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## ----echo=FALSE,message=FALSE,warning=FALSE,eval=TRUE-------------------------
library(FeatureExtraction)
## ----eval=FALSE---------------------------------------------------------------
# createLooCovariateSettings <- function(useLengthOfObs = TRUE) {
# covariateSettings <- list(useLengthOfObs = useLengthOfObs)
# attr(covariateSettings, "fun") <- "getDbLooCovariateData"
# class(covariateSettings) <- "covariateSettings"
# return(covariateSettings)
# }
## ----eval=FALSE---------------------------------------------------------------
# getDbLooCovariateData <- function(connection,
# oracleTempSchema = NULL,
# cdmDatabaseSchema,
# cdmVersion = "5",
# cohortTable = "#cohort_person",
# cohortIds = c(-1),
# rowIdField = "subject_id",
# covariateSettings,
# aggregated = FALSE) {
# writeLines("Constructing length of observation covariates")
# if (covariateSettings$useLengthOfObs == FALSE) {
# return(NULL)
# }
# if (aggregated) {
# stop("Aggregation not supported")
# }
#
# # Some SQL to construct the covariate:
# sql <- paste(
# "SELECT @row_id_field AS row_id, 1 AS covariate_id,",
# "DATEDIFF(DAY, observation_period_start_date, cohort_start_date)",
# "AS covariate_value",
# "FROM @cohort_table c",
# "INNER JOIN @cdm_database_schema.observation_period op",
# "ON op.person_id = c.subject_id",
# "WHERE cohort_start_date >= observation_period_start_date",
# "AND cohort_start_date <= observation_period_end_date",
# "{@cohort_ids != -1} ? {AND cohort_definition_id IN @cohort_ids}"
# )
# sql <- SqlRender::render(sql,
# cohort_table = cohortTable,
# cohort_ids = cohortIds,
# row_id_field = rowIdField,
# cdm_database_schema = cdmDatabaseSchema
# )
# sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
#
# # Retrieve the covariate:
# covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
#
# # Construct covariate reference:
# covariateRef <- data.frame(
# covariateId = 1,
# covariateName = "Length of observation",
# analysisId = 1,
# conceptId = 0
# )
#
# # Construct analysis reference:
# analysisRef <- data.frame(
# analysisId = 1,
# analysisName = "Length of observation",
# domainId = "Demographics",
# startDay = 0,
# endDay = 0,
# isBinary = "N",
# missingMeansZero = "Y"
# )
#
# # Construct analysis reference:
# metaData <- list(sql = sql, call = match.call())
# result <- Andromeda::andromeda(
# covariates = covariates,
# covariateRef = covariateRef,
# analysisRef = analysisRef
# )
# attr(result, "metaData") <- metaData
# class(result) <- "CovariateData"
# return(result)
# }
## ----eval=FALSE---------------------------------------------------------------
# looCovSet <- createLooCovariateSettings(useLengthOfObs = TRUE)
#
# covariates <- getDbCovariateData(
# connectionDetails = connectionDetails,
# cdmDatabaseSchema = cdmDatabaseSchema,
# cohortDatabaseSchema = resultsDatabaseSchema,
# cohortTable = "rehospitalization",
# cohortIds = c(1),
# covariateSettings = looCovSet
# )
## ----eval=FALSE---------------------------------------------------------------
# covariateSettings <- createCovariateSettings(
# useDemographicsGender = TRUE,
# useDemographicsAgeGroup = TRUE,
# useDemographicsRace = TRUE,
# useDemographicsEthnicity = TRUE,
# useDemographicsIndexYear = TRUE,
# useDemographicsIndexMonth = TRUE
# )
#
# looCovSet <- createLooCovariateSettings(useLengthOfObs = TRUE)
#
# covariateSettingsList <- list(covariateSettings, looCovSet)
#
# covariates <- getDbCovariateData(
# connectionDetails = connectionDetails,
# cdmDatabaseSchema = cdmDatabaseSchema,
# cohortDatabaseSchema = resultsDatabaseSchema,
# cohortTable = "rehospitalization",
# cohortIds = c(1),
# covariateSettings = covariateSettingsList
# )
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