R/PhenotypeTest.R
In emaprojects/PhenotypeTesting: A package to test COVID19 Phenotypes
#' # Copyright 2020 Observational Health Data Sciences and Informatics
#' #
#' # This file is part of covidPhenotypesTest
#' #
#' # 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.
#'
#' #' Execute the cohort phenotype test
#' #'
#' #' @details
#' #' This function executes the cohorts phenotype test.
#' #'
#' #' @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
#' #' (/). 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 (e.g. 'Medicare Claims
#' #' Synthetic Public Use Files (SynPUFs)').
#' #' @param databaseDescription A short description (several sentences) of the database.
#' #' @param createCohorts Create the cohortTable table with the exposure and outcome cohorts?
#' #' @param runSimpleMonthly Pull monthly counts
#' #' @param runOverlap Pull cohort overlap
#' #' @param minCellCount The minimum number of subjects contributing to a count before it can be included
#' #' in packaged results.
#' #'
#' runPhenoTest <- function(connectionDetails,
#' cdmDatabaseSchema,
#' cohortDatabaseSchema = cdmDatabaseSchema,
#' cohortTable = "cohort",
#' oracleTempSchema = cohortDatabaseSchema,
#' outputFolder,
#' databaseId = "Unknown",
#' databaseName = "Unknown",
#' databaseDescription = "Unknown",
#' createCohorts = TRUE,
#' createBaseCohorts = TRUE,
#' runSimpleMonthly = TRUE,
#' runOverlap = TRUE,
#' runPhevaluator = TRUE,
#' minCellCount = 5) {
#'
#' if (!file.exists(outputFolder))
#' dir.create(outputFolder, recursive = TRUE)
#'
#' connection <- DatabaseConnector::connect(connectionDetails)
#'
#' if (createCohorts) {
#' ParallelLogger::logInfo("Creating cohorts")
#' .createCohorts(connection = connection,
#' cdmDatabaseSchema = cdmDatabaseSchema,
#' cohortDatabaseSchema = cohortDatabaseSchema,
#' cohortTable = cohortTable,
#' oracleTempSchema = oracleTempSchema,
#' outputFolder = outputFolder)
#' }
#'
#' if (createBaseCohorts) {
#' ParallelLogger::logInfo("Creating base cohorts")
#' .createBaseCohorts(connection = connection,
#' cdmDatabaseSchema = cdmDatabaseSchema,
#' cohortDatabaseSchema = cohortDatabaseSchema,
#' cohortTable = cohortTable,
#' oracleTempSchema = oracleTempSchema,
#' outputFolder = outputFolder)
#' }
#'
#'
#' # Fetch cohort counts:
#' pathToCsv <- system.file("settings", "CohortsToCreate.csv", package = "covidPhenotypesTest")
#' cohortsToCreate <- readr::read_csv(pathToCsv, col_types = readr::cols())
#' sql <- "SELECT cohort_definition_id, COUNT(*) AS count FROM @cohort_database_schema.@cohort_table GROUP BY cohort_definition_id"
#' sql <- SqlRender::render(sql,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable)
#' sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts <- merge(counts, data.frame(cohortDefinitionId = cohortsToCreate$cohortId,
#' cohortName = cohortsToCreate$name))
#' write.csv(counts, file.path(outputFolder, "CohortCounts.csv"))
#'
#' if(runSimpleMonthly){
#'
#' message("Calculating phenotype incidence over time")
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "MonthlyCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (2, 4, 6, 8, 10)')
#'
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#' write.csv(counts, file.path(outputFolder, "MonthlyCohortCounts.csv"))
#'
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "MonthlyDenominators.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cdm_database_schema = cdmDatabaseSchema)
#'
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#' write.csv(counts, file.path(outputFolder, "MonthlyDenominators.csv"))
#'
#' }
#'
#' if(runOverlap){
#'
#' message("Calculating phenotype overlap")
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "OverlapCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (1, 3)')
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#'
#' pathToCsv <- system.file("settings", "CohortsToCreate.csv", package = "covidPhenotypesTest")
#' cohortsToCreate <- readr::read_csv(pathToCsv, col_types = readr::cols())
#'
#' counts <- counts %>%
#' mutate(combinationName = map_chr(comboId, ~extract_bitsum(.x, str_remove(cohortsToCreate$name, '_ema_phenos__'))))
#'
#' # browser()
#' final <- counts
#'
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "OverlapCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (1, 5)')
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#'
#' counts <- counts %>%
#' mutate(combinationName = map_chr(comboId, ~extract_bitsum(.x, str_remove(cohortsToCreate$name, '_ema_phenos__'))))
#'
#' final <- rbind(final, counts)
#'
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "OverlapCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (1, 7)')
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#'
#' counts <- counts %>%
#' mutate(combinationName = map_chr(comboId, ~extract_bitsum(.x, str_remove(cohortsToCreate$name, '_ema_phenos__'))))
#'
#' final <- rbind(final, counts)
#'
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "OverlapCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (1, 9)')
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#'
#' counts <- counts %>%
#' mutate(combinationName = map_chr(comboId, ~extract_bitsum(.x, str_remove(cohortsToCreate$name, '_ema_phenos__'))))
#'
#' final <- rbind(final, counts)
#'
#' write.csv(final, file.path(outputFolder, "CohortOverlap.csv"))
#'
#' }
#'
#' if(runPhevaluator){
#'
#' prevalence <- estimate_prevalence_2020(connection, cohortDatabaseSchema, cohortTable)
#'
#' prevalence <- round(max(c(prevalence,0.05),na.rm=T),3)
#'
#' run_phevaluator_covid(connection = connection, prevalence, cdmDatabaseSchema = cdmDatabaseSchema, cohortDatabaseSchema = cohortDatabaseSchema, cohortTable = cohortTable, outputFolder = outputFolder)
#'
#' }
#'
#' DatabaseConnector::disconnect(connection)
#'
#' }
emaprojects/PhenotypeTesting documentation built on Sept. 8, 2021, 1:12 p.m.
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#' # Copyright 2020 Observational Health Data Sciences and Informatics
#' #
#' # This file is part of covidPhenotypesTest
#' #
#' # 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.
#'
#' #' Execute the cohort phenotype test
#' #'
#' #' @details
#' #' This function executes the cohorts phenotype test.
#' #'
#' #' @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
#' #' (/). 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 (e.g. 'Medicare Claims
#' #' Synthetic Public Use Files (SynPUFs)').
#' #' @param databaseDescription A short description (several sentences) of the database.
#' #' @param createCohorts Create the cohortTable table with the exposure and outcome cohorts?
#' #' @param runSimpleMonthly Pull monthly counts
#' #' @param runOverlap Pull cohort overlap
#' #' @param minCellCount The minimum number of subjects contributing to a count before it can be included
#' #' in packaged results.
#' #'
#' runPhenoTest <- function(connectionDetails,
#' cdmDatabaseSchema,
#' cohortDatabaseSchema = cdmDatabaseSchema,
#' cohortTable = "cohort",
#' oracleTempSchema = cohortDatabaseSchema,
#' outputFolder,
#' databaseId = "Unknown",
#' databaseName = "Unknown",
#' databaseDescription = "Unknown",
#' createCohorts = TRUE,
#' createBaseCohorts = TRUE,
#' runSimpleMonthly = TRUE,
#' runOverlap = TRUE,
#' runPhevaluator = TRUE,
#' minCellCount = 5) {
#'
#' if (!file.exists(outputFolder))
#' dir.create(outputFolder, recursive = TRUE)
#'
#' connection <- DatabaseConnector::connect(connectionDetails)
#'
#' if (createCohorts) {
#' ParallelLogger::logInfo("Creating cohorts")
#' .createCohorts(connection = connection,
#' cdmDatabaseSchema = cdmDatabaseSchema,
#' cohortDatabaseSchema = cohortDatabaseSchema,
#' cohortTable = cohortTable,
#' oracleTempSchema = oracleTempSchema,
#' outputFolder = outputFolder)
#' }
#'
#' if (createBaseCohorts) {
#' ParallelLogger::logInfo("Creating base cohorts")
#' .createBaseCohorts(connection = connection,
#' cdmDatabaseSchema = cdmDatabaseSchema,
#' cohortDatabaseSchema = cohortDatabaseSchema,
#' cohortTable = cohortTable,
#' oracleTempSchema = oracleTempSchema,
#' outputFolder = outputFolder)
#' }
#'
#'
#' # Fetch cohort counts:
#' pathToCsv <- system.file("settings", "CohortsToCreate.csv", package = "covidPhenotypesTest")
#' cohortsToCreate <- readr::read_csv(pathToCsv, col_types = readr::cols())
#' sql <- "SELECT cohort_definition_id, COUNT(*) AS count FROM @cohort_database_schema.@cohort_table GROUP BY cohort_definition_id"
#' sql <- SqlRender::render(sql,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable)
#' sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts <- merge(counts, data.frame(cohortDefinitionId = cohortsToCreate$cohortId,
#' cohortName = cohortsToCreate$name))
#' write.csv(counts, file.path(outputFolder, "CohortCounts.csv"))
#'
#' if(runSimpleMonthly){
#'
#' message("Calculating phenotype incidence over time")
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "MonthlyCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (2, 4, 6, 8, 10)')
#'
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#' write.csv(counts, file.path(outputFolder, "MonthlyCohortCounts.csv"))
#'
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "MonthlyDenominators.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cdm_database_schema = cdmDatabaseSchema)
#'
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#' write.csv(counts, file.path(outputFolder, "MonthlyDenominators.csv"))
#'
#' }
#'
#' if(runOverlap){
#'
#' message("Calculating phenotype overlap")
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "OverlapCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (1, 3)')
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#'
#' pathToCsv <- system.file("settings", "CohortsToCreate.csv", package = "covidPhenotypesTest")
#' cohortsToCreate <- readr::read_csv(pathToCsv, col_types = readr::cols())
#'
#' counts <- counts %>%
#' mutate(combinationName = map_chr(comboId, ~extract_bitsum(.x, str_remove(cohortsToCreate$name, '_ema_phenos__'))))
#'
#' # browser()
#' final <- counts
#'
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "OverlapCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (1, 5)')
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#'
#' counts <- counts %>%
#' mutate(combinationName = map_chr(comboId, ~extract_bitsum(.x, str_remove(cohortsToCreate$name, '_ema_phenos__'))))
#'
#' final <- rbind(final, counts)
#'
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "OverlapCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (1, 7)')
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#'
#' counts <- counts %>%
#' mutate(combinationName = map_chr(comboId, ~extract_bitsum(.x, str_remove(cohortsToCreate$name, '_ema_phenos__'))))
#'
#' final <- rbind(final, counts)
#'
#' sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "OverlapCounts.sql",
#' packageName = "covidPhenotypesTest",
#' dbms = attr(connection, "dbms"),
#' oracleTempSchema = oracleTempSchema,
#' cohort_database_schema = cohortDatabaseSchema,
#' cohort_table = cohortTable,
#' limit_criteria = 'cohort_definition_id IN (1, 9)')
#' counts <- DatabaseConnector::querySql(connection, sql)
#' names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
#' counts$countPersons[counts$countPersons < minCellCount] <- -minCellCount
#'
#' counts <- counts %>%
#' mutate(combinationName = map_chr(comboId, ~extract_bitsum(.x, str_remove(cohortsToCreate$name, '_ema_phenos__'))))
#'
#' final <- rbind(final, counts)
#'
#' write.csv(final, file.path(outputFolder, "CohortOverlap.csv"))
#'
#' }
#'
#' if(runPhevaluator){
#'
#' prevalence <- estimate_prevalence_2020(connection, cohortDatabaseSchema, cohortTable)
#'
#' prevalence <- round(max(c(prevalence,0.05),na.rm=T),3)
#'
#' run_phevaluator_covid(connection = connection, prevalence, cdmDatabaseSchema = cdmDatabaseSchema, cohortDatabaseSchema = cohortDatabaseSchema, cohortTable = cohortTable, outputFolder = outputFolder)
#'
#' }
#'
#' DatabaseConnector::disconnect(connection)
#'
#' }
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