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Brief Description:

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.

 

Registration: It is now open 

Venue: Lady Margaret Hall Talbot Hall Theatre, Norham Gardens, Oxford OX2 6QA

Date: 19th- 23rd June 2023

For booking please use Booking information

Please see the Preliminary Programme here

AUDIENCE:

Pharmacists, clinicians, academics (including statisticians, epidemiologists, and related MSc/PhD students); Industry (pharmacy or device) or Regulatory staff with an interest in the use of routinely collected data for research. 

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, c) methods to minimise confounding (e.g. propensity scores), and d) the target trial framework.
 
7. PRACTICAL SKILLS IN RWE STUDY DESIGN AND ANALYSIS: Acquire hands-on experience and skills designing and implementing RWE analysis plans, and/or programmatic skills.

 

Cancellation Policy

You may cancel your booking for the Oxford Summer School 2023 by notifying us in writing. You will receive the % of refund back based on the period of notice received as follows: 

  • Before 28th April 2023: 100% 
  • After 28th April and until 31st May 2023: 50%.
  • From 1st June 2023: 0%


For more information about the registration process, please contact the course administrators (mahkameh.mafi@ndorms.ox.ac.uk).