Real World Epidemiology: Oxford Summer School

Oxford Summer School 2026: Real World Evidence using the OMOP Common Data Model

Our Real World Evidence Summer School will provide participants with the tools and concepts necessary to plan and execute Real World Evidence studies, with a focus on the use of the OMOP common data model. The course will have morning lectures followed by afternoon practicals where concepts discussed in the morning will be put in practice with hands-on sessions. Practical sessions will have two tracks: a) for those interested in the design of studies and use of existing analytical and data curation tools; and b) for more advanced data scientists and programmers interested in the development or modification of analytical code using R.

Course Venue and accommodation is St. Hilda's college

Cancellation Policy:
You may cancel your booking for the Oxford Summer School 2026 by notifying us in writing. Refunds will be provided based on the notice period as follows:

• Before 28th April 2026: 100% refund
• Between 28th April 2026 and 31st May 2026: 50% refund
• From 1st June 2026 onwards: No refund

Course Description

LEARNING GOALS:

 

1. DATA DISCOVERY: Gain an understanding of the existing sources of routinely collected data for epidemiological research

 

2. THE OMOP COMMON DATA MODEL: Become capable to explain the principles underpinning this common data model, and to provide examples of existing real world data mapped to this CDM.

 

3. RWE STUDY DESIGN/S: Be able to discuss common types of real world evidence study designs, including cohort, case-control, and case only studies.

 

4. PHARMACO- AND DEVICE EPIDEMIOLOGY: Be aware of the applications of real world data in both pharmaco and device epidemiological studies, including drug/device utilisation and safety research.

 

5. PREDICTION MODELLING: Learn basic concepts on the design and evaluation of prognostic/prediction models developed using real world data; and the use of such methods for treatment heterogeneity/personalised medicine research.

 

6. REAL WORLD EVIDENCE METHODS: Be familiar with the basics of RWE methods, including a) machine learning, b) principles of network/federated multi-database studies, and c) methods to minimise confounding (e.g. propensity scores).

 

7. TARGET TRIAL EMULATION: You will learn the basics of target trial emulation methods, and hear on previous experiences duplicating/replicating trials using Real World data

 

8. PRACTICAL SKILLS IN RWE STUDY DESIGN AND ANALYSIS: Acquire hands-on experience and skills designing and implementing RWE analysis plans, and/or programmatic skills.