--- title: "PopulationDiagnostics" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{a05_PopulationDiagnostics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE, fig.width = 7 ) library(CDMConnector) if (Sys.getenv("EUNOMIA_DATA_FOLDER") == "") Sys.setenv("EUNOMIA_DATA_FOLDER" = tempdir()) if (!dir.exists(Sys.getenv("EUNOMIA_DATA_FOLDER"))) dir.create(Sys.getenv("EUNOMIA_DATA_FOLDER")) if (!eunomia_is_available()) downloadEunomiaData(datasetName = "synpuf-1k") ``` ## Introduction In this example we're going to just create a cohort of individuals with an ankle sprain using the Eunomia synthetic data. ```{r, message=FALSE, warning=FALSE} library(CDMConnector) library(CohortConstructor) library(CodelistGenerator) library(PatientProfiles) library(IncidencePrevalence) library(PhenotypeR) con <- DBI::dbConnect(duckdb::duckdb(), CDMConnector::eunomiaDir("synpuf-1k", "5.3")) cdm <- CDMConnector::cdmFromCon(con = con, cdmName = "Eunomia Synpuf", cdmSchema = "main", writeSchema = "main", achillesSchema = "main") cdm$injuries <- conceptCohort(cdm = cdm, conceptSet = list( "ankle_sprain" = 81151 ), name = "injuries") ``` We can get the incidence and prevalence of our study cohort using `populationDiagnostics()`. ```{r} pop_diag <- populationDiagnostics(cdm$injuries) ``` We can quickly make tables with our results like so ```{r} tableIncidence(pop_diag, groupColumn = c("cdm_name", "outcome_cohort_name"), hide = "denominator_cohort_name", settingsColumn = c("denominator_age_group", "denominator_sex", "denominator_days_prior_observation", "outcome_cohort_name")) ``` ```{r} tablePrevalence(pop_diag, groupColumn = c("cdm_name", "outcome_cohort_name"), hide = "denominator_cohort_name", settingsColumn = c("denominator_age_group", "denominator_sex", "denominator_days_prior_observation", "outcome_cohort_name")) ```