Summarise Database Characteristics for OMOP CDM

Usage

databaseCharacteristics(
  cdm,
  omopTableName = c("visit_occurrence", "visit_detail", "condition_occurrence",
    "drug_exposure", "procedure_occurrence", "device_exposure", "measurement",
    "observation", "death"),
  sample = NULL,
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL,
  interval = "overall",
  conceptIdCounts = FALSE,
  ...
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

omopTableName

A character vector of the names of the tables to summarise in the cdm object. Run clinicalTables() to check the available options.

sample

Either an integer or a character string.

If an integer (n > 0), the function will first sample n distinct person_ids from the person table and then subset the input tables to those subjects.
If a character string, it must be the name of a cohort in the cdm; in this case, the input tables are subset to subjects (subject_id) belonging to that cohort.
Use NULL to disable subsetting (default value).

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

dateRange

A vector of two dates defining the desired study period. Only the start_date column of the OMOP table is checked to ensure it falls within this range. If dateRange is NULL, no restriction is applied.

interval

Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".

conceptIdCounts

Logical; whether to summarise concept ID counts (TRUE) or not (FALSE).

...

additional arguments passed to the OmopSketch functions that are used internally.

Value

A summarised_result object with the results.

Examples

if (FALSE) { # \dontrun{
library(OmopSketch)
library(omock)
library(dplyr)
library(here)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- databaseCharacteristics(
  cdm = cdm,
  sample = 100,
  omopTableName = c("drug_exposure", "condition_occurrence"),
  sex = TRUE,
  ageGroup = list(c(0, 50), c(51, 100)),
  interval = "years",
  conceptIdCounts = FALSE
)

result |>
  glimpse()

shinyCharacteristics(result = result, directory = here())

cdmDisconnect(cdm = cdm)
} # }