--- title: "Matched diagnostics" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{a04_MatchedDiagnostics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` ```{r, include = FALSE} knitr::opts_chunk$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 (!eunomia_is_available()) downloadEunomiaData(datasetName = "synpuf-1k") ``` ## Introduction In this example we're going to again create cohorts of individuals with an ankle sprain, ankle fracture, forearm fracture, or a hip fracture using the Eunomia synthetic data. ```{r, eval=FALSE} 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$injuries <- conceptCohort(cdm = cdm, conceptSet = list( "ankle_sprain" = 81151, "ankle_fracture" = 4059173, "forearm_fracture" = 4278672, "hip_fracture" = 4230399 ), name = "injuries") ``` ## Matched diagnostics Running the `matchedDiagnostics()` will compare the individuals in our cohorts with age and sex matched controls from the data source. This helps us to find features of our cohort that are particularly distinctive. ```{r, eval=FALSE} matched_diag <- matchedDiagnostics(cdm$injuries) ```