# Estimate period prevalence

`estimatePeriodPrevalence.Rd`

Estimate period prevalence

## Usage

```
estimatePeriodPrevalence(
cdm,
denominatorTable,
outcomeTable,
denominatorCohortId = NULL,
outcomeCohortId = NULL,
interval = "years",
completeDatabaseIntervals = TRUE,
fullContribution = FALSE,
strata = list(),
includeOverallStrata = TRUE,
minCellCount = 5,
returnParticipants = FALSE
)
```

## Arguments

- cdm
A CDM reference object

- denominatorTable
A cohort table with a set of denominator cohorts (for example, created using the

`generateDenominatorCohortSet()`

function).- outcomeTable
A cohort table in the cdm reference containing a set of outcome cohorts.

- denominatorCohortId
The cohort definition ids of the denominator cohorts of interest. If NULL all cohorts will be considered in the analysis.

- outcomeCohortId
The cohort definition ids of the outcome cohorts of interest. If NULL all cohorts will be considered in the analysis.

- interval
Time intervals over which period prevalence is estimated. This can be "weeks", "months", "quarters", "years", or "overall". ISO weeks will be used for weeks. Calendar months, quarters, or years can be used as the period. If more than one option is chosen then results will be estimated for each chosen interval.

- completeDatabaseIntervals
TRUE/ FALSE. Where TRUE, prevalence will only be estimated for those intervals where the database captures all the interval (based on the earliest and latest observation period start dates, respectively).

- fullContribution
TRUE/ FALSE. Where TRUE, individuals will only be included if they in the database for the entire interval of interest. If FALSE they are only required to present for one day of the interval in order to contribute.

- strata
Variables added to the denominator cohort table for which to stratify estimates.

- includeOverallStrata
Whether to include an overall result as well as strata specific results (when strata has been specified).

- minCellCount
Minimum number of events to report- results lower than this will be obscured. If NULL all results will be reported.

- returnParticipants
Either TRUE or FALSE. If TRUE references to participants from the analysis will be returned allowing for further analysis. Note, if using permanent tables and returnParticipants is TRUE, one table per analysis will be kept in the cdm write schema.

## Examples

```
# \donttest{
cdm <- mockIncidencePrevalenceRef(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
cdm = cdm, name = "denominator",
cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
)
#> ℹ Creating denominator cohorts
#> ✔ Cohorts created in 0 min and 3 sec
estimatePeriodPrevalence(
cdm = cdm,
denominatorTable = "denominator",
outcomeTable = "outcome",
interval = "months"
)
#> Getting prevalence for analysis 1 of 1
#> Time taken: 0 mins and 1 secs
#> # A tibble: 120 × 29
#> analysis_id prevalence_start_date prevalence_end_date n_cases n_population
#> <chr> <date> <date> <int> <int>
#> 1 1 2008-01-01 2008-01-31 NA 691
#> 2 1 2008-02-01 2008-02-29 NA 691
#> 3 1 2008-03-01 2008-03-31 NA 690
#> 4 1 2008-04-01 2008-04-30 5 688
#> 5 1 2008-05-01 2008-05-31 NA 685
#> 6 1 2008-06-01 2008-06-30 NA 685
#> 7 1 2008-07-01 2008-07-31 0 684
#> 8 1 2008-08-01 2008-08-31 0 681
#> 9 1 2008-09-01 2008-09-30 NA 681
#> 10 1 2008-10-01 2008-10-31 0 681
#> # ℹ 110 more rows
#> # ℹ 24 more variables: prevalence <dbl>, prevalence_95CI_lower <dbl>,
#> # prevalence_95CI_upper <dbl>, population_obscured <chr>,
#> # cases_obscured <chr>, result_obscured <chr>, outcome_cohort_id <int>,
#> # outcome_cohort_name <chr>, analysis_type <chr>, analysis_interval <chr>,
#> # analysis_complete_database_intervals <lgl>, analysis_time_point <chr>,
#> # analysis_full_contribution <lgl>, analysis_min_cell_count <dbl>, …
# }
```