R/SelfControlledCaseSeries.R
In ohdsi-studies/Eumaeus: Evaluating Use of Methods for Adverse Event Under Surveillance
Defines functions
createSccsAnalysesList
summarizeSccsAnalyses
computeSccsEstimates
subsetSingleSccsDataObject
subsetSccsData
runSccs
# @file CohortMethod.R
#
# Copyright 2021 Observational Health Data Sciences and Informatics
#
# This file is part of Eumaeus
#
# 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.
runSccs <- function(connectionDetails,
cdmDatabaseSchema,
cohortDatabaseSchema,
cohortTable,
outputFolder,
maxCores) {
start <- Sys.time()
sccsFolder <- file.path(outputFolder, "sccs")
if (!file.exists(sccsFolder))
dir.create(sccsFolder)
sccsSummaryFile <- file.path(outputFolder, "sccsSummary.csv")
if (!file.exists(sccsSummaryFile)) {
allControls <- loadAllControls(outputFolder)
exposureCohorts <- loadExposureCohorts(outputFolder) %>%
filter(.data$sampled == FALSE & .data$comparator == FALSE)
baseExposureIds <- exposureCohorts %>%
distinct(.data$baseExposureId) %>%
pull()
allEstimates <- list()
# baseExposureId <- baseExposureIds[1]
for (baseExposureId in baseExposureIds) {
exposures <- exposureCohorts %>%
filter(.data$baseExposureId == !!baseExposureId)
controls <- allControls %>%
filter(.data$exposureId == baseExposureId)
exposureFolder <- file.path(sccsFolder, sprintf("e_%s", baseExposureId))
if (!file.exists(exposureFolder))
dir.create(exposureFolder)
timePeriods <- splitTimePeriod(startDate = controls$startDate[1], endDate = controls$endDate[1])
timePeriods$folder <- file.path(exposureFolder, sprintf("sccsOutput_t%d", timePeriods$seqId))
sapply(timePeriods$folder[!file.exists(timePeriods$folder)], dir.create)
# i <- nrow(timePeriods)
for (i in nrow(timePeriods):1) {
periodEstimatesFile <- file.path(exposureFolder, sprintf("estimates_t%d.csv", timePeriods$seqId[i]))
if (!file.exists(periodEstimatesFile)) {
ParallelLogger::logInfo(sprintf("Executing SCCS for exposure %s and period: %s", baseExposureId, timePeriods$label[i]))
periodFolder <- timePeriods$folder[i]
estimates <- computeSccsEstimates(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDatabaseSchema,
cohortDatabaseSchema = cohortDatabaseSchema,
cohortTable = cohortTable,
exposures = exposures,
outcomeIds = controls$outcomeId,
periodFolder = periodFolder,
startDate = controls$startDate[1],
endDate = controls$endDate[1],
maxCores = maxCores)
if (i == nrow(timePeriods)) {
# All prior periods don't need to download data, can subset data of last period:
ParallelLogger::logInfo(sprintf("Subsetting SccsData objects for all periods for exposure %s", baseExposureId))
sccsDataFiles <- list.files(path = periodFolder, pattern = "SccsData_l[0-9]+.zip")
cluster <- ParallelLogger::makeCluster(min(4, maxCores))
ParallelLogger::clusterRequire(cluster, "Eumaeus")
invisible(ParallelLogger::clusterApply(cluster = cluster,
x = sccsDataFiles,
fun = subsetSccsData,
timePeriods = timePeriods))
ParallelLogger::stopCluster(cluster)
}
readr::write_csv(estimates, periodEstimatesFile)
} else {
estimates <- loadCmEstimates(periodEstimatesFile)
}
estimates$seqId <- timePeriods$seqId[i]
allEstimates[[length(allEstimates) + 1]] <- estimates
}
}
allEstimates <- bind_rows(allEstimates)
allEstimates <- allEstimates %>%
filter(.data$exposedOutcomes > 0)
readr::write_csv(allEstimates, sccsSummaryFile)
}
delta <- Sys.time() - start
message(paste("Completed SCCS analyses in", signif(delta, 3), attr(delta, "units")))
}
subsetSccsData <- function(sccsDataFile, timePeriods) {
for (i in 1:(nrow(timePeriods)-1)) {
bigSccsData <- NULL
priorPeriodFolder <- timePeriods$folder[i]
if (!file.exists(file.path(priorPeriodFolder, sccsDataFile))) {
ParallelLogger::logInfo(sprintf("- Subsetting %s to %s", sccsDataFile, timePeriods$label[i]))
if (is.null(bigSccsData)) {
lastPeriodFolder <- timePeriods$folder[nrow(timePeriods)]
bigSccsData <- SelfControlledCaseSeries::loadSccsData(file.path(lastPeriodFolder, sccsDataFile))
}
subsetSingleSccsDataObject(bigSccsData = bigSccsData,
endDate = timePeriods$endDate[i],
sccsDataFile = file.path(priorPeriodFolder, sccsDataFile))
}
}
}
subsetSingleSccsDataObject <- function(bigSccsData,
endDate,
sccsDataFile) {
sccsData <- Andromeda::andromeda()
sccsData$cases <- bigSccsData$cases %>%
collect() %>%
mutate(startDate = as.Date(paste(.data$startYear, .data$startMonth, .data$startDay, sep = "-"), format = "%Y-%m-%d")) %>%
filter(.data$startDate < !!endDate) %>%
mutate(newObservationDays = as.double(!!endDate - .data$startDate)) %>%
mutate(noninformativeEndCensor = ifelse(.data$newObservationDays < .data$observationDays, 1L, .data$noninformativeEndCensor)) %>%
mutate(observationDays = ifelse(.data$newObservationDays < .data$observationDays, .data$newObservationDays, .data$observationDays)) %>%
select(-.data$startDate, -.data$newObservationDays)
sccsData$eraRef <- bigSccsData$eraRef
sccsData$eras <- bigSccsData$eras
sccsData$eras <- sccsData$eras %>%
inner_join(select(sccsData$cases, .data$caseId, .data$observationDays), by = "caseId") %>%
filter(.data$startDay < .data$observationDays) %>%
select(-.data$observationDays)
metaData <- attr(bigSccsData, "metaData")
attr(sccsData, "metaData") <- metaData
class(sccsData) <- class(bigSccsData)
SelfControlledCaseSeries::saveSccsData(sccsData, sccsDataFile)
}
computeSccsEstimates <- function(connectionDetails,
cdmDatabaseSchema,
cohortDatabaseSchema,
cohortTable,
exposures,
outcomeIds,
startDate,
endDate,
periodFolder,
maxCores) {
sccsAnalysisList <- createSccsAnalysesList(startDate, endDate)
exposureOutcomeList <- list()
for (outcomeId in outcomeIds) {
if (nrow(exposures) == 1) {
exposureOutcome <- SelfControlledCaseSeries::createExposureOutcome(exposureId = exposures$exposureId,
exposureId2 = -1,
outcomeId = outcomeId)
exposureOutcomeList[[length(exposureOutcomeList) + 1]] <- exposureOutcome
} else {
exposureOutcome <- SelfControlledCaseSeries::createExposureOutcome(exposureId = exposures$exposureId[exposures$shot == "Both"],
exposureId2 = -1,
outcomeId = outcomeId)
exposureOutcomeList[[length(exposureOutcomeList) + 1]] <- exposureOutcome
exposureOutcome <- SelfControlledCaseSeries::createExposureOutcome(exposureId = exposures$exposureId[exposures$shot == "First"],
exposureId2 = exposures$exposureId[exposures$shot == "Second"],
outcomeId = outcomeId)
exposureOutcomeList[[length(exposureOutcomeList) + 1]] <- exposureOutcome
}
}
referenceTable <- SelfControlledCaseSeries::runSccsAnalyses(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDatabaseSchema,
outcomeDatabaseSchema = cohortDatabaseSchema,
outcomeTable = cohortTable,
exposureDatabaseSchema = cohortDatabaseSchema,
exposureTable = cohortTable,
outputFolder = periodFolder,
sccsAnalysisList = sccsAnalysisList,
exposureOutcomeList = exposureOutcomeList,
combineDataFetchAcrossOutcomes = FALSE,
getDbSccsDataThreads = min(4, maxCores),
createStudyPopulationThreads = min(10, maxCores),
createSccsIntervalDataThreads = min(10, maxCores),
fitSccsModelThreads = min(10, floor(maxCores/4)),
cvThreads = min(maxCores, 4))
estimates <- summarizeSccsAnalyses(referenceTable, periodFolder)
return(estimates)
}
summarizeSccsAnalyses <- function(referenceTable, periodFolder) {
# Custom summarize function because we want second dose as just another estimate
result <- list()
# i <- 10
for (i in 1:nrow(referenceTable)) {
# print(i)
sccsModel <- readRDS(file.path(periodFolder, as.character(referenceTable$sccsModelFile[i])))
attrition <- as.data.frame(sccsModel$metaData$attrition)
attrition <- attrition[nrow(attrition), ]
row <- referenceTable[i, c("outcomeId", "analysisId")]
row$outcomeSubjects <- attrition$outcomeSubjects
row$outcomeEvents <- attrition$outcomeEvents
row$outcomeObsPeriods <- attrition$outcomeObsPeriods
estimates <- sccsModel$estimates[grepl("^Main|^Second", sccsModel$estimates$covariateName), ]
if (!is.null(estimates) && nrow(estimates) != 0) {
for (j in 1:nrow(estimates)) {
row$exposureId <- estimates$originalEraId[j]
row$rr <- exp(estimates$logRr[j])
row$ci95lb <- exp(estimates$logLb95[j])
row$ci95ub <- exp(estimates$logUb95[j])
row$logRr <- estimates$logRr[j]
row$seLogRr <- estimates$seLogRr[j]
row$llr <- estimates$llr[j]
z <- row$logRr/row$seLogRr
row$p <- 2 * pmin(pnorm(z), 1 - pnorm(z))
covStats <- sccsModel$metaData$covariateStatistics %>%
filter(.data$covariateId == estimates$covariateId[j])
row$exposedSubjects <- covStats$personCount
row$exposedDays <- covStats$dayCount
row$exposedOutcomes <- covStats$outcomeCount
row$daysObserved <- sccsModel$metaData$daysObserved
result[[length(result) + 1]] <- row
}
}
}
result <- bind_rows(result)
return(result)
}
createSccsAnalysesList <- function(startDate, endDate) {
getDbSccsDataArgs <- SelfControlledCaseSeries::createGetDbSccsDataArgs(studyStartDate = format(startDate, "%Y%m%d"),
studyEndDate = format(endDate, "%Y%m%d"),
maxCasesPerOutcome = 250000,
deleteCovariatesSmallCount = 5,
exposureIds = c("exposureId", "exposureId2"))
createStudyPopulationArgs <- SelfControlledCaseSeries::createCreateStudyPopulationArgs(naivePeriod = 365,
firstOutcomeOnly = FALSE)
covarExposureOfInt1_28 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Main",
includeEraIds = "exposureId",
start = 1,
startAnchor = "era start",
end = 28,
endAnchor = "era start",
profileLikelihood = TRUE)
covarExposureOfInt1_42 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Main",
includeEraIds = "exposureId",
start = 1,
startAnchor = "era start",
end = 42,
endAnchor = "era start",
profileLikelihood = TRUE)
covarExposureOfInt0_1 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Main",
includeEraIds = "exposureId",
start = 0,
startAnchor = "era start",
end = 1,
endAnchor = "era start",
profileLikelihood = TRUE)
covarPreExp <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Pre-exposure",
includeEraIds = "exposureId",
start = -30,
end = -1,
endAnchor = "era start")
covarExposureOfInt2nd1_28 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Second",
includeEraIds = "exposureId2",
start = 1,
startAnchor = "era start",
end = 28,
endAnchor = "era start",
profileLikelihood = TRUE)
covarExposureOfInt2nd1_42 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Second",
includeEraIds = "exposureId2",
start = 1,
startAnchor = "era start",
end = 42,
endAnchor = "era start",
profileLikelihood = TRUE)
covarExposureOfInt2nd0_1 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Second",
includeEraIds = "exposureId2",
start = 0,
startAnchor = "era start",
end = 1,
endAnchor = "era start",
profileLikelihood = TRUE)
covarPreExp2nd <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Pre-exposure second",
includeEraIds = "exposureId2",
start = -30,
end = -1,
endAnchor = "era start")
createSccsIntervalDataArgsSimple1_28 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarPreExp,
covarExposureOfInt2nd1_28,
covarPreExp2nd)
)
createSccsIntervalDataArgsSimple1_42 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarPreExp,
covarExposureOfInt2nd1_42,
covarPreExp2nd)
)
createSccsIntervalDataArgsSimple0_1 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarPreExp,
covarExposureOfInt2nd0_1,
covarPreExp2nd)
)
ageSettings <- SelfControlledCaseSeries::createAgeCovariateSettings(ageKnots = 5,
allowRegularization = TRUE,
computeConfidenceIntervals = FALSE)
seasonalitySettings <- SelfControlledCaseSeries::createSeasonalityCovariateSettings(seasonKnots = 5,
allowRegularization = TRUE,
computeConfidenceIntervals = FALSE)
createSccsIntervalDataArgsAgeSeason1_28 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarPreExp,
covarExposureOfInt2nd1_28,
covarPreExp2nd),
ageCovariateSettings = ageSettings,
seasonalityCovariateSettings = seasonalitySettings
)
createSccsIntervalDataArgsAgeSeason1_42 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarPreExp,
covarExposureOfInt2nd1_42,
covarPreExp2nd),
ageCovariateSettings = ageSettings,
seasonalityCovariateSettings = seasonalitySettings
)
createSccsIntervalDataArgsAgeSeason0_1 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarPreExp,
covarExposureOfInt2nd0_1,
covarPreExp2nd),
ageCovariateSettings = ageSettings,
seasonalityCovariateSettings = seasonalitySettings
)
controlIntervalSettings1 <- SelfControlledCaseSeries::createControlIntervalSettings(
includeEraIds = c("exposureId", "exposureId2"),
start = -43,
startAnchor = "era start",
end = -15,
endAnchor = "era start"
)
createScriIntervalDataArgs1_28 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarExposureOfInt2nd1_28),
controlIntervalSettings = controlIntervalSettings1
)
createScriIntervalDataArgs1_42 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarExposureOfInt2nd1_42),
controlIntervalSettings = controlIntervalSettings1
)
createScriIntervalDataArgs0_1 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarExposureOfInt2nd0_1),
controlIntervalSettings = controlIntervalSettings1
)
controlIntervalSettings2 <- SelfControlledCaseSeries::createControlIntervalSettings(
includeEraIds = c("exposureId", "exposureId2"),
start = 43,
startAnchor = "era start",
end = 71,
endAnchor = "era start"
)
createScriIntervalDataArgsPost1_28 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarExposureOfInt2nd1_28),
controlIntervalSettings = controlIntervalSettings2
)
createScriIntervalDataArgsPost1_42 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarExposureOfInt2nd1_42),
controlIntervalSettings = controlIntervalSettings2
)
createScriIntervalDataArgsPost0_1 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarExposureOfInt2nd0_1),
controlIntervalSettings = controlIntervalSettings2
)
fitSccsModelArgs <- SelfControlledCaseSeries::createFitSccsModelArgs()
sccsAnalysis1 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 1,
description = "SCCS, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsSimple1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis2 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 2,
description = "SCCS adjusting for age and season, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsAgeSeason1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis3 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 3,
description = "SCRI with prior control interval, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgs1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis4 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 4,
description = "SCRI with posterior control interval, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsPost1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis5 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 5,
description = "SCCS, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsSimple1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis6 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 6,
description = "SCCS adjusting for age and season, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsAgeSeason1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis7 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 7,
description = "SCRI with prior control interval, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgs1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis8 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 8,
description = "SCRI with posterior control interval, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsPost1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis9 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 9,
description = "SCCS, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsSimple0_1,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis10 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 10,
description = "SCCS adjusting for age and season, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsAgeSeason0_1,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis11 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 11,
description = "SCRI with prior control interval, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgs0_1,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis12 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 12,
description = "SCRI with posterior control interval, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsPost0_1,
fitSccsModelArgs = fitSccsModelArgs)
# Added later
controlIntervalSettings3 <- SelfControlledCaseSeries::createControlIntervalSettings(
includeEraIds = c("exposureId", "exposureId2"),
start = 1,
startAnchor = "era start",
end = 999999,
endAnchor = "era start"
)
createScriIntervalDataArgsAllPost1_28 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarExposureOfInt2nd1_28),
controlIntervalSettings = controlIntervalSettings3
)
createScriIntervalDataArgsAllPost1_42 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarExposureOfInt2nd1_42),
controlIntervalSettings = controlIntervalSettings3
)
createScriIntervalDataArgsAllPost0_1 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarExposureOfInt2nd0_1),
controlIntervalSettings = controlIntervalSettings3
)
sccsAnalysis13 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 13,
description = "SCCS excluding pre-vaccination time, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsAllPost1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis14 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 14,
description = "SCCS excluding pre-vaccination time, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsAllPost1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis15 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 15,
description = "SCCS excluding pre-vaccination time, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsAllPost0_1,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysisList <- list(sccsAnalysis1,
sccsAnalysis2,
sccsAnalysis3,
sccsAnalysis4,
sccsAnalysis5,
sccsAnalysis6,
sccsAnalysis7,
sccsAnalysis8,
sccsAnalysis9,
sccsAnalysis10,
sccsAnalysis11,
sccsAnalysis12,
sccsAnalysis13,
sccsAnalysis14,
sccsAnalysis15)
return(sccsAnalysisList)
}
ohdsi-studies/Eumaeus documentation built on Feb. 12, 2024, 9:45 p.m.
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Defines functions createSccsAnalysesList summarizeSccsAnalyses computeSccsEstimates subsetSingleSccsDataObject subsetSccsData runSccs
# @file CohortMethod.R
#
# Copyright 2021 Observational Health Data Sciences and Informatics
#
# This file is part of Eumaeus
#
# 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.
runSccs <- function(connectionDetails,
cdmDatabaseSchema,
cohortDatabaseSchema,
cohortTable,
outputFolder,
maxCores) {
start <- Sys.time()
sccsFolder <- file.path(outputFolder, "sccs")
if (!file.exists(sccsFolder))
dir.create(sccsFolder)
sccsSummaryFile <- file.path(outputFolder, "sccsSummary.csv")
if (!file.exists(sccsSummaryFile)) {
allControls <- loadAllControls(outputFolder)
exposureCohorts <- loadExposureCohorts(outputFolder) %>%
filter(.data$sampled == FALSE & .data$comparator == FALSE)
baseExposureIds <- exposureCohorts %>%
distinct(.data$baseExposureId) %>%
pull()
allEstimates <- list()
# baseExposureId <- baseExposureIds[1]
for (baseExposureId in baseExposureIds) {
exposures <- exposureCohorts %>%
filter(.data$baseExposureId == !!baseExposureId)
controls <- allControls %>%
filter(.data$exposureId == baseExposureId)
exposureFolder <- file.path(sccsFolder, sprintf("e_%s", baseExposureId))
if (!file.exists(exposureFolder))
dir.create(exposureFolder)
timePeriods <- splitTimePeriod(startDate = controls$startDate[1], endDate = controls$endDate[1])
timePeriods$folder <- file.path(exposureFolder, sprintf("sccsOutput_t%d", timePeriods$seqId))
sapply(timePeriods$folder[!file.exists(timePeriods$folder)], dir.create)
# i <- nrow(timePeriods)
for (i in nrow(timePeriods):1) {
periodEstimatesFile <- file.path(exposureFolder, sprintf("estimates_t%d.csv", timePeriods$seqId[i]))
if (!file.exists(periodEstimatesFile)) {
ParallelLogger::logInfo(sprintf("Executing SCCS for exposure %s and period: %s", baseExposureId, timePeriods$label[i]))
periodFolder <- timePeriods$folder[i]
estimates <- computeSccsEstimates(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDatabaseSchema,
cohortDatabaseSchema = cohortDatabaseSchema,
cohortTable = cohortTable,
exposures = exposures,
outcomeIds = controls$outcomeId,
periodFolder = periodFolder,
startDate = controls$startDate[1],
endDate = controls$endDate[1],
maxCores = maxCores)
if (i == nrow(timePeriods)) {
# All prior periods don't need to download data, can subset data of last period:
ParallelLogger::logInfo(sprintf("Subsetting SccsData objects for all periods for exposure %s", baseExposureId))
sccsDataFiles <- list.files(path = periodFolder, pattern = "SccsData_l[0-9]+.zip")
cluster <- ParallelLogger::makeCluster(min(4, maxCores))
ParallelLogger::clusterRequire(cluster, "Eumaeus")
invisible(ParallelLogger::clusterApply(cluster = cluster,
x = sccsDataFiles,
fun = subsetSccsData,
timePeriods = timePeriods))
ParallelLogger::stopCluster(cluster)
}
readr::write_csv(estimates, periodEstimatesFile)
} else {
estimates <- loadCmEstimates(periodEstimatesFile)
}
estimates$seqId <- timePeriods$seqId[i]
allEstimates[[length(allEstimates) + 1]] <- estimates
}
}
allEstimates <- bind_rows(allEstimates)
allEstimates <- allEstimates %>%
filter(.data$exposedOutcomes > 0)
readr::write_csv(allEstimates, sccsSummaryFile)
}
delta <- Sys.time() - start
message(paste("Completed SCCS analyses in", signif(delta, 3), attr(delta, "units")))
}
subsetSccsData <- function(sccsDataFile, timePeriods) {
for (i in 1:(nrow(timePeriods)-1)) {
bigSccsData <- NULL
priorPeriodFolder <- timePeriods$folder[i]
if (!file.exists(file.path(priorPeriodFolder, sccsDataFile))) {
ParallelLogger::logInfo(sprintf("- Subsetting %s to %s", sccsDataFile, timePeriods$label[i]))
if (is.null(bigSccsData)) {
lastPeriodFolder <- timePeriods$folder[nrow(timePeriods)]
bigSccsData <- SelfControlledCaseSeries::loadSccsData(file.path(lastPeriodFolder, sccsDataFile))
}
subsetSingleSccsDataObject(bigSccsData = bigSccsData,
endDate = timePeriods$endDate[i],
sccsDataFile = file.path(priorPeriodFolder, sccsDataFile))
}
}
}
subsetSingleSccsDataObject <- function(bigSccsData,
endDate,
sccsDataFile) {
sccsData <- Andromeda::andromeda()
sccsData$cases <- bigSccsData$cases %>%
collect() %>%
mutate(startDate = as.Date(paste(.data$startYear, .data$startMonth, .data$startDay, sep = "-"), format = "%Y-%m-%d")) %>%
filter(.data$startDate < !!endDate) %>%
mutate(newObservationDays = as.double(!!endDate - .data$startDate)) %>%
mutate(noninformativeEndCensor = ifelse(.data$newObservationDays < .data$observationDays, 1L, .data$noninformativeEndCensor)) %>%
mutate(observationDays = ifelse(.data$newObservationDays < .data$observationDays, .data$newObservationDays, .data$observationDays)) %>%
select(-.data$startDate, -.data$newObservationDays)
sccsData$eraRef <- bigSccsData$eraRef
sccsData$eras <- bigSccsData$eras
sccsData$eras <- sccsData$eras %>%
inner_join(select(sccsData$cases, .data$caseId, .data$observationDays), by = "caseId") %>%
filter(.data$startDay < .data$observationDays) %>%
select(-.data$observationDays)
metaData <- attr(bigSccsData, "metaData")
attr(sccsData, "metaData") <- metaData
class(sccsData) <- class(bigSccsData)
SelfControlledCaseSeries::saveSccsData(sccsData, sccsDataFile)
}
computeSccsEstimates <- function(connectionDetails,
cdmDatabaseSchema,
cohortDatabaseSchema,
cohortTable,
exposures,
outcomeIds,
startDate,
endDate,
periodFolder,
maxCores) {
sccsAnalysisList <- createSccsAnalysesList(startDate, endDate)
exposureOutcomeList <- list()
for (outcomeId in outcomeIds) {
if (nrow(exposures) == 1) {
exposureOutcome <- SelfControlledCaseSeries::createExposureOutcome(exposureId = exposures$exposureId,
exposureId2 = -1,
outcomeId = outcomeId)
exposureOutcomeList[[length(exposureOutcomeList) + 1]] <- exposureOutcome
} else {
exposureOutcome <- SelfControlledCaseSeries::createExposureOutcome(exposureId = exposures$exposureId[exposures$shot == "Both"],
exposureId2 = -1,
outcomeId = outcomeId)
exposureOutcomeList[[length(exposureOutcomeList) + 1]] <- exposureOutcome
exposureOutcome <- SelfControlledCaseSeries::createExposureOutcome(exposureId = exposures$exposureId[exposures$shot == "First"],
exposureId2 = exposures$exposureId[exposures$shot == "Second"],
outcomeId = outcomeId)
exposureOutcomeList[[length(exposureOutcomeList) + 1]] <- exposureOutcome
}
}
referenceTable <- SelfControlledCaseSeries::runSccsAnalyses(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDatabaseSchema,
outcomeDatabaseSchema = cohortDatabaseSchema,
outcomeTable = cohortTable,
exposureDatabaseSchema = cohortDatabaseSchema,
exposureTable = cohortTable,
outputFolder = periodFolder,
sccsAnalysisList = sccsAnalysisList,
exposureOutcomeList = exposureOutcomeList,
combineDataFetchAcrossOutcomes = FALSE,
getDbSccsDataThreads = min(4, maxCores),
createStudyPopulationThreads = min(10, maxCores),
createSccsIntervalDataThreads = min(10, maxCores),
fitSccsModelThreads = min(10, floor(maxCores/4)),
cvThreads = min(maxCores, 4))
estimates <- summarizeSccsAnalyses(referenceTable, periodFolder)
return(estimates)
}
summarizeSccsAnalyses <- function(referenceTable, periodFolder) {
# Custom summarize function because we want second dose as just another estimate
result <- list()
# i <- 10
for (i in 1:nrow(referenceTable)) {
# print(i)
sccsModel <- readRDS(file.path(periodFolder, as.character(referenceTable$sccsModelFile[i])))
attrition <- as.data.frame(sccsModel$metaData$attrition)
attrition <- attrition[nrow(attrition), ]
row <- referenceTable[i, c("outcomeId", "analysisId")]
row$outcomeSubjects <- attrition$outcomeSubjects
row$outcomeEvents <- attrition$outcomeEvents
row$outcomeObsPeriods <- attrition$outcomeObsPeriods
estimates <- sccsModel$estimates[grepl("^Main|^Second", sccsModel$estimates$covariateName), ]
if (!is.null(estimates) && nrow(estimates) != 0) {
for (j in 1:nrow(estimates)) {
row$exposureId <- estimates$originalEraId[j]
row$rr <- exp(estimates$logRr[j])
row$ci95lb <- exp(estimates$logLb95[j])
row$ci95ub <- exp(estimates$logUb95[j])
row$logRr <- estimates$logRr[j]
row$seLogRr <- estimates$seLogRr[j]
row$llr <- estimates$llr[j]
z <- row$logRr/row$seLogRr
row$p <- 2 * pmin(pnorm(z), 1 - pnorm(z))
covStats <- sccsModel$metaData$covariateStatistics %>%
filter(.data$covariateId == estimates$covariateId[j])
row$exposedSubjects <- covStats$personCount
row$exposedDays <- covStats$dayCount
row$exposedOutcomes <- covStats$outcomeCount
row$daysObserved <- sccsModel$metaData$daysObserved
result[[length(result) + 1]] <- row
}
}
}
result <- bind_rows(result)
return(result)
}
createSccsAnalysesList <- function(startDate, endDate) {
getDbSccsDataArgs <- SelfControlledCaseSeries::createGetDbSccsDataArgs(studyStartDate = format(startDate, "%Y%m%d"),
studyEndDate = format(endDate, "%Y%m%d"),
maxCasesPerOutcome = 250000,
deleteCovariatesSmallCount = 5,
exposureIds = c("exposureId", "exposureId2"))
createStudyPopulationArgs <- SelfControlledCaseSeries::createCreateStudyPopulationArgs(naivePeriod = 365,
firstOutcomeOnly = FALSE)
covarExposureOfInt1_28 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Main",
includeEraIds = "exposureId",
start = 1,
startAnchor = "era start",
end = 28,
endAnchor = "era start",
profileLikelihood = TRUE)
covarExposureOfInt1_42 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Main",
includeEraIds = "exposureId",
start = 1,
startAnchor = "era start",
end = 42,
endAnchor = "era start",
profileLikelihood = TRUE)
covarExposureOfInt0_1 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Main",
includeEraIds = "exposureId",
start = 0,
startAnchor = "era start",
end = 1,
endAnchor = "era start",
profileLikelihood = TRUE)
covarPreExp <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Pre-exposure",
includeEraIds = "exposureId",
start = -30,
end = -1,
endAnchor = "era start")
covarExposureOfInt2nd1_28 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Second",
includeEraIds = "exposureId2",
start = 1,
startAnchor = "era start",
end = 28,
endAnchor = "era start",
profileLikelihood = TRUE)
covarExposureOfInt2nd1_42 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Second",
includeEraIds = "exposureId2",
start = 1,
startAnchor = "era start",
end = 42,
endAnchor = "era start",
profileLikelihood = TRUE)
covarExposureOfInt2nd0_1 <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Second",
includeEraIds = "exposureId2",
start = 0,
startAnchor = "era start",
end = 1,
endAnchor = "era start",
profileLikelihood = TRUE)
covarPreExp2nd <- SelfControlledCaseSeries::createEraCovariateSettings(label = "Pre-exposure second",
includeEraIds = "exposureId2",
start = -30,
end = -1,
endAnchor = "era start")
createSccsIntervalDataArgsSimple1_28 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarPreExp,
covarExposureOfInt2nd1_28,
covarPreExp2nd)
)
createSccsIntervalDataArgsSimple1_42 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarPreExp,
covarExposureOfInt2nd1_42,
covarPreExp2nd)
)
createSccsIntervalDataArgsSimple0_1 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarPreExp,
covarExposureOfInt2nd0_1,
covarPreExp2nd)
)
ageSettings <- SelfControlledCaseSeries::createAgeCovariateSettings(ageKnots = 5,
allowRegularization = TRUE,
computeConfidenceIntervals = FALSE)
seasonalitySettings <- SelfControlledCaseSeries::createSeasonalityCovariateSettings(seasonKnots = 5,
allowRegularization = TRUE,
computeConfidenceIntervals = FALSE)
createSccsIntervalDataArgsAgeSeason1_28 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarPreExp,
covarExposureOfInt2nd1_28,
covarPreExp2nd),
ageCovariateSettings = ageSettings,
seasonalityCovariateSettings = seasonalitySettings
)
createSccsIntervalDataArgsAgeSeason1_42 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarPreExp,
covarExposureOfInt2nd1_42,
covarPreExp2nd),
ageCovariateSettings = ageSettings,
seasonalityCovariateSettings = seasonalitySettings
)
createSccsIntervalDataArgsAgeSeason0_1 <- SelfControlledCaseSeries::createCreateSccsIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarPreExp,
covarExposureOfInt2nd0_1,
covarPreExp2nd),
ageCovariateSettings = ageSettings,
seasonalityCovariateSettings = seasonalitySettings
)
controlIntervalSettings1 <- SelfControlledCaseSeries::createControlIntervalSettings(
includeEraIds = c("exposureId", "exposureId2"),
start = -43,
startAnchor = "era start",
end = -15,
endAnchor = "era start"
)
createScriIntervalDataArgs1_28 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarExposureOfInt2nd1_28),
controlIntervalSettings = controlIntervalSettings1
)
createScriIntervalDataArgs1_42 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarExposureOfInt2nd1_42),
controlIntervalSettings = controlIntervalSettings1
)
createScriIntervalDataArgs0_1 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarExposureOfInt2nd0_1),
controlIntervalSettings = controlIntervalSettings1
)
controlIntervalSettings2 <- SelfControlledCaseSeries::createControlIntervalSettings(
includeEraIds = c("exposureId", "exposureId2"),
start = 43,
startAnchor = "era start",
end = 71,
endAnchor = "era start"
)
createScriIntervalDataArgsPost1_28 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarExposureOfInt2nd1_28),
controlIntervalSettings = controlIntervalSettings2
)
createScriIntervalDataArgsPost1_42 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarExposureOfInt2nd1_42),
controlIntervalSettings = controlIntervalSettings2
)
createScriIntervalDataArgsPost0_1 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarExposureOfInt2nd0_1),
controlIntervalSettings = controlIntervalSettings2
)
fitSccsModelArgs <- SelfControlledCaseSeries::createFitSccsModelArgs()
sccsAnalysis1 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 1,
description = "SCCS, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsSimple1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis2 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 2,
description = "SCCS adjusting for age and season, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsAgeSeason1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis3 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 3,
description = "SCRI with prior control interval, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgs1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis4 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 4,
description = "SCRI with posterior control interval, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsPost1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis5 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 5,
description = "SCCS, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsSimple1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis6 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 6,
description = "SCCS adjusting for age and season, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsAgeSeason1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis7 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 7,
description = "SCRI with prior control interval, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgs1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis8 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 8,
description = "SCRI with posterior control interval, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsPost1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis9 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 9,
description = "SCCS, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsSimple0_1,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis10 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 10,
description = "SCCS adjusting for age and season, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
createSccsIntervalDataArgs = createSccsIntervalDataArgsAgeSeason0_1,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis11 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 11,
description = "SCRI with prior control interval, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgs0_1,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis12 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 12,
description = "SCRI with posterior control interval, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsPost0_1,
fitSccsModelArgs = fitSccsModelArgs)
# Added later
controlIntervalSettings3 <- SelfControlledCaseSeries::createControlIntervalSettings(
includeEraIds = c("exposureId", "exposureId2"),
start = 1,
startAnchor = "era start",
end = 999999,
endAnchor = "era start"
)
createScriIntervalDataArgsAllPost1_28 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_28,
covarExposureOfInt2nd1_28),
controlIntervalSettings = controlIntervalSettings3
)
createScriIntervalDataArgsAllPost1_42 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt1_42,
covarExposureOfInt2nd1_42),
controlIntervalSettings = controlIntervalSettings3
)
createScriIntervalDataArgsAllPost0_1 <- SelfControlledCaseSeries::createCreateScriIntervalDataArgs(
eraCovariateSettings = list(covarExposureOfInt0_1,
covarExposureOfInt2nd0_1),
controlIntervalSettings = controlIntervalSettings3
)
sccsAnalysis13 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 13,
description = "SCCS excluding pre-vaccination time, tar 1-28 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsAllPost1_28,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis14 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 14,
description = "SCCS excluding pre-vaccination time, tar 1-42 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsAllPost1_42,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysis15 <- SelfControlledCaseSeries::createSccsAnalysis(analysisId = 15,
description = "SCCS excluding pre-vaccination time, tar 0-1 days",
getDbSccsDataArgs = getDbSccsDataArgs,
createStudyPopulationArgs = createStudyPopulationArgs,
design = "SCRI",
createScriIntervalDataArgs = createScriIntervalDataArgsAllPost0_1,
fitSccsModelArgs = fitSccsModelArgs)
sccsAnalysisList <- list(sccsAnalysis1,
sccsAnalysis2,
sccsAnalysis3,
sccsAnalysis4,
sccsAnalysis5,
sccsAnalysis6,
sccsAnalysis7,
sccsAnalysis8,
sccsAnalysis9,
sccsAnalysis10,
sccsAnalysis11,
sccsAnalysis12,
sccsAnalysis13,
sccsAnalysis14,
sccsAnalysis15)
return(sccsAnalysisList)
}
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