man-roxygen/GetCovarParams.R

In hxia/plp-git-demo: Package for patient level prediction using data in the OMOP Common Data Model

#' @details
#' This function uses the data in the CDM to construct a large set of covariates for the provided
#' cohort. The cohort is assumed to be in an existing temp table with these fields: 'subject_id',
#' 'cohort_definition_id', 'cohort_start_date'. Optionally, an extra field can be added containing the
#' unique identifier that will be used as rowID in the output. This function is called automatically
#' by the \code{\link{getDbPlpData}} function.
#'
#' @param connection          A connection to the server containing the schema as created using the
#'                            \code{connect} function in the \code{DatabaseConnector} package.
#' @param oracleTempSchema    A schema where temp tables can be created in Oracle.
#' @param cdmDatabaseSchema   The name of the database schema that contains the OMOP CDM instance.
#'                            Requires read permissions to this database. On SQL Server, this should
#'                            specifiy both the database and the schema, so for example
#'                            'cdm_instance.dbo'.
#' @param cdmVersion          Define the OMOP CDM version used: currently support "4" and "5".
#' @param cohortTempTable     Name of the temp table holding the cohort for which we want to construct
#'                            covaraites
#' @param rowIdField          The name of the field in the cohort temp table that is to be used as the
#'                            row_id field in the output table. This can be especially usefull if there
#'                            is more than one period per person.
#'
#' @return
#' Returns an object of type \code{covariateData}, containing information on the baseline covariates.
#' Information about multiple outcomes can be captured at once for efficiency reasons. This object is
#' a list with the following components: \describe{ \item{covariates}{An ffdf object listing the
#' baseline covariates per person in the cohorts. This is done using a sparse representation:
#' covariates with a value of 0 are omitted to save space. The covariates object will have three
#' columns: rowId, covariateId, and covariateValue. The rowId is usually equal to the person_id,
#' unless specified otherwise in the rowIdField argument.} \item{covariateRef}{An ffdf object
#' describing the covariates that have been extracted.} \item{metaData}{A list of objects with
#' information on how the covariateData object was constructed.} }