Summarise missing data in omop tables

Usage

summariseMissingData(
  cdm,
  omopTableName,
  col = NULL,
  sex = FALSE,
  year = lifecycle::deprecated(),
  interval = "overall",
  ageGroup = NULL,
  sample = 1e+05,
  dateRange = NULL
)

Arguments

cdm: A cdm object
omopTableName: A character vector of the names of the tables to summarise in the cdm object.
col: A character vector of column names to check for missing values. If NULL, all columns in the specified tables are checked. Default is NULL.
sex: TRUE or FALSE. If TRUE code use will be summarised by sex.
year: deprecated
interval: Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".
ageGroup: A list of ageGroup vectors of length two. Code use will be thus summarised by age groups.
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. By default sample = 100000
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.

Value

A summarised_result object with results overall and, if specified, by strata.

Examples

# \donttest{
library(OmopSketch)

cdm <- mockOmopSketch(numberIndividuals = 100)

result <- summariseMissingData(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "visit_occurrence"),
  sample = 10000
)
#> The person table has ≤ 10000 subjects; skipping sampling of the CDM.
#> The person table has ≤ 10000 subjects; skipping sampling of the CDM.

PatientProfiles::mockDisconnect(cdm)
# }