Why OMOP?

Last updated on 2025-09-10 | Edit this page

Overview

Questions

  • Why use OMOP?
  • Why not use spreadsheets?
  • What are the advantages of OMOP?
  • What are the disadvantages of OMOP?

Objectives

  • Examine the diagram of the OMOP tables and the data specification
  • Familiarise with the vocab schema
  • Join two or more tables together
  • Attempt to join data from spreadsheets with different structures
  • Describe the pros and cons on using OMOP vs raw data, why this is the way forward
  • Use Athena and other OHDSI tools for reference
  • Describe the full landscape of OMOP tools and the community

Introduction

This is a lesson created via The Carpentries Workbench. It is written in Pandoc-flavored Markdown for static files and R Markdown for dynamic files that can render code into output. Please refer to the Introduction to The Carpentries Workbench for full documentation.

What you need to know is that there are three sections required for a valid Carpentries lesson template:

  1. questions are displayed at the beginning of the episode to prime the learner for the content.
  2. objectives are the learning objectives for an episode displayed with the questions.
  3. keypoints are displayed at the end of the episode to reinforce the objectives.
Challenge

Challenge 1: Compare data from two separate OMOP data sets

Let’s read in an OMOP extract called extract_1 from the local files.

R 

omop_dataset_file_location_1 <- here::here("extracts/uclh1")

extract_1 <- read_omop_dataset(omop_dataset_file_location_1)

A colleague at the hospital is familiar with the events that occurred in hospital to one of the patients in the dataset. This patient has been identified by the data team as the anonymised patient with person id 7.

R 

extract_1$person |>
  filter(person_id==7) |>
  select(person_id, race_concept_id, gender_concept_id, year_of_birth) |>
  omopcept::omop_join_name_all() |>
  collect()
Checklist

Verify that the patient details match your colleague’s description.

Let’s take a sample of patients in this dataset, selecting those same columns.

R 

extract_1_pt_sample <- extract_1$person |>
  slice_sample(n = 10) |>
  select(person_id, race_concept_id, gender_concept_id, year_of_birth) |>
  collect()

We’ve received another OMOP dataset from another site.

R 

omop_dataset_file_location_2 <- here::here("extracts/other_site_1")

extract_2 <- read_omop_dataset(omop_dataset_file_location_2)

Let’s take a sample of patients from the second extract and bind them together.

Callout

Note that, because the structure of the data (table names, columns and data types) are set as standard by the OMOP specification, we are guaranteed to be able to bind these two datasets together without error. We can also re-apply the same code, only changing the reference to the new extract.

R 

extract_2_pt_sample <- extract_2$person |>
  slice_sample(n = 10) |>
  select(person_id, race_concept_id, gender_concept_id, year_of_birth) |>
  collect()
  
 bind_rows(extract_1_pt_sample, extract_2_pt_sample)

R 

dplyr::tibble(person_id = c(101,102,201,202), year_of_birth = c(1992,1993,1994,1995))
Challenge

Challenge 2: how do you nest solutions within challenge blocks?

You can add a line with at least three colons and a solution tag.

Figures

You can also include figures generated from R Markdown:

R 

pie(
  c(Sky = 78, "Sunny side of pyramid" = 17, "Shady side of pyramid" = 5), 
  init.angle = 315, 
  col = c("deepskyblue", "yellow", "yellow3"), 
  border = FALSE
)
pie chart illusion of a pyramid
Sun arise each and every morning

Or you can use standard markdown for static figures with the following syntax:

![optional caption that appears below the figure](figure url){alt='alt text for accessibility purposes'}

Blue Carpentries hex person logo with no text.
You belong in The Carpentries!
Callout

Callout sections can highlight information.

They are sometimes used to emphasise particularly important points but are also used in some lessons to present “asides”: content that is not central to the narrative of the lesson, e.g. by providing the answer to a commonly-asked question.

Math

One of our episodes contains \(\LaTeX\) equations when describing how to create dynamic reports with {knitr}, so we now use mathjax to describe this:

$\alpha = \dfrac{1}{(1 - \beta)^2}$ becomes: \(\alpha = \dfrac{1}{(1 - \beta)^2}\)

Cool, right?

Key Points
  • Use .md files for episodes when you want static content
  • Use .Rmd files for episodes when you need to generate output
  • Run sandpaper::check_lesson() to identify any issues with your lesson
  • Run sandpaper::build_lesson() to preview your lesson locally