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 covariates to report.
excludedCodes: Codes excluded.

Value

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

Examples

# \donttest{
library(CohortCharacteristics)
cdm <- CohortCharacteristics::mockCohortCharacteristics()
#> ! cohort columns will be reordered to match the expected order:
#>   cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date.
#> ! cohort columns will be reordered to match the expected order:
#>   cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date.

concept <- dplyr::tibble(
  concept_id = c(1125315, 1503328, 1516978, 317009, 378253, 4266367),
  domain_id = NA_character_,
  vocabulary_id = NA_character_,
  concept_class_id = NA_character_,
  concept_code = NA_character_,
  valid_start_date = as.Date("1900-01-01"),
  valid_end_date = as.Date("2099-01-01")
) |>
  dplyr::mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)
results <- cdm$cohort2 |>
  summariseLargeScaleCharacteristics(
    episodeInWindow = c("condition_occurrence"),
    minimumFrequency = 0
  )
#> ℹ Summarising large scale characteristics 
#> 
#>  - getting characteristics from table condition_occurrence (1 of 1)
mockDisconnect(cdm = cdm)
# }