The functions `estimatePointPrevalence()`

,
`estimatePeriodPrevalence()`

, and
`estimateIncidence()`

will calculate incidence and prevalence
rates of a set of outcome cohorts for a set of denominator cohorts.
While the denominator cohorts can be created using the
`generateDenominatorCohortSet()`

from this package, the set
of outcome cohorts used will be defined externally. Below we provide
some general instructions for defining outcome cohorts and adding them
to a cdm reference.

OMOP CDM cohort definitions can be represented as JSON. These JSON definition could be created using tools such as ATLAS (https://github.com/OHDSI/Atlas) or capR (https://github.com/OHDSI/Capr). How we define outcome cohorts will depend on our research question, and we will need to consider several aspects such as event persistence and restrictions in the number of events occurring per person.

In general outcome definitions should include all occurrences of an
outcome in an individual´s history without restriction, as requirements
such as prior history can be incorporated when using
`generateDenominatorCohortSet()`

as can other criteria such
as diagnoses of conditions or medication use (using the strata cohort
option). In addition, it should be kept in mind that the approach to
defining cohort exit can impact results for both incidence and
prevalence rates. For incidence rates, we can decide whether we want to
restrict our analysis to first events or if we want to include all the
events that occur per person. If we want to consider multiple events per
person, the duration of these events will be of importance, as we are
not going to capture subsequent events if prior events have not yet been
concluded. In addition, outcome washouts will be implemented relative to
cohort exit from any previous event. Event persistence will also
influence results generated by `estimatePointPrevalence()`

and `estimatePeriodPrevalence()`

as the number of individuals
included in the numerator for a given moment or period of time will
differ depending on how the outcome cohort exit strategy was
defined.

Once we have a set of cohort definitions expressed as JSONs (saved in a folder called “outcome_cohorts”), we can add the cohorts to an existing cdm reference in a table called “outcome_table” like so:

```
<- CDMConnector::readCohortSet(here::here("outcome_cohorts"))
outcome_cohorts <- CDMConnector::generateCohortSet(
cdm cdm = cdm,
cohortSet = outcome_cohorts,
name = outcome_table
)
```

As long as the table created is in the format of an OMOP CDM cohort table, an outcome cohort could also be defined via bespoke code. It is beyond the scope of this vignette to describe all the ways a custom code could be written to define an outcome, but suffice it to say that as with defining a JSON cohort expression the outcome cohort created should reflect the study question at hand. Similar considerations as above would apply on defining outcome entry events, appropriate cohort exit criteria, and so on.