R/SynthesizePositiveControls.R

In chandryou/TicagrelorVsClopidogrel: A Package Skeleton for Comparative Effectiveness Studies

# Copyright 2018 Observational Health Data Sciences and Informatics
#
# This file is part of TicagrelorVsClopidogrel
#
# 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.

#' Synthesize positive controls
#'
#' @details
#' This function will synthesize positve controls based on the negative controls. The simulated outcomes
#' will be added to the cohort table.
#'
#' @param connectionDetails    An object of type \code{connectionDetails} as created using the
#'                             \code{\link[DatabaseConnector]{createConnectionDetails}} function in the
#'                             DatabaseConnector package.
#' @param cdmDatabaseSchema    Schema name where your patient-level data in OMOP CDM format resides.
#'                             Note that for SQL Server, this should include both the database and
#'                             schema name, for example 'cdm_data.dbo'.
#' @param cohortDatabaseSchema Schema name where intermediate data can be stored. You will need to have
#'                             write priviliges in this schema. Note that for SQL Server, this should
#'                             include both the database and schema name, for example 'cdm_data.dbo'.
#' @param cohortTable          The name of the table that will be created in the work database schema.
#'                             This table will hold the exposure and outcome cohorts used in this
#'                             study.
#' @param oracleTempSchema     Should be used in Oracle to specify a schema where the user has write
#'                             priviliges for storing temporary tables.
#' @param outputFolder         Name of local folder to place results; make sure to use forward slashes
#'                             (/)
#' @param maxCores             How many parallel cores should be used? If more cores are made available
#'                             this can speed up the analyses.
#'
#' @export
synthesizePositiveControls <- function(connectionDetails,
                                       cdmDatabaseSchema,
                                       cohortDatabaseSchema,
                                       cohortTable = "cohort",
                                       oracleTempSchema,
                                       outputFolder,
                                       maxCores = 1) {
  
  
  synthesisFolder <- file.path(outputFolder, "positiveControlSynthesis")
  if (!file.exists(synthesisFolder))
    dir.create(synthesisFolder)
  
  synthesisSummaryFile <- file.path(outputFolder, "SynthesisSummary.csv")
  if (!file.exists(synthesisSummaryFile)) {
    pathToCsv <- system.file("settings", "NegativeControls.csv", package = "TicagrelorVsClopidogrel")
    negativeControls <- read.csv(pathToCsv)
    exposureOutcomePairs <- data.frame(exposureId = negativeControls$targetId,
                                       outcomeId = negativeControls$outcomeId)
    exposureOutcomePairs <- unique(exposureOutcomePairs)
    pathToJson <- system.file("settings", "positiveControlSynthArgs.json", package = "TicagrelorVsClopidogrel")
    args <- ParallelLogger::loadSettingsFromJson(pathToJson)
    args$control$threads <- min(c(10, maxCores))
    
    result <- MethodEvaluation::injectSignals(connectionDetails = connectionDetails,
                                              cdmDatabaseSchema = cdmDatabaseSchema,
                                              oracleTempSchema = oracleTempSchema,
                                              exposureDatabaseSchema = cohortDatabaseSchema,
                                              exposureTable = cohortTable,
                                              outcomeDatabaseSchema = cohortDatabaseSchema,
                                              outcomeTable = cohortTable,
                                              outputDatabaseSchema = cohortDatabaseSchema,
                                              outputTable = cohortTable,
                                              createOutputTable = FALSE,
                                              exposureOutcomePairs = exposureOutcomePairs,
                                              workFolder = synthesisFolder,
                                              modelThreads = max(1, round(maxCores/8)),
                                              generationThreads = min(6, maxCores),
                                              # External args start here
                                              outputIdOffset = args$outputIdOffset,
                                              firstExposureOnly = args$firstExposureOnly,
                                              firstOutcomeOnly = args$firstOutcomeOnly,
                                              removePeopleWithPriorOutcomes = args$removePeopleWithPriorOutcomes,
                                              modelType = args$modelType,
                                              washoutPeriod = args$washoutPeriod,
                                              riskWindowStart = args$riskWindowStart,
                                              riskWindowEnd = args$riskWindowEnd,
                                              addExposureDaysToEnd = args$addExposureDaysToEnd,
                                              effectSizes = args$effectSizes,
                                              precision = args$precision,
                                              prior = args$prior,
                                              control = args$control,
                                              maxSubjectsForModel = args$maxSubjectsForModel,
                                              minOutcomeCountForModel = args$minOutcomeCountForModel,
                                              minOutcomeCountForInjection = args$minOutcomeCountForInjection,
                                              covariateSettings = args$covariateSettings
                                              # External args stop here
    )
    write.csv(result, synthesisSummaryFile, row.names = FALSE)
  } else {
    result <- read.csv(synthesisSummaryFile)
  }
  ParallelLogger::logTrace("Merging positive with negative controls ")
  pathToCsv <- system.file("settings", "NegativeControls.csv", package = "TicagrelorVsClopidogrel")
  negativeControls <- read.csv(pathToCsv)
  
  synthesisSummary <- read.csv(synthesisSummaryFile)
  synthesisSummary$targetId <- synthesisSummary$exposureId
  synthesisSummary <- merge(synthesisSummary, negativeControls)
  synthesisSummary <- synthesisSummary[synthesisSummary$trueEffectSize != 0, ]
  synthesisSummary$outcomeName <- paste0(synthesisSummary$OutcomeName, ", RR=", synthesisSummary$targetEffectSize)
  synthesisSummary$oldOutcomeId <- synthesisSummary$outcomeId
  synthesisSummary$outcomeId <- synthesisSummary$newOutcomeId
  
  pathToCsv <- system.file("settings", "NegativeControls.csv", package = "TicagrelorVsClopidogrel")
  negativeControls <- read.csv(pathToCsv)
  negativeControls$targetEffectSize <- 1
  negativeControls$trueEffectSize <- 1
  negativeControls$trueEffectSizeFirstExposure <- 1
  negativeControls$oldOutcomeId <- negativeControls$outcomeId
  allControls <- rbind(negativeControls, synthesisSummary[, names(negativeControls)])
  write.csv(allControls, file.path(outputFolder, "AllControls.csv"), row.names = FALSE)
}