Cohort diagnostics"

In PhenotypeR: Assess Study Cohorts Using a Common Data Model

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

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", message = FALSE, warning = FALSE,
  fig.width = 7
)

library(CDMConnector)
if (Sys.getenv("EUNOMIA_DATA_FOLDER") == "") Sys.setenv("EUNOMIA_DATA_FOLDER" = tempdir())
if (!dir.exists(Sys.getenv("EUNOMIA_DATA_FOLDER"))) dir.create(Sys.getenv("EUNOMIA_DATA_FOLDER"))
if (!eunomiaIsAvailable()) downloadEunomiaData(datasetName = "synpuf-1k")

Introduction

In this example we're going to summarise cohort diagnostics results for cohorts of individuals with an ankle sprain, ankle fracture, forearm fracture, or a hip fracture using the Eunomia synthetic data.

Again, we'll begin by creating our study cohorts.

library(CDMConnector)
library(CohortConstructor)
library(CodelistGenerator)
library(PatientProfiles)
library(CohortCharacteristics)
library(PhenotypeR)
library(dplyr)
library(ggplot2)

con <- DBI::dbConnect(duckdb::duckdb(), 
                      CDMConnector::eunomiaDir("synpuf-1k", "5.3"))
cdm <- CDMConnector::cdmFromCon(con = con, 
                                cdmName = "Eunomia Synpuf",
                                cdmSchema   = "main",
                                writeSchema = "main", 
                                achillesSchema = "main")

cdm$injuries <- conceptCohort(cdm = cdm,
  conceptSet = list(
    "ankle_sprain" = 81151,
    "ankle_fracture" = 4059173,
    "forearm_fracture" = 4278672,
    "hip_fracture" = 4230399
  ),
  name = "injuries")

Cohort diagnostics

We can run cohort diagnostics analyses for each of our overall cohorts like so:

cohort_diag <- cohortDiagnostics(cdm$injuries)

Our results will include a summary of the overlap between our cohorts. We could visualise this

plotCohortOverlap(cohort_diag, uniqueCombinations = TRUE)

Moreover, our results will also include a summary of the characteristics of each cohort, stratified by age group and sex.

tableCharacteristics(cohort_diag, groupColumn = c("age_group", "sex"))

You can also visualise the age distribution:

tableCharacteristics(cohort_diag, groupColumn = c("age_group", "sex"))

PhenotypeR documentation built on April 3, 2025, 10:46 p.m.