Summarise the characteristics of the base population of a cdm object.

Usage

summarisePopulationCharacteristics(
  cdm,
  studyPeriod = c(NA, NA),
  sex = FALSE,
  ageGroup = NULL
)

Arguments

cdm: A cdm object.
studyPeriod: Dates to trim the observation period. If NA, start_observation_period and/or end_observation_period are used.
sex: Boolean variable. Whether to stratify the results by sex.
ageGroup: List of age groups to stratify by at index date. Set to NULL if no stratification is needed.

Value

A summarised_result object.

Examples

# \donttest{
library(dplyr)
library(OmopSketch)

# Connect to a mock database
cdm <- mockOmopSketch()

# Run summarise population characteristics
summarisedPopulation <- summarisePopulationCharacteristics(cdm = cdm,
                                                          studyPeriod = c("2010-01-01",NA),
                                                          sex = TRUE,
                                                          ageGroup = NULL
                                                          )
#> Warning: ! 1 casted column in og_003_1726698563 (cohort_set) as do not match expected
#>   column type:
#> • `cohort_definition_id` from numeric to integer
#> Warning: ! 1 column in og_003_1726698563 do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> ! cohort columns will be reordered to match the expected order:
#>   cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date.
#> ℹ Building new trimmed cohort
#> Warning: ! 1 column in tmp_003_og_005_1726698563 do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> Creating initial cohort
#> ! 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.
#> ✔ Cohort trimmed
#> ℹ adding demographics columns
#> ℹ summarising data
#> ✔ summariseCharacteristics finished!
#> ! The following column type were changed:
#> • variable_name: from integer to character
summarisedPopulation |> print()
#> # A tibble: 122 × 13
#>    result_id cdm_name       group_name  group_level  strata_name strata_level
#>        <int> <chr>          <chr>       <chr>        <chr>       <chr>       
#>  1         1 mockOmopSketch cohort_name demographics overall     overall     
#>  2         1 mockOmopSketch cohort_name demographics sex         Female      
#>  3         1 mockOmopSketch cohort_name demographics sex         Male        
#>  4         1 mockOmopSketch cohort_name demographics overall     overall     
#>  5         1 mockOmopSketch cohort_name demographics sex         Female      
#>  6         1 mockOmopSketch cohort_name demographics sex         Male        
#>  7         1 mockOmopSketch cohort_name demographics overall     overall     
#>  8         1 mockOmopSketch cohort_name demographics overall     overall     
#>  9         1 mockOmopSketch cohort_name demographics overall     overall     
#> 10         1 mockOmopSketch cohort_name demographics overall     overall     
#> # ℹ 112 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>
PatientProfiles::mockDisconnect(cdm = cdm)

# }