Database diagnostics
In PhenotypeR: Assess Study Cohorts Using a Common Data Model

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 be using the Eunomia synthetic data.

library(CDMConnector)
library(OmopSketch)
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")

Database diagnostics

Although we may have created our study cohort, to inform analytic decisions and interpretation of results requires an understanding of the dataset from which it has been derived. The databaseDiagnostics() function will help us better understand a data source.

To run database diagnostics we just need to provide our cdm reference to the function.

db_diagnostics <- databaseDiagnostics(cdm)

Database diagnostics builds on OmopSketch package to perform the following analyses:

Snapshot: Summarises the meta data of a CDM object by using summariseOmopSnapshot()
Observation periods: Summarises the observation period table by using summariseObservationPeriod(). This will allow us to see if there are individuals with multiple, non-overlapping, observation periods and how long each observation period lasts on average.

The output is a summarised result object.