This function is used to summarise the large scale characteristics of a cohort table

Usage

summariseLargeScaleCharacteristics(
  cohort,
  strata = list(),
  window = list(c(-Inf, -366), c(-365, -31), c(-30, -1), c(0, 0), c(1, 30), c(31, 365),
    c(366, Inf)),
  eventInWindow = NULL,
  episodeInWindow = NULL,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  includeSource = FALSE,
  minimumFrequency = 0.005,
  excludedCodes = c(0)
)

Arguments

cohort: The cohort to characterise.
strata: Stratification list.
window: Temporal windows that we want to characterize.
eventInWindow: Tables to characterise the events in the window. eventInWindow must be provided if episodeInWindow is not specified.
episodeInWindow: Tables to characterise the episodes in the window. episodeInWindow must be provided if eventInWindow is not specified.
indexDate: Variable in x that contains the date to compute the intersection.
censorDate: whether to censor overlap events at a specific date or a column date of x
includeSource: Whether to include source concepts.
minimumFrequency: Minimum frequency of codes to be reported. If a concept_id has a frequency smaller than minimumFrequency in a certain window that estimate will be eliminated from the result object.
excludedCodes: Codes excluded.

Value

The output of this function is a ResultSummary containing the relevant information.

Examples

if (FALSE) { # \dontrun{
library(CohortCharacteristics)
library(duckdb)
library(CDMConnector)
library(DrugUtilisation)
library(dplyr, warn.conflicts = FALSE)

con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con, cdmSchem = "main", writeSchema = "main")

cdm <- generateIngredientCohortSet(
  cdm = cdm, name = "my_cohort", ingredient = "acetaminophen"
)

cdm$my_cohort |>
  summariseLargeScaleCharacteristics(
    window = list(c(-365, -1), c(1, 365)),
    eventInWindow = "condition_occurrence"
  ) |>
  glimpse()

cdmDisconnect(cdm)
} # }