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Export.R
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# Copyright 2018 Observational Health Data Sciences and Informatics
#
# This file is part of Legend
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#' Export all results to tables
#'
#' @description
#' Outputs all results to a folder called 'export', and zips them.
#'
#' @param indicationId A string denoting the indicationId for which the results should be
#' exported.
#' @param outputFolder Name of local folder to place results; make sure to use forward slashes
#' (/). Do not use a folder on a network drive since this greatly impacts
#' performance.
#' @param databaseId A short string for identifying the database (e.g. 'Synpuf').
#' @param databaseName The full name of the database.
#' @param databaseDescription A short description (several sentences) of the database.
#' @param minCellCount The minimum cell count for fields contains person counts or fractions.
#' @param maxCores How many parallel cores should be used? If more cores are made
#' available this can speed up the analyses.
#'
#' @export
exportResults <- function(indicationId = "Depression",
outputFolder,
databaseId,
databaseName,
databaseDescription,
minCellCount = 5,
maxCores) {
indicationFolder <- file.path(outputFolder, indicationId)
exportFolder <- file.path(indicationFolder, "export")
if (!file.exists(exportFolder)) {
dir.create(exportFolder, recursive = TRUE)
}
exportIndication(indicationId = indicationId,
outputFolder = outputFolder,
exportFolder = exportFolder,
databaseId = databaseId)
exportAnalyses(indicationId = indicationId,
outputFolder = outputFolder,
exportFolder = exportFolder,
databaseId = databaseId)
exportExposures(indicationId = indicationId,
outputFolder = outputFolder,
exportFolder = exportFolder,
databaseId = databaseId)
exportOutcomes(indicationId = indicationId,
outputFolder = outputFolder,
exportFolder = exportFolder,
databaseId = databaseId)
exportMetadata(indicationId = indicationId,
outputFolder = outputFolder,
exportFolder = exportFolder,
databaseId = databaseId,
databaseName = databaseName,
databaseDescription = databaseDescription,
minCellCount = minCellCount)
exportMainResults(indicationId = indicationId,
outputFolder = outputFolder,
exportFolder = exportFolder,
databaseId = databaseId,
minCellCount = minCellCount,
maxCores = maxCores)
exportDiagnostics(indicationId = indicationId,
outputFolder = outputFolder,
exportFolder = exportFolder,
databaseId = databaseId,
minCellCount = minCellCount,
maxCores = maxCores)
# Add all to zip file -------------------------------------------------------------------------------
ParallelLogger::logInfo("Adding results to zip file")
zipName <- file.path(exportFolder, paste0("Results", indicationId, databaseId, ".zip"))
files <- list.files(exportFolder, pattern = ".*\\.csv$")
oldWd <- setwd(exportFolder)
on.exit(setwd(oldWd))
DatabaseConnector::createZipFile(zipFile = zipName, files = files)
ParallelLogger::logInfo("Results are ready for sharing at:", zipName)
}
swapColumnContents <- function(df, column1 = "targetId", column2 = "comparatorId") {
temp <- df[, column1]
df[, column1] <- df[, column2]
df[, column2] <- temp
return(df)
}
getAsymAnalysisIds <- function() {
cmAnalysisListFile <- system.file("settings",
sprintf("cmAnalysisListAsym%s.json", indicationId),
package = "Legend")
cmAnalysisListAsym <- CohortMethod::loadCmAnalysisList(cmAnalysisListFile)
analysisIds <- as.vector(unlist(ParallelLogger::selectFromList(cmAnalysisListAsym, "analysisId")))
return(analysisIds)
}
enforceMinCellValue <- function(data, fieldName, minValues, silent = FALSE) {
toCensor <- !is.na(data[, fieldName]) & data[, fieldName] < minValues & data[, fieldName] != 0
if (!silent) {
percent <- round(100 * sum(toCensor)/nrow(data), 1)
ParallelLogger::logInfo(" censoring ",
sum(toCensor),
" values (",
percent,
"%) from ",
fieldName,
" because value below minimum")
}
if (length(minValues) == 1) {
data[toCensor, fieldName] <- -minValues
} else {
data[toCensor, fieldName] <- -minValues[toCensor]
}
return(data)
}
exportIndication <- function(indicationId, outputFolder, exportFolder, databaseId) {
ParallelLogger::logInfo("Exporting indication")
ParallelLogger::logInfo("- indication table")
pathToCsv <- system.file("settings", "Indications.csv", package = "Legend")
indications <- read.csv(pathToCsv)
indications$definition <- ""
indications <- indications[indications$indicationId == indicationId, ]
indicationTable <- indications[, c("indicationId", "indicationName", "definition")]
colnames(indicationTable) <- SqlRender::camelCaseToSnakeCase(colnames(indicationTable))
fileName <- file.path(exportFolder, "indication.csv")
write.csv(indicationTable, fileName, row.names = FALSE)
}
exportAnalyses <- function(indicationId, outputFolder, exportFolder, databaseId) {
ParallelLogger::logInfo("Exporting analyses")
ParallelLogger::logInfo("- cohort_method_analysis table")
tempFileName <- tempfile()
cmAnalysisListFile <- system.file("settings",
sprintf("cmAnalysisList%s.json", indicationId),
package = "Legend")
cmAnalysisList <- CohortMethod::loadCmAnalysisList(cmAnalysisListFile)
cmAnalysisListFile <- system.file("settings",
sprintf("cmAnalysisListInteractions%s.json", indicationId),
package = "Legend")
cmAnalysisList <- c(cmAnalysisList, CohortMethod::loadCmAnalysisList(cmAnalysisListFile))
cmAnalysisListFile <- system.file("settings",
sprintf("cmAnalysisListAsym%s.json", indicationId),
package = "Legend")
cmAnalysisList <- c(cmAnalysisList, CohortMethod::loadCmAnalysisList(cmAnalysisListFile))
cmAnalysisToRow <- function(cmAnalysis) {
ParallelLogger::saveSettingsToJson(cmAnalysis, tempFileName)
row <- data.frame(analysisId = cmAnalysis$analysisId,
description = cmAnalysis$description,
definition = readChar(tempFileName, file.info(tempFileName)$size))
return(row)
}
cohortMethodAnalysis <- lapply(cmAnalysisList, cmAnalysisToRow)
cohortMethodAnalysis <- do.call("rbind", cohortMethodAnalysis)
cohortMethodAnalysis <- unique(cohortMethodAnalysis)
unlink(tempFileName)
colnames(cohortMethodAnalysis) <- SqlRender::camelCaseToSnakeCase(colnames(cohortMethodAnalysis))
fileName <- file.path(exportFolder, "cohort_method_analysis.csv")
write.csv(cohortMethodAnalysis, fileName, row.names = FALSE)
ParallelLogger::logInfo("- covariate_analysis table")
indicationFolder <- file.path(outputFolder, indicationId)
if (!file.exists(file.path(indicationFolder, "allCovariates"))) {
warning("Can't find allCovariates, skipping covariate_analysis table")
} else {
covariateData <- FeatureExtraction::loadCovariateData(file.path(indicationFolder,
"allCovariates"))
covariateAnalysis <- ff::as.ram(covariateData$analysisRef)
covariateAnalysis <- covariateAnalysis[, c("analysisId", "analysisName")]
colnames(covariateAnalysis) <- c("covariate_analysis_id", "covariate_analysis_name")
fileName <- file.path(exportFolder, "covariate_analysis.csv")
write.csv(covariateAnalysis, fileName, row.names = FALSE)
}
ParallelLogger::logInfo("- incidence_analysis table")
incidenceAnalysis <- data.frame(incidence_analysis_id = c("On-treatment", "Intent-to-treat"),
description = c("On-treatment", "Intent-to-treat"))
fileName <- file.path(exportFolder, "incidence_analysis.csv")
write.csv(incidenceAnalysis, fileName, row.names = FALSE)
}
exportExposures <- function(indicationId, outputFolder, exportFolder, databaseId) {
ParallelLogger::logInfo("Exporting exposures")
ParallelLogger::logInfo("- single_exposure_of_interest table")
pathToCsv <- system.file("settings", "ExposuresOfInterest.csv", package = "Legend")
exposuresOfInterest <- read.csv(pathToCsv)
singleExposuresOfInterest <- exposuresOfInterest[, c("cohortId", "name", "indicationId")]
singleExposuresOfInterest$definition <- ""
singleExposuresOfInterest$filterConceptIds <- ""
singleExposuresOfInterest$description <- sprintf("First use of %s",
tolower(singleExposuresOfInterest$name))
idx <- singleExposuresOfInterest$indicationId == "Hypertension"
singleExposuresOfInterest$description[idx] <- sprintf("First-line %s monotherapy",
tolower(singleExposuresOfInterest$name[idx]))
singleExposuresOfInterest <- singleExposuresOfInterest[singleExposuresOfInterest$indicationId ==
indicationId, ]
colnames(singleExposuresOfInterest) <- c("exposure_id",
"exposure_name",
"indication_id",
"definition",
"filter_concept_ids",
"description")
fileName <- file.path(exportFolder, "single_exposure_of_interest.csv")
write.csv(singleExposuresOfInterest, fileName, row.names = FALSE)
ParallelLogger::logInfo("- combi_exposure_of_interest table")
pathToCsv <- file.path(outputFolder, indicationId, "exposureCombis.csv")
if (file.exists(pathToCsv)) {
combiExposures <- read.csv(pathToCsv, stringsAsFactors = FALSE)
combiExposures$indicationId <- indicationId
combiExposures <- combiExposures[, c("cohortDefinitionId",
"cohortName",
"exposureId1",
"exposureId2",
"indicationId")]
combiExposures$description <- sprintf("First-line dual therapy of %s",
tolower(gsub("&", "and", combiExposures$cohortName)))
colnames(combiExposures) <- c("exposure_id",
"exposure_name",
"single_exposure_id_1",
"single_exposure_id_2",
"indication_id",
"description")
} else {
combiExposures <- NULL
}
if (is.null(combiExposures) || nrow(combiExposures) == 0) {
combiExposures <- data.frame(exposure_id = -1,
exposure_name = "dummy",
single_exposure_id_1 = -1,
single_exposure_id_2 = -1,
indication_id = "Dummy",
description = "dummy")
}
fileName <- file.path(exportFolder, "combi_exposure_of_interest.csv")
write.csv(combiExposures, fileName, row.names = FALSE)
ParallelLogger::logInfo("- exposure_group table")
pathToCsv <- system.file("settings", "ExposuresOfInterest.csv", package = "Legend")
exposuresOfInterest <- read.csv(pathToCsv)
exposureGroup <- exposuresOfInterest[exposuresOfInterest$indicationId == indicationId, c("cohortId",
"type")]
pathToCsv <- file.path(outputFolder, indicationId, "exposureCombis.csv")
if (file.exists(pathToCsv)) {
combiExposures <- read.csv(pathToCsv, stringsAsFactors = FALSE)
exposureGroup <- rbind(exposureGroup,
data.frame(cohortId = combiExposures$cohortDefinitionId,
type = combiExposures$exposureType))
}
colnames(exposureGroup) <- c("exposure_id", "exposure_group")
fileName <- file.path(exportFolder, "exposure_group.csv")
write.csv(exposureGroup, fileName, row.names = FALSE)
}
exportOutcomes <- function(indicationId, outputFolder, exportFolder, databaseId) {
ParallelLogger::logInfo("Exporting outcomes")
ParallelLogger::logInfo("- outcome_of_interest table")
pathToCsv <- system.file("settings", "OutcomesOfInterest.csv", package = "Legend")
outcomesOfInterest <- read.csv(pathToCsv, stringsAsFactors = FALSE)
outcomesOfInterest <- outcomesOfInterest[outcomesOfInterest$indicationId == indicationId, ]
getDefinition <- function(name) {
fileName <- system.file("cohorts", paste0(name, ".json"), package = "Legend")
return(readChar(fileName, file.info(fileName)$size))
}
outcomesOfInterest$definition <- sapply(outcomesOfInterest$name, getDefinition)
outcomesOfInterest$description <- ""
outcomesOfInterest <- outcomesOfInterest[, c("cohortId",
"name",
"definition",
"indicationId",
"description")]
colnames(outcomesOfInterest) <- c("outcome_id",
"outcome_name",
"definition",
"indication_id",
"description")
fileName <- file.path(exportFolder, "outcome_of_interest.csv")
write.csv(outcomesOfInterest, fileName, row.names = FALSE)
ParallelLogger::logInfo("- negative_control_outcome table")
pathToCsv <- system.file("settings", "NegativeControls.csv", package = "Legend")
negativeControls <- read.csv(pathToCsv)
negativeControls <- negativeControls[negativeControls$indicationId == indicationId, ]
negativeControls <- negativeControls[, c("cohortId", "name", "conceptId", "indicationId")]
colnames(negativeControls) <- c("outcome_id", "outcome_name", "concept_id", "indication_id")
fileName <- file.path(exportFolder, "negative_control_outcome.csv")
write.csv(negativeControls, fileName, row.names = FALSE)
colnames(negativeControls) <- SqlRender::snakeCaseToCamelCase(colnames(negativeControls)) # Need this later
ParallelLogger::logInfo("- positive_control_outcome table")
pathToCsv <- file.path(outputFolder, indicationId, "signalInjectionSummary.csv")
positiveControls <- read.csv(pathToCsv, stringsAsFactors = FALSE)
positiveControls$indicationId <- indicationId
positiveControls <- merge(positiveControls,
negativeControls[negativeControls$indicationId == indicationId,
c("outcomeId", "outcomeName")])
positiveControls$outcomeName <- paste0(positiveControls$outcomeName,
", RR = ",
positiveControls$targetEffectSize)
positiveControls <- positiveControls[, c("newOutcomeId",
"outcomeName",
"exposureId",
"outcomeId",
"targetEffectSize",
"indicationId")]
colnames(positiveControls) <- c("outcomeId",
"outcomeName",
"exposureId",
"negativeControlId",
"effectSize",
"indication_id")
colnames(positiveControls) <- SqlRender::camelCaseToSnakeCase(colnames(positiveControls))
fileName <- file.path(exportFolder, "positive_control_outcome.csv")
write.csv(positiveControls, fileName, row.names = FALSE)
}
exportMetadata <- function(indicationId,
outputFolder,
exportFolder,
databaseId,
databaseName,
databaseDescription,
minCellCount) {
ParallelLogger::logInfo("Exporting metadata")
ParallelLogger::logInfo("- database table")
database <- data.frame(database_id = databaseId,
database_name = databaseName,
description = databaseDescription,
is_meta_analysis = 0)
fileName <- file.path(exportFolder, "database.csv")
write.csv(database, fileName, row.names = FALSE)
ParallelLogger::logInfo("- exposure_summary table")
pathToCsv <- file.path(outputFolder, indicationId, "pairedExposureSummary.csv")
if (file.exists(pathToCsv)) {
exposurePairs <- read.csv(pathToCsv, stringsAsFactors = FALSE)
} else {
warning("Can't find pairedExposureSummary.csv, so using pairedExposureSummaryFilteredBySize")
pathToCsv <- file.path(outputFolder, indicationId, "pairedExposureSummaryFilteredBySize.csv")
exposurePairs <- read.csv(pathToCsv, stringsAsFactors = FALSE)
}
exposurePairs$databaseId <- databaseId
exposureSummary1 <- exposurePairs[, c("databaseId", "targetId", "targetMinDate", "targetMaxDate")]
colnames(exposureSummary1) <- c("databaseId", "exposureId", "minDate", "maxDate")
exposureSummary2 <- exposurePairs[, c("databaseId",
"comparatorId",
"comparatorMinDate",
"comparatorMaxDate")]
colnames(exposureSummary2) <- c("databaseId", "exposureId", "minDate", "maxDate")
exposureSummary <- unique(rbind(exposureSummary1, exposureSummary2))
colnames(exposureSummary) <- SqlRender::camelCaseToSnakeCase(colnames(exposureSummary))
fileName <- file.path(exportFolder, "exposure_summary.csv")
write.csv(exposureSummary, fileName, row.names = FALSE)
ParallelLogger::logInfo("- comparison_summary table")
exposurePairs <- exposurePairs[, c("databaseId",
"targetId",
"comparatorId",
"pairedMinDate",
"pairedMaxDate")]
colnames(exposurePairs) <- c("databaseId", "targetId", "comparatorId", "minDate", "maxDate")
exposurePairs <- rbind(exposurePairs,
swapColumnContents(exposurePairs, "targetId", "comparatorId"))
colnames(exposurePairs) <- SqlRender::camelCaseToSnakeCase(colnames(exposurePairs))
fileName <- file.path(exportFolder, "comparison_summary.csv")
write.csv(exposurePairs, fileName, row.names = FALSE)
ParallelLogger::logInfo("- attrition table")
pathToCsv <- system.file("settings", "OutcomesOfInterest.csv", package = "Legend")
outcomesOfInterest <- read.csv(pathToCsv, stringsAsFactors = FALSE)
fileName <- file.path(exportFolder, "attrition.csv")
if (file.exists(fileName)) {
unlink(fileName)
}
pathToRds <- file.path(outputFolder, indicationId, "cmOutput", "outcomeModelReference1.rds")
if (!file.exists(pathToRds)) {
warning("Cannot find ", pathToRds)
return(NULL)
}
outcomeModelReference1 <- readRDS(pathToRds)
pathToRds <- file.path(outputFolder, indicationId, "cmOutput", "outcomeModelReference2.rds")
outcomeModelReference2 <- readRDS(pathToRds)
pathToRds <- file.path(outputFolder, indicationId, "cmOutput", "outcomeModelReference3.rds")
outcomeModelReference3 <- readRDS(pathToRds)
outcomeModelReference <- rbind(outcomeModelReference1,
outcomeModelReference2,
outcomeModelReference3)
outcomeModelReference <- outcomeModelReference[outcomeModelReference$outcomeId %in% outcomesOfInterest$cohortId, ]
# Flag assymmetric analyses:
analysisIds <- Legend:::getAsymAnalysisIds()
outcomeModelReference$symmetrical <- TRUE
outcomeModelReference$symmetrical[outcomeModelReference$analysisId %in% analysisIds] <- FALSE
first <- !file.exists(fileName)
pb <- txtProgressBar(style = 3)
for (i in 1:nrow(outcomeModelReference)) {
outcomeModel <- readRDS(file.path(outputFolder,
indicationId,
"cmOutput",
outcomeModelReference$outcomeModelFile[i]))
attrition <- outcomeModel$attrition[, c("description", "targetPersons", "comparatorPersons")]
attrition <- attrition[2:nrow(attrition), ] # First row is duplicate of last row from DB pull
attrition$sequenceNumber <- 1:nrow(attrition) + 5
attrition1 <- attrition[, c("sequenceNumber", "description", "targetPersons")]
colnames(attrition1)[3] <- "subjects"
attrition1$exposureId <- outcomeModelReference$targetId[i]
attrition2 <- attrition[, c("sequenceNumber", "description", "comparatorPersons")]
colnames(attrition2)[3] <- "subjects"
attrition2$exposureId <- outcomeModelReference$comparatorId[i]
attrition <- rbind(attrition1, attrition2)
attrition$targetId <- outcomeModelReference$targetId[i]
attrition$comparatorId <- outcomeModelReference$comparatorId[i]
if (outcomeModelReference$symmetrical[i]) {
attrition <- rbind(attrition,
Legend:::swapColumnContents(attrition, "targetId", "comparatorId"))
}
attrition$analysisId <- outcomeModelReference$analysisId[i]
attrition$outcomeId <- outcomeModelReference$outcomeId[i]
attrition$databaseId <- databaseId
attrition <- attrition[, c("databaseId",
"exposureId",
"targetId",
"comparatorId",
"outcomeId",
"analysisId",
"sequenceNumber",
"description",
"subjects")]
attrition <- Legend:::enforceMinCellValue(attrition, "subjects", minCellCount, silent = TRUE)
colnames(attrition) <- SqlRender::camelCaseToSnakeCase(colnames(attrition))
write.table(x = attrition,
file = fileName,
row.names = FALSE,
col.names = first,
sep = ",",
dec = ".",
qmethod = "double",
append = !first)
first <- FALSE
if (i %% 1000 == 10) {
setTxtProgressBar(pb, i/nrow(outcomeModelReference))
}
}
setTxtProgressBar(pb, 1)
close(pb)
pathToCsv <- file.path(outputFolder, indicationId, "attrition.csv")
if (!file.exists(pathToCsv)) {
warning("Cannot find attrition.csv (attrition from DB), not adding to attrition table")
} else {
attritionFromDb <- read.csv(pathToCsv, stringsAsFactors = FALSE)
attritionFromDb$targetId[attritionFromDb$targetId == -1] <- NA
attritionFromDb$comparatorId[attritionFromDb$comparatorId == -1] <- NA
attritionFromDbTc <- attritionFromDb[!is.na(attritionFromDb$targetId), ]
attritionFromDb <- rbind(attritionFromDb, Legend:::swapColumnContents(attritionFromDbTc,
"targetId",
"comparatorId"))
attritionFromDb$analysisId <- NA
attritionFromDb$outcomeId <- NA
attritionFromDb$databaseId <- databaseId
attritionFromDb <- attritionFromDb[, c("databaseId",
"exposureId",
"targetId",
"comparatorId",
"outcomeId",
"analysisId",
"sequenceNumber",
"description",
"subjects")]
colnames(attritionFromDb) <- SqlRender::camelCaseToSnakeCase(colnames(attritionFromDb))
write.table(x = attritionFromDb,
file = fileName,
row.names = FALSE,
col.names = first,
sep = ",",
dec = ".",
qmethod = "double",
append = !first)
}
ParallelLogger::logInfo("- covariate table")
covariateFolder <- file.path(outputFolder, indicationId, "allCovariates")
if (!file.exists(pathToCsv)) {
warning("Cannot find allCovariates folder, skipping covariate table")
} else {
covariateData <- FeatureExtraction::loadCovariateData(covariateFolder)
covariateNames <- ff::as.ram(covariateData$covariateRef[,
c("covariateId", "covariateName", "analysisId")])
covariateNames <- unique(covariateNames)
covariateNames$databaseId <- databaseId
colnames(covariateNames)[colnames(covariateNames) == "analysisId"] <- "covariateAnalysisId"
colnames(covariateNames) <- SqlRender::camelCaseToSnakeCase(colnames(covariateNames))
fileName <- file.path(exportFolder, "covariate.csv")
write.csv(covariateNames, fileName, row.names = FALSE)
rm(covariateNames) # Free up memory
}
ParallelLogger::logInfo("- cm_follow_up_dist table")
pathToCsv <- system.file("settings", "OutcomesOfInterest.csv", package = "Legend")
outcomesOfInterest <- read.csv(pathToCsv, stringsAsFactors = FALSE)
pathToRds <- file.path(outputFolder, indicationId, "cmOutput", "outcomeModelReference1.rds")
outcomeModelReference1 <- readRDS(pathToRds)
pathToRds <- file.path(outputFolder, indicationId, "cmOutput", "outcomeModelReference2.rds")
outcomeModelReference2 <- readRDS(pathToRds)
pathToRds <- file.path(outputFolder, indicationId, "cmOutput", "outcomeModelReference3.rds")
outcomeModelReference3 <- readRDS(pathToRds)
outcomeModelReference <- rbind(outcomeModelReference1,
outcomeModelReference2,
outcomeModelReference3)
outcomeModelReference <- outcomeModelReference[outcomeModelReference$outcomeId %in% outcomesOfInterest$cohortId, ]
analysisIds <- Legend:::getAsymAnalysisIds()
outcomeModelReference$symmetrical <- TRUE
outcomeModelReference$symmetrical[outcomeModelReference$analysisId %in% analysisIds] <- FALSE
getResult <- function(i) {
strataPop <- readRDS(file.path(outputFolder,
indicationId,
"cmOutput",
outcomeModelReference$strataFile[i]))
targetDist <- quantile(strataPop$survivalTime[strataPop$treatment == 1],
c(0, 0.1, 0.25, 0.5, 0.85, 0.9, 1))
comparatorDist <- quantile(strataPop$survivalTime[strataPop$treatment == 0],
c(0, 0.1, 0.25, 0.5, 0.85, 0.9, 1))
row <- data.frame(target_id = outcomeModelReference$targetId[i],
comparator_id = outcomeModelReference$comparatorId[i],
outcome_id = outcomeModelReference$outcomeId[i],
analysis_id = outcomeModelReference$analysisId[i],
target_min_days = targetDist[1],
target_p10_days = targetDist[2],
target_p25_days = targetDist[3],
target_median_days = targetDist[4],
target_p75_days = targetDist[5],
target_p90_days = targetDist[6],
target_max_days = targetDist[7],
comparator_min_days = comparatorDist[1],
comparator_p10_days = comparatorDist[2],
comparator_p25_days = comparatorDist[3],
comparator_median_days = comparatorDist[4],
comparator_p75_days = comparatorDist[5],
comparator_p90_days = comparatorDist[6],
comparator_max_days = comparatorDist[7])
if (outcomeModelReference$symmetrical[i]) {
row2 <- data.frame(target_id = outcomeModelReference$comparatorId[i],
comparator_id = outcomeModelReference$targetId[i],
outcome_id = outcomeModelReference$outcomeId[i],
analysis_id = outcomeModelReference$analysisId[i],
target_min_days = comparatorDist[1],
target_p10_days = comparatorDist[2],
target_p25_days = comparatorDist[3],
target_median_days = comparatorDist[4],
target_p75_days = comparatorDist[5],
target_p90_days = comparatorDist[6],
target_max_days = comparatorDist[7],
comparator_min_days = targetDist[1],
comparator_p10_days = targetDist[2],
comparator_p25_days = targetDist[3],
comparator_median_days = targetDist[4],
comparator_p75_days = targetDist[5],
comparator_p90_days = targetDist[6],
comparator_max_days = targetDist[7])
row <- rbind(row, row2)
}
return(row)
}
results <- plyr::llply(1:nrow(outcomeModelReference), getResult, .progress = "text")
results <- do.call("rbind", results)
results$database_id <- databaseId
fileName <- file.path(exportFolder, "cm_follow_up_dist.csv")
write.csv(results, fileName, row.names = FALSE)
rm(results) # Free up memory
}
exportMainResults <- function(indicationId,
outputFolder,
exportFolder,
databaseId,
minCellCount,
maxCores) {
ParallelLogger::logInfo("Exporting main results")
ParallelLogger::logInfo("- cohort_method_result table")
positiveControls <- read.csv(file.path(exportFolder, "positive_control_outcome.csv"))
colnames(positiveControls) <- SqlRender::snakeCaseToCamelCase(colnames(positiveControls))
negativeControls <- read.csv(file.path(exportFolder, "negative_control_outcome.csv"))
colnames(negativeControls) <- SqlRender::snakeCaseToCamelCase(colnames(negativeControls))
summaryFile1 <- file.path(outputFolder, indicationId, "analysisSummary1.csv")
analysesSum1 <- read.csv(summaryFile1)
analysesSum2 <- read.csv(file.path(outputFolder, indicationId, "analysisSummary2.csv"))
analysesSum3 <- read.csv(file.path(outputFolder, indicationId, "analysisSummary3.csv"))
if (file.exists(file.path(outputFolder, indicationId, "analysisSummary4.csv"))) {
analysesSum4 <- read.csv(file.path(outputFolder, indicationId, "analysisSummary4.csv"))
analysesSum <- rbind(analysesSum1[,
colnames(analysesSum2)],
analysesSum2,
analysesSum3,
analysesSum4)
} else {
analysesSum <- rbind(analysesSum1[,
colnames(analysesSum2)],
analysesSum2,
analysesSum3)
}
analysisIds <- Legend:::getAsymAnalysisIds()
analysesSum$symmetrical <- TRUE
analysesSum$symmetrical[analysesSum$analysisId %in% analysisIds] <- FALSE
ParallelLogger::logInfo(" Performing empirical calibration on main effects")
cluster <- ParallelLogger::makeCluster(min(6, maxCores))
subsets <- split(analysesSum,
paste(analysesSum$targetId, analysesSum$comparatorId, analysesSum$analysisId))
rm(analysesSum) # Free up memory
results <- ParallelLogger::clusterApply(cluster,
subsets,
Legend:::calibrate,
negativeControls = negativeControls,
positiveControls = positiveControls)
ParallelLogger::stopCluster(cluster)
rm(subsets) # Free up memory
results <- do.call("rbind", results)
results$databaseId <- databaseId
results <- enforceMinCellValue(results, "targetSubjects", minCellCount)
results <- enforceMinCellValue(results, "comparatorSubjects", minCellCount)
results <- enforceMinCellValue(results, "targetOutcomes", minCellCount)
results <- enforceMinCellValue(results, "comparatorOutcomes", minCellCount)
colnames(results) <- SqlRender::camelCaseToSnakeCase(colnames(results))
fileName <- file.path(exportFolder, "cohort_method_result.csv")
write.csv(results, fileName, row.names = FALSE)
rm(results) # Free up memory
ParallelLogger::logInfo("- cm_interaction_result table")
pathToRds <- file.path(outputFolder, indicationId, "cmOutput", "outcomeModelReference1.rds")
outcomeModelReference <- readRDS(pathToRds)
outcomeModelReference <- outcomeModelReference[outcomeModelReference$analysisId > 4, ]
if (nrow(outcomeModelReference) > 0) {
loadInteractionsFromOutcomeModel <- function(i) {
outcomeModel <- readRDS(file.path(outputFolder,
indicationId,
"cmOutput",
outcomeModelReference$outcomeModelFile[i]))
if (!is.null(outcomeModel$subgroupCounts)) {
rows <- data.frame(targetId = outcomeModelReference$targetId[i],
comparatorId = outcomeModelReference$comparatorId[i],
outcomeId = outcomeModelReference$outcomeId[i],
analysisId = outcomeModelReference$analysisId[i],
interactionCovariateId = outcomeModel$subgroupCounts$subgroupCovariateId,
rrr = NA,
ci95Lb = NA,
ci95Ub = NA,
p = NA,
i2 = NA,
logRrr = NA,
seLogRrr = NA,
targetSubjects = outcomeModel$subgroupCounts$targetPersons,
comparatorSubjects = outcomeModel$subgroupCounts$comparatorPersons,
targetDays = outcomeModel$subgroupCounts$targetDays,
comparatorDays = outcomeModel$subgroupCounts$comparatorDays,
targetOutcomes = outcomeModel$subgroupCounts$targetOutcomes,
comparatorOutcomes = outcomeModel$subgroupCounts$comparatorOutcomes)
if (!is.null(outcomeModel$outcomeModelInteractionEstimates)) {
idx <- match(outcomeModel$outcomeModelInteractionEstimates$covariateId,
rows$interactionCovariateId)
rows$rrr[idx] <- exp(outcomeModel$outcomeModelInteractionEstimates$logRr)
rows$ci95Lb[idx] <- exp(outcomeModel$outcomeModelInteractionEstimates$logLb95)
rows$ci95Ub[idx] <- exp(outcomeModel$outcomeModelInteractionEstimates$logUb95)
rows$logRrr[idx] <- outcomeModel$outcomeModelInteractionEstimates$logRr
rows$seLogRrr[idx] <- outcomeModel$outcomeModelInteractionEstimates$seLogRr
z <- rows$logRrr[idx]/rows$seLogRrr[idx]
rows$p[idx] <- 2 * pmin(pnorm(z), 1 - pnorm(z))
}
return(rows)
} else {
return(NULL)
}
}
interactions <- plyr::llply(1:nrow(outcomeModelReference),
loadInteractionsFromOutcomeModel,
.progress = "text")
interactions <- do.call("rbind", interactions)
ParallelLogger::logInfo(" Performing empirical calibration on interaction effects")
cluster <- ParallelLogger::makeCluster(min(6, maxCores))
subsets <- split(interactions,
paste(interactions$targetId, interactions$comparatorId, interactions$analysisId))
interactions <- ParallelLogger::clusterApply(cluster,
subsets,
Legend:::calibrateInteractions,
negativeControls = negativeControls)
ParallelLogger::stopCluster(cluster)
rm(subsets) # Free up memory
interactions <- do.call("rbind", interactions)
# Add TC -> CT swap
interactionsCt <- interactions
interactionsCt <- Legend:::swapColumnContents(interactionsCt, "targetId", "comparatorId")
interactionsCt <- Legend:::swapColumnContents(interactionsCt, "targetSubjects", "comparatorSubjects")
interactionsCt <- Legend:::swapColumnContents(interactionsCt, "targetDays", "comparatorDays")
interactionsCt <- Legend:::swapColumnContents(interactionsCt, "targetOutcomes", "comparatorOutcomes")
interactionsCt$rrr <- 1/interactionsCt$rrr
interactionsCt$logRrr <- -interactionsCt$logRrr
temp <- 1/interactionsCt$ci95Lb
interactionsCt$ci95Lb <- 1/interactionsCt$ci95Ub
interactionsCt$ci95Ub <- temp
interactions <- rbind(interactions, interactionsCt)
interactions$databaseId <- databaseId
interactions <- enforceMinCellValue(interactions, "targetSubjects", minCellCount)
interactions <- enforceMinCellValue(interactions, "comparatorSubjects", minCellCount)
interactions <- enforceMinCellValue(interactions, "targetOutcomes", minCellCount)
interactions <- enforceMinCellValue(interactions, "comparatorOutcomes", minCellCount)
colnames(interactions) <- SqlRender::camelCaseToSnakeCase(colnames(interactions))
fileName <- file.path(exportFolder, "cm_interaction_result.csv")
write.csv(interactions, fileName, row.names = FALSE)
rm(interactions) # Free up memory
}
ParallelLogger::logInfo("- incidence table")
pathToCsv <- file.path(outputFolder, indicationId, "incidence.csv")
if (!file.exists(pathToCsv)) {
warning("Can't find incidence.csv, skipping incidence table")
} else {
incidence <- read.csv(pathToCsv)
incidence$databaseId <- databaseId
incidence <- enforceMinCellValue(incidence, "subjects", minCellCount)
incidence <- enforceMinCellValue(incidence, "outcomes", minCellCount)
colnames(incidence) <- SqlRender::camelCaseToSnakeCase(colnames(incidence))
fileName <- file.path(exportFolder, "incidence.csv")
write.csv(incidence, fileName, row.names = FALSE)
}
ParallelLogger::logInfo("- chronograph table")
pathToCsv <- file.path(outputFolder, indicationId, "chronographData.csv")
if (!file.exists(pathToCsv)) {
warning("Can't find chronographData.csv, skipping chronograph table")
} else {
chronograph <- read.csv(pathToCsv)
chronograph$databaseId <- databaseId
chronograph <- chronograph[, c("databaseId",
"exposureId",
"outcomeId",
"periodId",
"outcomeCount",
"expectedCount",
"ic",
"icLow",
"icHigh")]
colnames(chronograph) <- c("databaseId",
"exposureId",
"outcomeId",
"time",
"outcomes",
"expectedOutcomes",
"ic",
"icLb",
"icUb")
# IC metric depends on number of observed outcomes, so consider together for minCellCount:
toCensor <- chronograph$outcomes < minCellCount & chronograph$outcomes != 0
percent <- round(100 * sum(toCensor)/nrow(chronograph), 1)
chronograph$outcomes[toCensor] <- minCellCount
chronograph$ic[toCensor] <- NA
chronograph$icLb[toCensor] <- NA
chronograph$icUb[toCensor] <- NA
ParallelLogger::logInfo(" censoring ",
sum(toCensor),
" values (",
percent,
"%) from outcomes, ic, icLb, icUb because value below minimum")
colnames(chronograph) <- SqlRender::camelCaseToSnakeCase(colnames(chronograph))
fileName <- file.path(exportFolder, "chronograph.csv")
write.csv(chronograph, fileName, row.names = FALSE)
}
}
calibrate <- function(subset, negativeControls, positiveControls) {
ncs <- subset[subset$outcomeId %in% negativeControls$outcomeId, ]
ncs <- ncs[!is.na(ncs$seLogRr), ]
if (nrow(ncs) > 5) {
null <- EmpiricalCalibration::fitMcmcNull(ncs$logRr, ncs$seLogRr)
calibratedP <- EmpiricalCalibration::calibrateP(null = null,
logRr = subset$logRr,
seLogRr = subset$seLogRr)
subset$calibratedP <- calibratedP$p
} else {
subset$calibratedP <- rep(NA, nrow(subset))
}
targetPcs <- merge(subset, data.frame(targetId = positiveControls$exposureId,
outcomeId = positiveControls$outcomeId,
effectSize = positiveControls$effectSize))
if (subset$symmetrical[1]) {
comparatorPcs <- merge(subset, data.frame(comparatorId = positiveControls$exposureId,
outcomeId = positiveControls$outcomeId,
effectSize = positiveControls$effectSize))
subsetTc <- subset[!(subset$outcomeId %in% comparatorPcs$outcomeId), ]
subsetCt <- subset[!(subset$outcomeId %in% targetPcs$outcomeId), ]
subsetCt <- swapColumnContents(subsetCt, "targetId", "comparatorId")
subsetCt <- swapColumnContents(subsetCt, "target", "comparator")
subsetCt <- swapColumnContents(subsetCt, "targetDays", "comparatorDays")
subsetCt <- swapColumnContents(subsetCt, "eventsTarget", "eventsComparator")
subsetCt$rr <- 1/subsetCt$rr
temp <- 1/subsetCt$ci95lb
subsetCt$ci95lb <- 1/subsetCt$ci95ub
subsetCt$ci95ub <- temp
subsetCt$logRr <- -subsetCt$logRr
comparatorPcs <- comparatorPcs[!is.na(comparatorPcs$seLogRr), ]
if (nrow(comparatorPcs) > 5) {
model <- EmpiricalCalibration::fitSystematicErrorModel(logRr = c(-ncs$logRr,
-comparatorPcs$logRr),
seLogRr = c(ncs$seLogRr,
comparatorPcs$seLogRr),
trueLogRr = c(rep(0, nrow(ncs)),
log(comparatorPcs$effectSize)),
estimateCovarianceMatrix = FALSE)
calibratedCi <- EmpiricalCalibration::calibrateConfidenceInterval(logRr = subsetCt$logRr,
seLogRr = subsetCt$seLogRr,
model = model)
subsetCt$calibratedRr <- exp(calibratedCi$logRr)
subsetCt$calibratedCi95Lb <- exp(calibratedCi$logLb95Rr)
subsetCt$calibratedCi95Ub <- exp(calibratedCi$logUb95Rr)
subsetCt$calibratedLogRr <- calibratedCi$logRr
subsetCt$calibratedSeLogRr <- calibratedCi$seLogRr
} else {
subsetCt$calibratedRr <- rep(NA, nrow(subsetCt))
subsetCt$calibratedCi95Lb <- rep(NA, nrow(subsetCt))
subsetCt$calibratedCi95Ub <- rep(NA, nrow(subsetCt))
subsetCt$calibratedLogRr <- rep(NA, nrow(subsetCt))
subsetCt$calibratedSeLogRr <- rep(NA, nrow(subsetCt))
}
} else {
subsetTc <- subset
subsetCt <- NULL
}
targetPcs <- targetPcs[!is.na(targetPcs$seLogRr), ]
if (nrow(targetPcs) > 5) {
model <- EmpiricalCalibration::fitSystematicErrorModel(logRr = c(ncs$logRr, targetPcs$logRr),
seLogRr = c(ncs$seLogRr,
targetPcs$seLogRr),
trueLogRr = c(rep(0, nrow(ncs)),
log(targetPcs$effectSize)),
estimateCovarianceMatrix = FALSE)
calibratedCi <- EmpiricalCalibration::calibrateConfidenceInterval(logRr = subsetTc$logRr,
seLogRr = subsetTc$seLogRr,
model = model)
subsetTc$calibratedRr <- exp(calibratedCi$logRr)
subsetTc$calibratedCi95Lb <- exp(calibratedCi$logLb95Rr)
subsetTc$calibratedCi95Ub <- exp(calibratedCi$logUb95Rr)
subsetTc$calibratedLogRr <- calibratedCi$logRr
subsetTc$calibratedSeLogRr <- calibratedCi$seLogRr
} else {
subsetTc$calibratedRr <- rep(NA, nrow(subsetTc))
subsetTc$calibratedCi95Lb <- rep(NA, nrow(subsetTc))
subsetTc$calibratedCi95Ub <- rep(NA, nrow(subsetTc))
subsetTc$calibratedLogRr <- rep(NA, nrow(subsetTc))
subsetTc$calibratedSeLogRr <- rep(NA, nrow(subsetTc))
}
subset <- rbind(subsetTc, subsetCt)
subset$i2 <- rep(NA, nrow(subset))
subset <- subset[, c("targetId",
"comparatorId",
"outcomeId",
"analysisId",
"rr",
"ci95lb",
"ci95ub",
"p",
"i2",
"logRr",
"seLogRr",
"target",
"comparator",
"targetDays",
"comparatorDays",
"eventsTarget",
"eventsComparator",
"calibratedP",
"calibratedRr",
"calibratedCi95Lb",
"calibratedCi95Ub",
"calibratedLogRr",
"calibratedSeLogRr")]
colnames(subset) <- c("targetId",
"comparatorId",
"outcomeId",
"analysisId",
"rr",
"ci95Lb",
"ci95Ub",
"p",
"i2",
"logRr",
"seLogRr",
"targetSubjects",
"comparatorSubjects",
"targetDays",
"comparatorDays",
"targetOutcomes",
"comparatorOutcomes",
"calibratedP",
"calibratedRr",
"calibratedCi95Lb",
"calibratedCi95Ub",
"calibratedLogRr",
"calibratedSeLogRr")
return(subset)
}
calibrateInteractions <- function(subset, negativeControls) {
ncs <- subset[subset$outcomeId %in% negativeControls$outcomeId, ]
ncs <- ncs[!is.na(ncs$seLogRr), ]
if (nrow(ncs) > 5) {
null <- EmpiricalCalibration::fitMcmcNull(ncs$logRr, ncs$seLogRr)
calibratedP <- EmpiricalCalibration::calibrateP(null = null,
logRr = subset$logRr,
seLogRr = subset$seLogRr)
subset$calibratedP <- calibratedP$p
} else {
subset$calibratedP <- rep(NA, nrow(subset))
}
return(subset)
}
exportDiagnostics <- function(indicationId,
outputFolder,
exportFolder,
databaseId,
minCellCount,
maxCores) {
ParallelLogger::logInfo("Exporting diagnostics")
exportCovariateBalance(indicationId = indicationId,
outputFolder = outputFolder,
exportFolder = exportFolder,
databaseId = databaseId,
minCellCount = minCellCount)
ParallelLogger::logInfo("- preference_score_dist table")
preparePlot <- function(i, outcomeModelReference) {
psFileName <- file.path(outputFolder,
indicationId,
"cmOutput",
outcomeModelReference$sharedPsFile[i])
if (file.exists(psFileName)) {
ps <- readRDS(psFileName)
if (min(ps$propensityScore) < max(ps$propensityScore)) {
ps <- CohortMethod:::computePreferenceScore(ps)
d1 <- density(ps$preferenceScore[ps$treatment == 1], from = 0, to = 1, n = 100)
d0 <- density(ps$preferenceScore[ps$treatment == 0], from = 0, to = 1, n = 100)
result <- data.frame(databaseId = databaseId,
targetId = outcomeModelReference$targetId[i],
comparatorId = outcomeModelReference$comparatorId[i],
preferenceScore = d1$x,
targetDensity = d1$y,
comparatorDensity = d0$y)
reversed <- swapColumnContents(swapColumnContents(result, "targetId", "comparatorId"),
"targetDensity",
"comparatorDensity")
reversed$preferenceScore <- 1 - reversed$preferenceScore
result <- rbind(result, reversed)
return(result)
}
}
return(NULL)
}
outcomeModelReference <- readRDS(file.path(outputFolder,
indicationId,
"cmOutput",
"outcomeModelReference1.rds"))
outcomeModelReference <- outcomeModelReference[order(outcomeModelReference$sharedPsFile), ]
outcomeModelReference <- outcomeModelReference[!duplicated(outcomeModelReference$sharedPsFile), ]