Emulating and extending randomised controlled trials using routinely-collected data from Europe

PROJECT OVERVIEW

This DPhil in Clinical Epidemiology and Medical Statistics is a 3-year project focussing on the emulation and extension of randomised controlled trials (RCTs) using de-identified, routinely-collected, real-world medical record data from primary and secondary care across European countries.

Evidence generated from real-world data is becoming increasingly important for regulators to complement findings from RCTs when evaluating the effectiveness and safety of medicines. Moreover, real-world data provides great opportunities in complementing RCTs by extending them to i.e. include previously excluded/underrepresented groups or evaluate potential additional study outcomes or treatment indications. However, with routinely-collected data being observational in nature, such studies are prone to confounding bias and advanced analytical methods and study designs need to be applied when conducting causal inference research.

In recent years, the RCT DUPLICATE initiative investigated the performance of causal inference methods to produce valid estimates of treatment benefits by emulating 32 RCTs using US health insurance claims. Based on these learnings, the 3-step “benchmark – expand – calibrate” approach was proposed as a structured approach to emulate the original RCT in RWD, to subsequently expand the scope of that RCT (e.g. through the inclusion of additional outcomes) when emulating it in RWD, and finally to use calibration methods to reduce unmeasured confounding.

The proposed project will test the applicability of the “benchmark – expand – calibrate” approach across different data types (e.g. in electronic health records from the UK and other European countries), across different study types (evaluating safety and/or effectiveness of treatments) and various disease areas (e.g. cardiovascular and metabolic diseases, and musculoskeletal conditions).

Related publications

• Hernán MA, Robins JM. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. Am J Epidemiol. 2016 Apr 15;183(8):758-64. doi: 10.1093/aje/kwv254
• Wang SV et al.: RCT-DUPLICATE Initiative. Emulation of Randomized Clinical Trials With Nonrandomized Database Analyses: Results of 32 Clinical Trials. JAMA. 2023 Apr 25;329(16):1376-1385. doi: 10.1001/jama.2023.4221.
• Schneeweiss, Krueger. Non-Randomized Database Analyses to Complement Randomized Clinical Trials: Promising Approaches for Cardiovascular Medicine. Circulation. 2025 May 27, Vol 151(21). doi: 10.1161/CIRCULATIONAHA.125.073235

Training

The Health Data Sciences Section is part of the Botnar Institute for Musculoskeletal Sciences, University of Oxford. The Botnar Institute plays host to the University of Oxford's Institute of Musculoskeletal Sciences, which enables and encourages research and education into the causes of musculoskeletal disease and their treatment. Training will be provided in techniques including epidemiology, biostatistics, common data models, causal inference, and real world evidence methods and data.

A core curriculum of lectures will be taken in the first term to provide a solid foundation in a broad range of subjects, incl. data analysis.  Students will also be required to attend regular seminars within the Department and those relevant in the wider University.

Students will be expected to present data regularly in Departmental seminars, the Health Data Sciences Section’s fortnightly lab meeting, and to attend external conferences to present their research globally, with limited financial support from the Department.

Students will also have the opportunity to work closely with our multiple collaborators nationally and internationally, including academic centres of excellence (Harvard University, Universitat Autonoma de Barcelona, Erasmus Medical Centre, among others), regulators (UK MHRA, European Medicines Agency), and industry.

Students will have access to various courses run by the Medical Sciences Division Skills Training Team and other Departments. All students are required to attend a 2-day Statistical and Experimental Design course at NDORMS (information will be provided once accepted to the programme).

How to Apply

Please contact the relevant supervisor(s), to register your interest in the project, and, if required, the departmental Education Team (graduate.studies@ndorms.ox.ac.uk), who will be able to advise you of the essential requirements for the programme and provide further information on how to make an official application.

Interested applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject and will also need to provide evidence of English language competence (where applicable). The application guide and form is found online and the DPhil will commence in October 2026.

Applications should be made to the following programme using the specified course code. 

D.Phil in Clinical Epidemiology and Medical Statistics (course code: RD_NNRA1) 

For further information, please visit http://www.ox.ac.uk/admissions/graduate/applying-to-oxford.