Generate a matched age and sex cohort

Introduction

CohortConstructor packages includes a function to obtain an age and sex matched cohort, the generateMatchedCohortSet() function. In this vignette, we will explore the usage of this function.

Create mock data

We will first use mockDrugUtilisation() function from DrugUtilisation package to create mock data.

library(CohortConstructor)
library(dplyr)

cdm <- mockCohortConstructor(nPerson = 1000)

As we will use cohort1 to explore generateMatchedCohortSet(), let us first use cohort_attrition() from CDMConnector package to explore this cohort:

CDMConnector::cohort_set(cdm$cohort1)

Use generateMatchedCohortSet() to create an age-sex matched cohort

Let us first see an example of how this function works. For its usage, we need to provide a cdm object, the targetCohortName, which is the name of the table containing the cohort of interest, and the name of the new generated tibble containing the cohort and the matched cohort. We will also use the argument targetCohortId to specify that we only want a matched cohort for cohort_definition_id = 1.

cdm$matched_cohort1 <- matchCohorts(
  cohort = cdm$cohort1,
  cohortId = 1,
  name = "matched_cohort1")

CDMConnector::cohort_set(cdm$matched_cohort1)

Notice that in the generated tibble, there are two cohorts: cohort_definition_id = 1 (original cohort), and cohort_definition_id = 4 (matched cohort). target_cohort_name column indicates which is the original cohort. match_sex and match_year_of_birth adopt boolean values (TRUE/FALSE) indicating if we have matched for sex and age, or not. match_status indicate if it is the original cohort (target) or if it is the matched cohort (matched). target_cohort_id indicates which is the cohort_id of the original cohort.

Check the exclusion criteria applied to generate the new cohorts by using cohort_attrition() from CDMConnector package:

# Original cohort
CDMConnector::cohort_attrition(cdm$matched_cohort1) %>% filter(cohort_definition_id == 1)

# Matched cohort
CDMConnector::cohort_attrition(cdm$matched_cohort1) %>% filter(cohort_definition_id == 4)

Briefly, from the original cohort, we exclude first those individuals that do not have a match, and then individuals that their matching pair is not in observation during the assigned cohort_start_date. From the matched cohort, we start from the whole database and we first exclude individuals that are in the original cohort. Afterwards, we exclude individuals that do not have a match, then individuals that are not in observation during the assigned cohort_start_date, and finally we remove as many individuals as required to fulfill the ratio.

Notice that matching pairs are randomly assigned, so it is probable that every time you execute this function, the generated cohorts change. Use set.seed() to avoid this.

matchSex parameter

matchSex is a boolean parameter (TRUE/FALSE) indicating if we want to match by sex (TRUE) or we do not want to (FALSE).

matchYear parameter

matchYear is another boolean parameter (TRUE/FALSE) indicating if we want to match by age (TRUE) or we do not want (FALSE).

Notice that if matchSex = FALSE and matchYear = FALSE, we will obtain an unmatched comparator cohort.

ratio parameter

The default matching ratio is 1:1 (ratio = 1). Use cohort_counts() from CDMConnector to check if the matching has been done as desired.

CDMConnector::cohort_count(cdm$matched_cohort1)

You can modify the ratio parameter to tailor your matched cohort. ratio can adopt values from 1 to Inf.

cdm$matched_cohort2 <- matchCohorts(
  cohort = cdm$cohort1,
  cohortId = 1,
  name = "matched_cohort2",
  ratio = Inf)

CDMConnector::cohort_count(cdm$matched_cohort2)

Generate matched cohorts simultaneously across multiple cohorts

All these functionalities can be implemented across multiple cohorts simultaneously. Specify in targetCohortId parameter which are the cohorts of interest. If set to NULL, all the cohorts present in targetCohortName will be matched.

cdm$matched_cohort3 <- matchCohorts(
  cohort = cdm$cohort1,
  cohortId = c(1,3),
  name = "matched_cohort3",
  ratio = 2)

CDMConnector::cohort_set(cdm$matched_cohort3) %>% arrange(cohort_definition_id)

CDMConnector::cohort_count(cdm$matched_cohort3) %>% arrange(cohort_definition_id)

Notice that each cohort has their own (and independent of other cohorts) matched cohort.