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Cohort diagnostics

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, match = TRUE)

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"))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.