop ----
plotName <- paste0("attritionDiagramStrata", outputImageSuffix);
CohortMethod::drawAttritionDiagram(strataPop,
fileName = file.path(outputFolder, plotName))
# Plot the covariate balance ----
balanceFile <- result$covariateBalanceFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
balance <- readRDS(balanceFile)
# View the covariate balance scatter plot ----
CohortMethod::plotCovariateBalanceScatterPlot(balance)
# Save the covariate balance scatter plot ----
plotName <- paste0("covBalScatter", outputImageSuffix);
CohortMethod::plotCovariateBalanceScatterPlot(balance,
fileName = file.path(outputFolder, plotName))
# View the plot of top variables ----
CohortMethod::plotCovariateBalanceOfTopVariables(balance)
# Save the plot of top variables ----
plotName <- paste0("covBalTop", outputImageSuffix);
CohortMethod::plotCovariateBalanceOfTopVariables(balance,
fileName = file.path(outputFolder, plotName))
# Outcome Model ----
outcomeFile <- result$outcomeModelFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
outcomeModel <- readRDS(outcomeFile)
# Calibrated results -----
outcomeSummary <- newSummary[newSummary$targetId == currentAnalysisSubset$targetId &
newSummary$comparatorId == currentAnalysisSubset$comparatorId &
newSummary$outcomeId == currentOutcomeId &
newSummary$analysisId == analysisId, ]
outcomeSummaryOutput <- data.frame(outcomeSummary$rr,
outcomeSummary$ci95lb,
outcomeSummary$ci95ub,
outcomeSummary$logRr,
outcomeSummary$seLogRr,
outcomeSummary$p,
outcomeSummary$calibratedP,
outcomeSummary$calibratedP_lb95ci,
outcomeSummary$calibratedP_ub95ci,
outcomeSummary$null_mean,
outcomeSummary$null_sd)
colnames(outcomeSummaryOutput) <- c("Estimate",
"lower .95",
"upper .95",
"logRr",
"seLogRr",
"p",
"cal p",
"cal p - lower .95",
"cal p - upper .95",
"null mean",
"null sd")
rownames(outcomeSummaryOutput) <- "treatment"
if (!blindOutcomeModel)
{
# View the outcome model -----
outcomeModelOutput <- capture.output(outcomeModel)
outcomeModelOutput <- head(outcomeModelOutput,n=length(outcomeModelOutput)-2)
outcomeSummaryOutput <- capture.output(printCoefmat(outcomeSummaryOutput))
outcomeModelOutput <- c(outcomeModelOutput, outcomeSummaryOutput)
writeLines(outcomeModelOutput)
}
}
}
}
}
modelling <- function()
{
}
alondhe/PPMD documentation built on May 14, 2019, 6:14 p.m.
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#' Create Target and Comparator Cohorts
#' @param cdmDb
#' @param scratchDatabaseSchema
#' @param connectionDetails
#'
#' @export
createCohorts <- function(cdmDb, scratchDatabaseSchema, connectionDetails)
{
cohorts <- list(
list(cohortId = 5441, clause = "and A.episode_length >= 161 and A.episode_length < 259"),
list(cohortId = 5442, clause = "and A.episode_length >= 259 and A.episode_length <= 294")
)
connection <- DatabaseConnector::connect(connectionDetails = connectionDetails)
tablePrefix <- paste("al", "ppmd", strsplit(x = cdmDb$key, split = "_")[[1]][1], sep = "_")
for (cohort in cohorts)
{
sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "createCohort.sql",
packageName = "PPMD",
dbms = connectionDetails$dbms,
cdm_database_schema = cdmDb$cdmDatabaseSchema,
target_database_schema = scratchDatabaseSchema,
target_cohort_table = paste(tablePrefix, "cohort", sep = "_"),
resultsDatabaseSchema = cdmDb$resultsDatabaseSchema,
whereClause = cohort$clause,
target_cohort_id = cohort$cohortId)
DatabaseConnector::executeSql(connection = connection, sql = sql)
}
DatabaseConnector::disconnect(connection = connection)
}
#' @export
createDeliveryCovariateSettings <- function(useDelivery = TRUE) {
covariateSettings <- list(useDelivery = useDelivery)
attr(covariateSettings, "fun") <- "getDeliveryCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' @export
getDeliveryCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE)
{
if (covariateSettings$useDelivery == FALSE) {
return(NULL)
}
if (aggregated)
{
stop("Aggregation not supported")
}
writeLines("***Creating custom delivery covariates***")
sql <- loadRenderTranslateSql(sqlFilename = "createDeliveryCovariates.sql",
packageName = "PPMD",
dbms = attr(connection, "dbms"),
cohortTable = cohortTable,
resultsDatabaseSchema = "cdm_optum_extended_ses_v675.ohdsi_results")
sql <- SqlRender::translateSql(sql, targetDialect = attr(connection, "dbms"))$sql
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql.ffdf(connection, sql)
# Convert colum names to camelCase:
colnames(covariates) <- SqlRender::snakeCaseToCamelCase(colnames(covariates))
# Construct covariate reference:
covariateRef_1 <- data.frame(covariateId = 1,
covariateName = "SB",
analysisId = 1,
conceptId = 0)
covariateRef_2 <- data.frame(covariateId = 2,
covariateName = "ECT",
analysisId = 1,
conceptId = 0)
covariateRef_3 <- data.frame(covariateId = 3,
covariateName = "LB or DELIV",
analysisId = 1,
conceptId = 0)
covariateRef <- rbind(covariateRef_1, covariateRef_2, covariateRef_3)
covariateRef <- ff::as.ffdf(covariateRef)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 1,
analysisName = "Delivery Type",
domainId = "Procedure",
startDay = 0,
endDay = 0,
isBinary = "Y",
missingMeansZero = "Y")
analysisRef <- ff::as.ffdf(analysisRef)
#Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- list(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef,
metaData = metaData)
class(result) <- "covariateData"
writeLines("***Custom Delivery covariates created***")
return(result)
}
#' Run the analysis for a CDM
#'
#' @param cdmDb
#' @param scratchDatabaseSchema
#' @param connectionDetails
#' @param negativeControlConcepts
#'
#' @export
run <- function(cdmDb, scratchDatabaseSchema, connectionDetails, excludedConcepts,
negativeControlConcepts, blindOutcomeModel = TRUE, outputFolder)
{
# Data extraction ----
tablePrefix <- paste("al", "ppmd", strsplit(x = cdmDb$key, split = "_")[[1]][1], sep = "_")
# cdmDatabaseSchema <- cdmDb$cdmDatabaseSchema
# resultsDatabaseSchema <- scratchDatabaseSchema
exposureTable <- paste(tablePrefix, "cohort", sep = "_")
# outcomeTable <- "cohort"
cdmVersion <- "5"
maxCores <- parallel::detectCores()
# if(!dir.exists(outputFolder)){
# dir.create(outputFolder, recursive = TRUE)
# }
#setwd(outputFolder)
targetCohortId <- 5441
comparatorCohortId <- 5442
outcomeCohortId <- 5440
outcomeList <- c(outcomeCohortId)
# Default Prior & Control settings ----
defaultPrior <- Cyclops::createPrior("laplace",
exclude = c(0),
useCrossValidation = TRUE)
defaultControl <- Cyclops::createControl(cvType = "auto",
startingVariance = 0.01,
noiseLevel = "quiet",
tolerance = 2e-07,
cvRepetitions = 10,
threads = 1)
# PLEASE NOTE ----
# If you want to use your code in a distributed network study
# you will need to create a temporary cohort table with common cohort IDs.
# The code below ASSUMES you are only running in your local network
# where common cohort IDs have already been assigned in the cohort table.
# Get all Concept IDs for exclusion ----
# Get all Concept IDs for inclusion ----
includedConcepts <- c()
# Get all Concept IDs for exclusion in the outcome model ----
omExcludedConcepts <- c()
# Get all Concept IDs for inclusion exclusion in the outcome model ----
omIncludedConcepts <- c()
# Create drug comparator and outcome arguments by combining target + comparator + outcome + negative controls ----
dcos <- CohortMethod::createDrugComparatorOutcomes(targetId = targetCohortId,
comparatorId = comparatorCohortId,
excludedCovariateConceptIds = excludedConcepts,
includedCovariateConceptIds = includedConcepts,
outcomeIds = c(outcomeList, negativeControlConcepts))
drugComparatorOutcomesList <- list(dcos)
# Define which types of covariates must be constructed ----
covariateSettings <- FeatureExtraction::createCovariateSettings(useDemographicsGender = TRUE,
useDemographicsAge = TRUE,
useDemographicsAgeGroup = TRUE,
useDemographicsRace = TRUE,
useDemographicsEthnicity = TRUE,
useDemographicsIndexYear = TRUE,
useDemographicsIndexMonth = TRUE,
useDemographicsPriorObservationTime = TRUE,
useDemographicsPostObservationTime = TRUE,
useDemographicsTimeInCohort = FALSE,
useDemographicsIndexYearMonth = TRUE,
useConditionOccurrenceAnyTimePrior = TRUE,
useConditionOccurrenceLongTerm = TRUE,
useConditionOccurrenceMediumTerm = TRUE,
useConditionOccurrenceShortTerm = TRUE,
useConditionOccurrenceInpatientAnyTimePrior = TRUE,
useConditionOccurrenceInpatientLongTerm = TRUE,
useConditionOccurrenceInpatientMediumTerm = TRUE,
useConditionOccurrenceInpatientShortTerm = TRUE,
useConditionEraAnyTimePrior = TRUE,
useConditionEraLongTerm = TRUE,
useConditionEraMediumTerm = TRUE,
useConditionEraShortTerm = TRUE,
useConditionEraOverlapping = FALSE,
useConditionEraStartLongTerm = TRUE,
useConditionEraStartMediumTerm = TRUE,
useConditionEraStartShortTerm = TRUE,
useConditionGroupEraAnyTimePrior = TRUE,
useConditionGroupEraLongTerm = TRUE,
useConditionGroupEraMediumTerm = TRUE,
useConditionGroupEraShortTerm = TRUE,
useConditionGroupEraOverlapping = FALSE,
useConditionGroupEraStartLongTerm = TRUE,
useConditionGroupEraStartMediumTerm = TRUE,
useConditionGroupEraStartShortTerm = TRUE,
useDrugExposureAnyTimePrior = TRUE,
useDrugExposureLongTerm = TRUE,
useDrugExposureMediumTerm = TRUE,
useDrugExposureShortTerm = TRUE,
useDrugEraAnyTimePrior = TRUE,
useDrugEraLongTerm = TRUE,
useDrugEraMediumTerm = TRUE,
useDrugEraShortTerm = TRUE,
useDrugEraOverlapping = FALSE,
useDrugEraStartLongTerm = TRUE,
useDrugEraStartMediumTerm = TRUE,
useDrugEraStartShortTerm = TRUE,
useDrugGroupEraAnyTimePrior = TRUE,
useDrugGroupEraLongTerm = TRUE,
useDrugGroupEraMediumTerm = TRUE,
useDrugGroupEraShortTerm = TRUE,
useDrugGroupEraOverlapping = FALSE,
useDrugGroupEraStartLongTerm = TRUE,
useDrugGroupEraStartMediumTerm = TRUE,
useDrugGroupEraStartShortTerm = TRUE,
useProcedureOccurrenceAnyTimePrior = TRUE,
useProcedureOccurrenceLongTerm = TRUE,
useProcedureOccurrenceMediumTerm = TRUE,
useProcedureOccurrenceShortTerm = TRUE,
useDeviceExposureAnyTimePrior = TRUE,
useDeviceExposureLongTerm = TRUE,
useDeviceExposureMediumTerm = TRUE,
useDeviceExposureShortTerm = TRUE,
useMeasurementAnyTimePrior = TRUE,
useMeasurementLongTerm = TRUE,
useMeasurementMediumTerm = TRUE,
useMeasurementShortTerm = TRUE,
useMeasurementValueAnyTimePrior = TRUE,
useMeasurementValueLongTerm = TRUE,
useMeasurementValueMediumTerm = TRUE,
useMeasurementValueShortTerm = TRUE,
useMeasurementRangeGroupAnyTimePrior = TRUE,
useMeasurementRangeGroupLongTerm = TRUE,
useMeasurementRangeGroupMediumTerm = TRUE,
useMeasurementRangeGroupShortTerm = TRUE,
useObservationAnyTimePrior = TRUE,
useObservationLongTerm = TRUE,
useObservationMediumTerm = TRUE,
useObservationShortTerm = TRUE,
useCharlsonIndex = TRUE,
useDcsi = TRUE,
useChads2 = TRUE,
useChads2Vasc = TRUE,
useDistinctConditionCountLongTerm = TRUE,
useDistinctConditionCountMediumTerm = TRUE,
useDistinctConditionCountShortTerm = TRUE,
useDistinctIngredientCountLongTerm = TRUE,
useDistinctIngredientCountMediumTerm = TRUE,
useDistinctIngredientCountShortTerm = TRUE,
useDistinctProcedureCountLongTerm = TRUE,
useDistinctProcedureCountMediumTerm = TRUE,
useDistinctProcedureCountShortTerm = TRUE,
useDistinctMeasurementCountLongTerm = TRUE,
useDistinctMeasurementCountMediumTerm = TRUE,
useDistinctMeasurementCountShortTerm = TRUE,
useVisitCountLongTerm = TRUE,
useVisitCountMediumTerm = TRUE,
useVisitCountShortTerm = TRUE,
longTermStartDays = -365,
mediumTermStartDays = -180,
shortTermStartDays = -30,
endDays = -1,
includedCovariateConceptIds = c(),
addDescendantsToInclude = TRUE,
excludedCovariateConceptIds = excludedConcepts,
addDescendantsToExclude = TRUE,
includedCovariateIds = c())
covariateSettingsList <- list(covariateSettings, createDeliveryCovariateSettings(useDelivery = TRUE))
getDbCmDataArgs <- CohortMethod::createGetDbCohortMethodDataArgs(washoutPeriod = 365,
firstExposureOnly = TRUE,
removeDuplicateSubjects = TRUE,
studyStartDate = "20090101",
studyEndDate = "20121231",
excludeDrugsFromCovariates = FALSE,
covariateSettings = covariateSettingsList)
createStudyPopArgs <- CohortMethod::createCreateStudyPopulationArgs(removeSubjectsWithPriorOutcome = TRUE,
firstExposureOnly = TRUE,
washoutPeriod = 365,
removeDuplicateSubjects = TRUE,
minDaysAtRisk = 1,
riskWindowStart = 0,
addExposureDaysToStart = FALSE,
riskWindowEnd = 180,
addExposureDaysToEnd = TRUE)
fitOutcomeModelArgs1 <- CohortMethod::createFitOutcomeModelArgs(useCovariates = FALSE,
modelType = "logistic",
stratified = TRUE,
includeCovariateIds = omIncludedConcepts,
excludeCovariateIds = omExcludedConcepts,
prior = defaultPrior,
control = defaultControl)
createPsArgs1 <- CohortMethod::createCreatePsArgs(control = defaultControl) # Using only defaults
trimByPsArgs1 <- CohortMethod::createTrimByPsArgs() # Using only defaults
trimByPsToEquipoiseArgs1 <- CohortMethod::createTrimByPsToEquipoiseArgs() # Using only defaults
matchOnPsArgs1 <- CohortMethod::createMatchOnPsArgs(caliper = 0.25, caliperScale = "standardized", maxRatio = 10)
stratifyByPsArgs1 <- CohortMethod::createStratifyByPsArgs() # Using only defaults
cmAnalysis1 <- CohortMethod::createCmAnalysis(analysisId = 1,
description = "Early Pregnancy vs On-Time Pregnancy, PPMD",
getDbCohortMethodDataArgs = getDbCmDataArgs,
createStudyPopArgs = createStudyPopArgs,
createPs = TRUE,
createPsArgs = createPsArgs1,
trimByPs = FALSE,
trimByPsArgs = trimByPsArgs1,
trimByPsToEquipoise = FALSE,
trimByPsToEquipoiseArgs = trimByPsToEquipoiseArgs1,
matchOnPs = TRUE,
matchOnPsArgs = matchOnPsArgs1,
stratifyByPs = FALSE,
stratifyByPsArgs = stratifyByPsArgs1,
computeCovariateBalance = TRUE,
fitOutcomeModel = TRUE,
fitOutcomeModelArgs = fitOutcomeModelArgs1)
cmAnalysisList <- list(cmAnalysis1)
# Run the analysis ----
result <- CohortMethod::runCmAnalyses(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDb$cdmDatabaseSchema,
exposureDatabaseSchema = scratchDatabaseSchema,
exposureTable = exposureTable,
outcomeDatabaseSchema = cdmDb$resultsDatabaseSchema,
outcomeTable = "cohort",
cdmVersion = cdmVersion,
outputFolder = outputFolder,
cmAnalysisList = cmAnalysisList,
drugComparatorOutcomesList = drugComparatorOutcomesList,
getDbCohortMethodDataThreads = 1,
createPsThreads = 2,
psCvThreads = min(20, maxCores),
computeCovarBalThreads = min(3, maxCores),
createStudyPopThreads = min(3, maxCores),
trimMatchStratifyThreads = min(10, maxCores),
fitOutcomeModelThreads = max(1, round(maxCores/4)),
outcomeCvThreads = min(4, maxCores),
refitPsForEveryOutcome = FALSE)
## Summarize the results
analysisSummary <- CohortMethod::summarizeAnalyses(result)
head(analysisSummary)
# Perform Empirical Calibration ----
newSummary <- data.frame()
# Calibrate p-values:
drugComparatorOutcome <- drugComparatorOutcomesList[[1]]
for (drugComparatorOutcome in drugComparatorOutcomesList) {
for (analysisId in unique(analysisSummary$analysisId)) {
subset <- analysisSummary[analysisSummary$analysisId == analysisId &
analysisSummary$targetId == drugComparatorOutcome$targetId &
analysisSummary$comparatorId == drugComparatorOutcome$comparatorId, ]
negControlSubset <- subset[analysisSummary$outcomeId %in% negativeControlConcepts, ]
negControlSubset <- negControlSubset[!is.na(negControlSubset$logRr) & negControlSubset$logRr != 0, ]
hoiSubset <- subset[!(analysisSummary$outcomeId %in% negativeControlConcepts), ]
hoiSubset <- hoiSubset[!is.na(hoiSubset$logRr) & hoiSubset$logRr != 0, ]
if (nrow(negControlSubset) > 10) {
null <- EmpiricalCalibration::fitMcmcNull(negControlSubset$logRr, negControlSubset$seLogRr)
# View the empirical calibration plot with only negative controls
EmpiricalCalibration::plotCalibrationEffect(negControlSubset$logRr,
negControlSubset$seLogRr)
# Save the empirical calibration plot with only negative controls
plotName <- paste("calEffectNoHois_a",analysisId, "_t", drugComparatorOutcome$targetId, "_c", drugComparatorOutcome$comparatorId, ".png", sep = "")
EmpiricalCalibration::plotCalibrationEffect(negControlSubset$logRr,
negControlSubset$seLogRr,
fileName = file.path(outputFolder, plotName))
# View the empirical calibration plot with negative controls and HOIs plotted
EmpiricalCalibration::plotCalibrationEffect(negControlSubset$logRr,
negControlSubset$seLogRr,
hoiSubset$logRr,
hoiSubset$seLogRr)
# Save the empirical calibration plot with negative controls and HOIs plotted
plotName <- paste("calEffect_a",analysisId, "_t", drugComparatorOutcome$targetId, "_c", drugComparatorOutcome$comparatorId, ".png", sep = "")
EmpiricalCalibration::plotCalibrationEffect(negControlSubset$logRr,
negControlSubset$seLogRr,
hoiSubset$logRr,
hoiSubset$seLogRr,
fileName = file.path(outputFolder, plotName))
calibratedP <- calibrateP(null, subset$logRr, subset$seLogRr)
subset$calibratedP <- calibratedP$p
subset$calibratedP_lb95ci <- calibratedP$lb95ci
subset$calibratedP_ub95ci <- calibratedP$ub95ci
mcmc <- attr(null, "mcmc")
subset$null_mean <- mean(mcmc$chain[, 1])
subset$null_sd <- 1/sqrt(mean(mcmc$chain[, 2]))
} else {
subset$calibratedP <- NA
subset$calibratedP_lb95ci <- NA
subset$calibratedP_ub95ci <- NA
subset$null_mean <- NA
subset$null_sd <- NA
}
newSummary <- rbind(newSummary, subset)
}
}
# Results ----
drugComparatorOutcome <- drugComparatorOutcomesList[[1]]
for (drugComparatorOutcome in drugComparatorOutcomesList) {
for (analysisId in unique(analysisSummary$analysisId)) {
currentAnalysisSubset <- analysisSummary[analysisSummary$analysisId == analysisId &
analysisSummary$targetId == drugComparatorOutcome$targetId &
analysisSummary$comparatorId == drugComparatorOutcome$comparatorId &
analysisSummary$outcomeId %in% outcomeList, ]
for(currentOutcomeId in unique(currentAnalysisSubset$outcomeId)) {
outputImageSuffix <- paste0("_a",analysisId, "_t", currentAnalysisSubset$targetId, "_c", currentAnalysisSubset$comparatorId, "_o", currentOutcomeId, ".png")
cohortMethodFile <- result$cohortMethodDataFolder[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
cohortMethodData <- loadCohortMethodData(cohortMethodFile)
studyPopFile <- result$studyPopFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
# Return the attrition table for the study population ----
studyPop <- readRDS(studyPopFile)
getAttritionTable(studyPop)
# View the attrition diagram
drawAttritionDiagram(studyPop,
treatmentLabel = "Target",
comparatorLabel = "Comparator")
# Save the attrition diagram ----
plotName <- paste0("attritionDiagram", outputImageSuffix);
drawAttritionDiagram(studyPop,
treatmentLabel = "Target",
comparatorLabel = "Comparator",
fileName = file.path(outputFolder, plotName))
psFile <- result$psFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
ps <- readRDS(psFile)
# Compute the area under the receiver-operator curve (AUC) for the propensity score model ----
CohortMethod::computePsAuc(ps)
# Plot the propensity score distribution ----
CohortMethod::plotPs(ps,
scale = "preference")
# Save the propensity score distribution ----
plotName <- paste0("propensityScorePlot", outputImageSuffix);
CohortMethod::plotPs(ps,
scale = "preference",
fileName = file.path(outputFolder, plotName))
# Inspect the propensity model ----
propensityModel <- CohortMethod::getPsModel(ps, cohortMethodData)
head(propensityModel)
strataFile <- result$strataFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
strataPop <- readRDS(strataFile)
# View PS With Population Trimmed By Percentile ----
CohortMethod::plotPs(strataPop,
ps,
scale = "preference")
# Save PS With Population Trimmed By Percentile ----
plotName <- paste0("propensityScorePlotStrata", outputImageSuffix);
CohortMethod::plotPs(strataPop,
ps,
scale = "preference",
fileName = file.path(outputFolder, plotName))
# Get the attrition table and diagram for the strata pop ----
CohortMethod::getAttritionTable(strataPop)
# View the attrition diagram for the strata pop ----
CohortMethod::drawAttritionDiagram(strataPop)
# Save the attrition diagram for the strata pop ----
plotName <- paste0("attritionDiagramStrata", outputImageSuffix);
CohortMethod::drawAttritionDiagram(strataPop,
fileName = file.path(outputFolder, plotName))
# Plot the covariate balance ----
balanceFile <- result$covariateBalanceFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
balance <- readRDS(balanceFile)
# View the covariate balance scatter plot ----
CohortMethod::plotCovariateBalanceScatterPlot(balance)
# Save the covariate balance scatter plot ----
plotName <- paste0("covBalScatter", outputImageSuffix);
CohortMethod::plotCovariateBalanceScatterPlot(balance,
fileName = file.path(outputFolder, plotName))
# View the plot of top variables ----
CohortMethod::plotCovariateBalanceOfTopVariables(balance)
# Save the plot of top variables ----
plotName <- paste0("covBalTop", outputImageSuffix);
CohortMethod::plotCovariateBalanceOfTopVariables(balance,
fileName = file.path(outputFolder, plotName))
# Outcome Model ----
outcomeFile <- result$outcomeModelFile[result$target == currentAnalysisSubset$targetId &
result$comparatorId == currentAnalysisSubset$comparatorId &
result$outcomeId == currentOutcomeId &
result$analysisId == analysisId]
outcomeModel <- readRDS(outcomeFile)
# Calibrated results -----
outcomeSummary <- newSummary[newSummary$targetId == currentAnalysisSubset$targetId &
newSummary$comparatorId == currentAnalysisSubset$comparatorId &
newSummary$outcomeId == currentOutcomeId &
newSummary$analysisId == analysisId, ]
outcomeSummaryOutput <- data.frame(outcomeSummary$rr,
outcomeSummary$ci95lb,
outcomeSummary$ci95ub,
outcomeSummary$logRr,
outcomeSummary$seLogRr,
outcomeSummary$p,
outcomeSummary$calibratedP,
outcomeSummary$calibratedP_lb95ci,
outcomeSummary$calibratedP_ub95ci,
outcomeSummary$null_mean,
outcomeSummary$null_sd)
colnames(outcomeSummaryOutput) <- c("Estimate",
"lower .95",
"upper .95",
"logRr",
"seLogRr",
"p",
"cal p",
"cal p - lower .95",
"cal p - upper .95",
"null mean",
"null sd")
rownames(outcomeSummaryOutput) <- "treatment"
if (!blindOutcomeModel)
{
# View the outcome model -----
outcomeModelOutput <- capture.output(outcomeModel)
outcomeModelOutput <- head(outcomeModelOutput,n=length(outcomeModelOutput)-2)
outcomeSummaryOutput <- capture.output(printCoefmat(outcomeSummaryOutput))
outcomeModelOutput <- c(outcomeModelOutput, outcomeSummaryOutput)
writeLines(outcomeModelOutput)
}
}
}
}
}
modelling <- function()
{
}
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