Summarise missing data

In OmopSketch: Characterise Tables of an OMOP Common Data Model Instance

Introduction

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

In this vignette, we explore how OmopSketch functions can serve as a valuable tool for summarising missingness in databases containing electronic health records mapped to the OMOP Common Data Model.

Create a mock cdm

To illustrate the package’s functionality, we begin by loading the required packages and connecting to a test CDM using the Eunomia GiBleed dataset.

library(dplyr)
library(omock)
library(OmopSketch)

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

cdm

Summary of missing data

A common first step in data quality assessment is to identify missing values. In this contest, missing data are defined as either NA values or concept IDs equal to 0 (counts are separated by either of the cases).

You can use the summariseMissingData() function to summarise missingness across the clinical tables in the CDM:

result_missingData <- summariseMissingData(
  cdm = cdm,
  omopTableName = "observation_period"
)

result_missingData |> 
  glimpse()

Summarise by OMOP CDM table

You can choose to summarise missing data for specific OMOP CDM tables using the argument omopTableName.

result_missingData <- summariseMissingData(
  cdm = cdm,
  omopTableName = c(
    "observation_period", "visit_occurrence", "condition_occurrence", 
    "drug_exposure", "procedure_occurrence", "device_exposure", "measurement",
    "observation", "death"
  )
)

Summarise by sex

You can choose to summarise missing data by sex by setting the argument sex to TRUE.

result_missingData <- summariseMissingData(
  cdm = cdm,
  omopTableName = c(
    "observation_period", "visit_occurrence", "condition_occurrence",
    "drug_exposure", "procedure_occurrence", "device_exposure", "measurement", 
    "observation", "death"
  ),
  sex = TRUE
)

Summarise by age group

You can choose to summarise missing data by age group by creating a list defining the age groups you want to use.

result_missingData <- summariseMissingData(
  cdm = cdm,
  omopTableName = c(
    "observation_period", "visit_occurrence", "condition_occurrence", 
    "drug_exposure", "procedure_occurrence", "device_exposure", "measurement", 
    "observation", "death"
  ),
  ageGroup = list(c(0, 17), c(18, 64), c(65, 150))
)

Summarise by date and/or time interval

You can also summarise missing data within a specific date range or across defined time intervals using the dateRange and interval arguments. The interval argument supports "overall" (no time stratification), "years", "quarters", or "months".

result_missingData <- summariseMissingData(
  cdm = cdm,
  omopTableName = c(
    "observation_period", "visit_occurrence", "condition_occurrence", 
    "drug_exposure", "procedure_occurrence", "device_exposure", "measurement", 
    "observation", "death"
  ),
  interval = "years",
  dateRange = as.Date(c("2012-01-01", "2019-01-01"))
)

Summarise by column

You can also choose to summarise missing data for specific columns in the OMOP CDM tables using the argument col.

result_missingData <- summariseMissingData(
  cdm = cdm,
  omopTableName = c(
    "observation_period", "visit_occurrence", "condition_occurrence", 
    "drug_exposure", "procedure_occurrence", "device_exposure", "measurement", 
    "observation", "death"
  ),
  col = c("observation_period_start_date", "observation_period_end_date")
)

Summarise in sample of OMOP CDM

Finally, you can summarise missing data on a subset of subjects via the sample argument: provide an integer to randomly select that many person_ids from the person table, or a character string naming a cohort table to limit counts to its subject_ids.

result_missingData <- summariseMissingData(
  cdm = cdm,
  omopTableName = c(
    "observation_period", "visit_occurrence", "condition_occurrence", 
    "drug_exposure", "procedure_occurrence", "device_exposure", "measurement", 
    "observation", "death"
  ),
  sample = 1000
)

Visualise summary results

You can present these results using the function tableMissingData().

result_missingData <- summariseMissingData(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "drug_exposure", "procedure_occurrence"),
  sex = TRUE,
  ageGroup = list(c(0, 17), c(18, 64), c(65, 150)),
  interval = "years",
  dateRange = as.Date(c("2012-01-01", "2019-01-01")),
  sample = 1000
)
result_missingData |> tableMissingData()

This table can either be of type gt (default) or flextable.

tableMissingData(result = result_missingData, type = "gt")

Disconnect from CDM

Finally, disconnect from the mock CDM.

cdmDisconnect(cdm = cdm)

OmopSketch documentation built on Aug. 27, 2026, 5:07 p.m.