Summarise characteristics of cohorts in a cohort table

Usage

summariseCharacteristics(
  cohort,
  cohortId = NULL,
  strata = list(),
  counts = TRUE,
  demographics = TRUE,
  ageGroup = NULL,
  tableIntersectFlag = list(),
  tableIntersectCount = list(),
  tableIntersectDate = list(),
  tableIntersectDays = list(),
  cohortIntersectFlag = list(),
  cohortIntersectCount = list(),
  cohortIntersectDate = list(),
  cohortIntersectDays = list(),
  conceptIntersectFlag = list(),
  conceptIntersectCount = list(),
  conceptIntersectDate = list(),
  conceptIntersectDays = list(),
  otherVariables = character(),
  estimates = list(),
  weights = NULL,
  otherVariablesEstimates = lifecycle::deprecated()
)

Arguments

cohort: A cohort_table object.
cohortId: A cohort definition id to restrict by. If NULL, all cohorts will be included.
strata: A list of variables to stratify results. These variables must have been added as additional columns in the cohort table.
counts: TRUE or FALSE. If TRUE, record and person counts will be produced.
demographics: TRUE or FALSE. If TRUE, patient demographics (cohort start date, cohort end date, age, sex, prior observation, and future observation will be summarised).
ageGroup: A list of age groups to stratify results by.
tableIntersectFlag: A list of arguments that uses PatientProfiles::addTableIntersectFlag() to add variables to summarise.
tableIntersectCount: A list of arguments that uses PatientProfiles::addTableIntersectCount() to add variables to summarise.
tableIntersectDate: A list of arguments that uses PatientProfiles::addTableIntersectDate() to add variables to summarise.
tableIntersectDays: A list of arguments that uses PatientProfiles::addTableIntersectDays() to add variables to summarise.
cohortIntersectFlag: A list of arguments that uses PatientProfiles::addCohortIntersectFlag() to add variables to summarise.
cohortIntersectCount: A list of arguments that uses PatientProfiles::addCohortIntersectCount() to add variables to summarise.
cohortIntersectDate: A list of arguments that uses PatientProfiles::addCohortIntersectDate() to add variables to summarise.
cohortIntersectDays: A list of arguments that uses PatientProfiles::addCohortIntersectDays() to add variables to summarise.
conceptIntersectFlag: A list of arguments that uses PatientProfiles::addConceptIntersectFlag() to add variables to summarise.
conceptIntersectCount: A list of arguments that uses PatientProfiles::addConceptIntersectCount() to add variables to summarise.
conceptIntersectDate: A list of arguments that uses PatientProfiles::addConceptIntersectDate() to add variables to summarise.
conceptIntersectDays: A list of arguments that uses PatientProfiles::addConceptIntersectDays() to add variables to summarise.
otherVariables: Other variables contained in cohort that you want to be summarised.
estimates: To modify the default estimates for a variable. By default: 'min', 'q25', 'median', 'q75', 'max' for "date", "numeric" and "integer" variables ("numeric" and "integer" also use 'mean' and 'sd' estimates). 'count' and 'percentage' for "categorical" and "binary". You have to provide them as a list: list(age = c("median", "density")). You can also use 'date', 'numeric', 'integer', 'binary', 'categorical', 'demographics', 'intersect', 'other', 'table_intersect_count', ...
weights: Column in cohort that points to weights of each individual.
otherVariablesEstimates: deprecated.

Value

A summary of the characteristics of the cohorts in the cohort table.

Examples

# \donttest{
library(dplyr, warn.conflicts = FALSE)
library(CohortCharacteristics)
library(PatientProfiles)

cdm <- mockCohortCharacteristics()

cdm$cohort1 |>
  addSex() |>
  addAge(
    ageGroup = list(c(0, 40), c(41, 150))
  ) |>
  summariseCharacteristics(
    strata = list("sex", "age_group"),
    cohortIntersectFlag = list(
      "Cohort 2 Flag" = list(
        targetCohortTable = "cohort2", window = c(-365, 0)
      )
    ),
    cohortIntersectCount = list(
      "Cohort 2 Count" = list(
        targetCohortTable = "cohort2", window = c(-365, 0)
      )
    )
  ) |>
  glimpse()
#> ℹ adding demographics columns
#> ℹ adding cohortIntersectFlag 1/1
#> window names casted to snake_case:
#> • `-365 to 0` -> `365_to_0`
#> ℹ adding cohortIntersectCount 1/1
#> window names casted to snake_case:
#> • `-365 to 0` -> `365_to_0`
#> ℹ summarising data
#> ℹ summarising cohort cohort_1
#> ℹ summarising cohort cohort_2
#> ℹ summarising cohort cohort_3
#> ✔ summariseCharacteristics finished!
#> Rows: 836
#> Columns: 13
#> $ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name         <chr> "PP_MOCK", "PP_MOCK", "PP_MOCK", "PP_MOCK", "PP_MOCK"…
#> $ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level      <chr> "cohort_1", "cohort_1", "cohort_1", "cohort_1", "coho…
#> $ strata_name      <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level     <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name    <chr> "Number records", "Number subjects", "Cohort start da…
#> $ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ estimate_name    <chr> "count", "count", "min", "q25", "median", "q75", "max…
#> $ estimate_type    <chr> "integer", "integer", "date", "date", "date", "date",…
#> $ estimate_value   <chr> "5", "5", "1930-02-09", "1966-02-09", "1971-03-07", "…
#> $ additional_name  <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…

# }