Combining Cohorts

library(CohortConstructor)
library(CohortCharacteristics)
library(ggplot2)

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

con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
cdm <- cdm_from_con(con, cdm_schema = "main", 
                    write_schema = c(prefix = "my_study_", schema = "main"))

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

To check whether there is an overlap between records in both cohorts using the function intersectCohorts().

cdm$medintersect <- CohortConstructor::intersectCohorts(
  cohort = cdm$medications,
  name = "medintersect"
)

cohortCount(cdm$medintersect)
#> # A tibble: 1 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <int>           <int>
#> 1                    1              6               6

There are 6 individuals who had overlapping records in the diclofenac and acetaminophen cohorts.

We can choose the number of days between cohort entries using the gap argument.

cdm$medintersect <- CohortConstructor::intersectCohorts(
  cohort = cdm$medications,
  gap = 365,
  name = "medintersect"
)

cohortCount(cdm$medintersect)
#> # A tibble: 1 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <int>           <int>
#> 1                    1             94              94

There are 94 individuals who had overlapping records (within 365 days) in the diclofenac and acetaminophen cohorts.

cdm$medunion <- CohortConstructor::unionCohorts(
  cohort = cdm$medications,
  name = "medunion"
)

cohortCount(cdm$medunion)
#> # A tibble: 1 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <int>           <int>
#> 1                    1          10189            2605

We have now created a new cohort which includes individuals in either the diclofenac cohort or the acetaminophen cohort.

You can keep the original cohorts in the new table if you use the argument keepOriginalCohorts = TRUE.

cdm$medunion <- CohortConstructor::unionCohorts(
  cohort = cdm$medications,
  name = "medunion",
  keepOriginalCohorts = TRUE
)

cohortCount(cdm$medunion)
#> # A tibble: 3 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <int>           <int>
#> 1                    1           9365            2580
#> 2                    2            830             830
#> 3                    3          10189            2605

You can also choose the number of days between two subsequent cohort entries to be merged using the gap argument.

cdm$medunion <- CohortConstructor::unionCohorts(
  cohort = cdm$medications,
  name = "medunion",
  gap = 365,
  keepOriginalCohorts = TRUE
)

cohortCount(cdm$medunion)
#> # A tibble: 3 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <int>           <int>
#> 1                    1           9365            2580
#> 2                    2            830             830
#> 3                    3           9682            2605