R/CreateOutcomeCohorts.R

In OHDSI/Legend: Large-Scale Evidence Generation and Evaluation in a Network of Databases

# 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.


#' Create the outcome cohorts
#'
#' @details
#' This function will create the outcome cohorts following the definitions included in this package.
#'
#' @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 tablePrefix            A prefix to be used for all table names created for this study.
#' @param indicationId           A string denoting the indicationId for which the exposure cohorts
#'                               should be created.
#' @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 (/)
#'
#' @export
createOutcomeCohorts <- function(connectionDetails,
                                 cdmDatabaseSchema,
                                 cohortDatabaseSchema,
                                 tablePrefix = "legend",
                                 indicationId = "Depression",
                                 oracleTempSchema,
                                 outputFolder) {
    ParallelLogger::logInfo("Creating outcome cohorts for indicationId: ", indicationId)

    indicationFolder <- file.path(outputFolder, indicationId)
    outcomeCohortTable <- paste(tablePrefix, tolower(indicationId), "out_cohort", sep = "_")
    if (!file.exists(indicationFolder)) {
        dir.create(indicationFolder, recursive = TRUE)
    }
    conn <- DatabaseConnector::connect(connectionDetails)
    on.exit(DatabaseConnector::disconnect(conn))

    # Creating outcome of interest cohorts ------------------------------
    ParallelLogger::logInfo("- Creating outcome of interest cohorts")
    .createCohorts(connection = conn,
                   cdmDatabaseSchema = cdmDatabaseSchema,
                   cohortDatabaseSchema = cohortDatabaseSchema,
                   cohortTable = outcomeCohortTable,
                   oracleTempSchema = oracleTempSchema,
                   outputFolder = indicationFolder,
                   indicationId = indicationId)

    # Creating negative control outcome cohorts -------------------
    ParallelLogger::logInfo("- Creating negative control outcome cohorts")
    pathToCsv <- system.file("settings", "NegativeControls.csv", package = "Legend")
    negativeControls <- read.csv(pathToCsv)
    negativeControls <- negativeControls[negativeControls$indicationId == indicationId, ]
    data <- negativeControls[, c("conceptId", "cohortId")]
    colnames(data) <- SqlRender::camelCaseToSnakeCase(colnames(data))
    DatabaseConnector::insertTable(connection = conn,
                                   tableName = "#negative_controls",
                                   data = data,
                                   dropTableIfExists = TRUE,
                                   createTable = TRUE,
                                   tempTable = TRUE,
                                   oracleTempSchema = oracleTempSchema)
    sql <- SqlRender::loadRenderTranslateSql("NegativeControls.sql",
                                             "Legend",
                                             dbms = connectionDetails$dbms,
                                             oracleTempSchema = oracleTempSchema,
                                             cdm_database_schema = cdmDatabaseSchema,
                                             target_database_schema = cohortDatabaseSchema,
                                             target_cohort_table = outcomeCohortTable)
    DatabaseConnector::executeSql(conn, sql)

    sql <- "TRUNCATE TABLE #negative_controls; DROP TABLE #negative_controls;"
    sql <- SqlRender::translateSql(sql = sql,
                                   targetDialect = connectionDetails$dbms,
                                   oracleTempSchema = oracleTempSchema)$sql
    DatabaseConnector::executeSql(conn, sql, progressBar = FALSE, reportOverallTime = FALSE)


    # Count cohorts --------------------------------------------------------------------------------
    ParallelLogger::logInfo("Counting outcome cohorts")
    sql <- "SELECT cohort_definition_id, COUNT(*) AS count FROM @cohort_database_schema.@cohort_table GROUP BY cohort_definition_id"
    sql <- SqlRender::renderSql(sql,
                                cohort_database_schema = cohortDatabaseSchema,
                                cohort_table = outcomeCohortTable)$sql
    sql <- SqlRender::translateSql(sql, targetDialect = connectionDetails$dbms)$sql
    counts <- DatabaseConnector::querySql(conn, sql)
    names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))

    pathToCsv <- system.file("settings", "OutcomesOfInterest.csv", package = "Legend")
    outcomesOfInterest <- read.csv(pathToCsv)

    countsOutcomesOfInterest <- merge(counts,
                                      data.frame(cohortDefinitionId = outcomesOfInterest$cohortId,
                                                 cohortName = outcomesOfInterest$name))
    countsNegativeControls <- merge(counts, data.frame(cohortDefinitionId = negativeControls$cohortId,
                                                       cohortName = negativeControls$name))
    counts <- rbind(countsOutcomesOfInterest, countsNegativeControls)
    counts <- unique(counts)
    write.csv(counts, file.path(indicationFolder, "outcomeCohortCounts.csv"), row.names = FALSE)
}