For this example we’ll use the Eunomia synthetic data from the CDMConnector package.
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())
cdm <- cdmFromCon(con, cdmSchema = "main", 
                    writeSchema = 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)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$medintersect)There are 6 individuals who had overlapping records in the diclofenac and acetaminophen cohorts.
We can choose the number of days between cohort entries using the
gap argument.
cdm$medintersect <- CohortConstructor::intersectCohorts(
  cohort = cdm$medications,
  gap = 365,
  name = "medintersect"
)
cohortCount(cdm$medintersect)There are 94 individuals who had overlapping records (within 365 days) in the diclofenac and acetaminophen cohorts.
We can also combine different cohorts using the function
unionCohorts().
cdm$medunion <- CohortConstructor::unionCohorts(
  cohort = cdm$medications,
  name = "medunion"
)
cohortCount(cdm$medunion)We have now created a new cohort which includes individuals in either the diclofenac cohort or the acetaminophen cohort.
You can keep the original cohorts in the new table if you use the
argument keepOriginalCohorts = TRUE.
cdm$medunion <- CohortConstructor::unionCohorts(
  cohort = cdm$medications,
  name = "medunion",
  keepOriginalCohorts = TRUE
)
cohortCount(cdm$medunion)You can also choose the number of days between two subsequent cohort
entries to be merged using the gap argument.