Introduction
The CohortConstructor package is designed to support cohort building
pipelines. When using the package the general workflow is to first build
a set of base cohorts and then subsequently apply inclusion criteria to
derive the final study cohorts of interest. Base cohorts are built by
domain (rather than by cohort definition) and from one base cohort many
study cohorts can be derived.
Building a cohort set by domain
Let´s say we want to build 5 cohorts with 3 (asthma, copd, and
diabetes) defined based on concepts seen in the condition occurrence
table while the other 2 (acetaminophen and warfarin) are based on
concepts recorded in the drug exposure table. We can build these cohorts
independently, one after the other. However, this approach will mean
repeating 3 joins to the condition occurrence tables and 2 joins to the
drug exposure table (with the concepts in the concept sets). To make
this less computationally expensive, we could instead create the cohorts
by domain. In this case we will instead make one join with the condition
occurrence table and one to the drug exposure (using all the concept
sets together).
Deriving study cohorts from base cohorts
When making study cohorts we often have a concept sets to define a
clinical event along with various study-specific inclusion criteria, for
example criteria around an amount of prior observation and age. Often we
may have sensitivity analysis where the concept set remains the same but
these inclusion criteria change. In such situations we can make cohorts
one-by-one. However, this can lead to duplication as we can see in the
example below we identify asthma records multiple times. An alternative
approach is to build a base cohort, in this case based on asthma
records, after which we derive multiple cohorts from this where the
different inclusion criteria are each applied.
Considerations when building cohorts
CohortConstructor provides a means of building cohorts via a
pipeline, with cohorts created through the application of a sequence of
functions. It is important to note that the order of the sequence will
often have important implications. In the example below we have just one
individual who has three recorded diagnoses of asthma. One diagnosis was
in 2008 and two in 2009, with only the last coming after the
individual´s 18th birthday. Below three cohort pipelines are shown with
restrictions around calendar dates, age, and that the record was the
first. Only in cohort pipeline A, however, would the individual be
included in the final cohort, with their third diagnosis used for cohort
start. In pipeline B and C the individual would have been excluded.
