# Splitting cohorts

For this example we’ll use the Eunomia synthetic data from the CDMConnector package.

    con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())
    cdm <- CDMConnector::cdmFromCon(con, cdmSchema = "main",
                        writeSchema = "main", writePrefix = "my_study_")

Let’s start by creating two drug cohorts, one for users of diclofenac and another for users of acetaminophen.

    cdm$medications <- conceptCohort(cdm = cdm,
                                     conceptSet = list("diclofenac" = 1124300,
                                                       "acetaminophen" = 1127433),
                                     name = "medications")
    cohortCount(cdm$medications)
    #> # A tibble: 2 × 3
    #>   cohort_definition_id number_records number_subjects
    #>                  <int>          <int>           <int>
    #> 1                    1           9365            2580
    #> 2                    2            830             830
    settings(cdm$medications)
    #> # A tibble: 2 × 4
    #>   cohort_definition_id cohort_name   cdm_version vocabulary_version
    #>                  <int> <chr>         <chr>       <chr>
    #> 1                    1 acetaminophen 5.3         v5.0 18-JAN-19
    #> 2                    2 diclofenac    5.3         v5.0 18-JAN-19

We can stratify cohorts based on specified columns using the function `stratifyCohorts()`. In this example, let’s stratify the medications cohort by age and sex.

    cdm$stratified <- cdm$medications |>
      addAge(ageGroup = list("Child" = c(0,17), "18 to 65" = c(18,64), "65 and Over" = c(65, Inf))) |>
      addSex(name = "stratified") |>
      stratifyCohorts(strata = list("sex", "age_group"), name = "stratified")

    cohortCount(cdm$stratified)
    #> # A tibble: 10 × 3
    #>    cohort_definition_id number_records number_subjects
    #>                   <int>          <int>           <int>
    #>  1                    1           4718            2264
    #>  2                    2           4647            2215
    #>  3                    3            435             435
    #>  4                    4            395             395
    #>  5                    5           5857            2331
    #>  6                    6            716             397
    #>  7                    7           2792            1751
    #>  8                    8            830             830
    #>  9                    9              0               0
    #> 10                   10              0               0
    settings(cdm$stratified)
    #> # A tibble: 10 × 10
    #>    cohort_definition_id cohort_name          target_cohort_id target_cohort_name
    #>                   <int> <chr>                           <int> <chr>
    #>  1                    1 acetaminophen_female                1 acetaminophen
    #>  2                    2 acetaminophen_male                  1 acetaminophen
    #>  3                    3 diclofenac_female                   2 diclofenac
    #>  4                    4 diclofenac_male                     2 diclofenac
    #>  5                    5 acetaminophen_18_to…                1 acetaminophen
    #>  6                    6 acetaminophen_65_an…                1 acetaminophen
    #>  7                    7 acetaminophen_child                 1 acetaminophen
    #>  8                    8 diclofenac_18_to_65                 2 diclofenac
    #>  9                    9 diclofenac_65_and_o…                2 diclofenac
    #> 10                   10 diclofenac_child                    2 diclofenac
    #> # ℹ 6 more variables: cdm_version <chr>, vocabulary_version <chr>,
    #> #   target_cohort_table_name <chr>, strata_columns <chr>, sex <chr>,
    #> #   age_group <chr>