Constructs covariates using the cohort_attribute table.

  oracleTempSchema = NULL,
  cohortTable = "#cohort_person",
  cohortId = -1,
  cohortIds = c(-1),
  cdmVersion = "5",
  rowIdField = "subject_id",
  aggregated = FALSE



A connection to the server containing the schema as created using the connect function in the DatabaseConnector package.


A schema where temp tables can be created in Oracle.


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'.


Name of the table holding the cohort for which we want to construct covariates. If it is a temp table, the name should have a hash prefix, e.g. '#temp_table'. If it is a non-temp table, it should include the database schema, e.g. 'cdm_database.cohort'.


DEPRECATED:For which cohort ID should covariates be constructed? If set to -1, covariates will be constructed for all cohorts in the specified cohort table.


For which cohort ID(s) should covariates be constructed? If set to c(-1), covariates will be constructed for all cohorts in the specified cohort table.


The version of the Common Data Model used. Currently only cdmVersion = "5" is supported.


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.


An object of type covariateSettings as created using the createCohortAttrCovariateSettings function.


Should aggregate statistics be computed instead of covariates per cohort entry?


Returns an object of type CovariateData, which is an Andromeda object 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:


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.


A table describing the covariates that have been extracted.

. The CovariateData object will also have a metaData attribute, a list of objects with information on how the covariateData object was constructed.


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. Typically, users don't call this function directly but rather use the getDbCovariateData function instead.


# \donttest{
connectionDetails <- Eunomia::getEunomiaConnectionDetails()
#> attempting to download GiBleed
#> attempting to extract and load: /Users/ginberg/Data/eunomia/ to: /Users/ginberg/Data/eunomia/GiBleed_5.3.sqlite
  connectionDetails = connectionDetails,
  cdmDatabaseSchema = "main",
  cohortDatabaseSchema = "main",
  cohortTable = "cohort"
#> Cohorts created in table main.cohort
#>   cohortId       name
#> 1        1  Celecoxib
#> 2        2 Diclofenac
#> 3        3    GiBleed
#> 4        4     NSAIDs
#>                                                                                        description
#> 1    A simplified cohort definition for new users of celecoxib, designed specifically for Eunomia.
#> 2    A simplified cohort definition for new users ofdiclofenac, designed specifically for Eunomia.
#> 3 A simplified cohort definition for gastrointestinal bleeding, designed specifically for Eunomia.
#> 4       A simplified cohort definition for new users of NSAIDs, designed specifically for Eunomia.
#>   count
#> 1  1844
#> 2   850
#> 3   479
#> 4  2694
connection <- DatabaseConnector::connect(connectionDetails)
#> Connecting using SQLite driver
covariateSettings <- createCohortAttrCovariateSettings(
  attrDatabaseSchema = "main",
  cohortAttrTable = "cohort_attribute",
  attrDefinitionTable = "attribute_definition",
  includeAttrIds = c(1),
  isBinary = FALSE,
  missingMeansZero = FALSE

covData <- getDbCohortAttrCovariatesData(
  connection = connection,
  oracleTempSchema = NULL,
  cdmDatabaseSchema = "main",
  cdmVersion = "5",
  cohortTable = "cohort",
  cohortIds = 1,
  rowIdField = "subject_id",
  covariateSettings = covariateSettings,
  aggregated = FALSE
#> Constructing covariates from cohort attributes table
#> Inserting data took 0.00329 secs
#> Loading took 0.025 secs
# }