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  • Project No: NDORMS-2025/06
  • Intake: 2025

PROJECT OUTLINE:

This DPhil in Clinical Epidemiology and Medical Statistics will include a 3-year project focussing on target trial emulation studies to generate real-world evidence for arthritis and orthopaedic treatments using de-identified, routinely-collected data from primary and secondary care in the UK and across Europe.

Evidence generated from real-world data is becoming increasingly important for regulators to complement findings from randomized-controlled trials (RCT) when evaluating the effectiveness and safety of medicines and medical devices. However, with routinely-collected data being observational in nature, 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 target trial emulation framework has been developed to prevent biases in the design of observational studies. Moreover, the FDA’s RCT DUPLICATE investigated the performance of causal inference methods to produce valid estimates of treatment benefits by emulating 32 randomized-controlled trials using US health insurance claims.

The proposed project will leaverage learnings from RCT DUPLICATE and use the target trial emulation framework to conduct causal inference studies on the safety and/or effectiveness of treatments for

KEY 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.
  • Hernán MA, Sauer BC, Hernández-Díaz S, et al. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016 Nov;79:70-75. doi: 10.1016/j.jclinepi.2016.04.014.
  • Wang SV, Schneeweiss S, 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.

 

KEY WORDS:

Real-World Evidence, Target Trial Emulation, Drug Safety, Medical Devices, Health Data Sciences

RESEARCH GROUP AND SUPERVISION TEAM:

The project will be supervised by a team of experienced researchers and experts in the field of real-world evidence and orthopeadics. Prof. Daniel Prieto-Alhambra, Professor for Pharmaco- and Device Epidemiology, is leading the Health Data Sciences Team at the Botnar Research Centre, and will be the main supervisor for the DPhil project.

The supervision team will be complemented by Prof. Matthew Costa, Professor for Orthopeadic Trauma Surgery; Dr. Annika Jodicke, Senior Researcher in Pharmacoepidemiology; Dr. Albert Prats-Uribe, Senior Clinical Researcher in Public Health and Epidemiology and Dr. Edward Burn, Senior Researcher in Epidemiology and Health Economics.

TRAINING:

The Health Data Sciences section is part of the Botnar Research Centre, University of Oxford. Training will be provided in health data 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.  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 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 (Columbia 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 and run by the IT department (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 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 2025.

Applications for this project 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.

 

The Botnar Institute is a proud supporter of the Academic Futures scholarship programme, designed to address under-representation and help improve equality, diversity and inclusion in our graduate student body. The Botnar and the wider University rely on bringing the very best minds from across the world together, whatever their race, gender, religion or background to create new ideas, insights and innovations to change the world for the better. Up to 50 full awards are available across the three programme streams, and you can find further information on each stream on their individual tabs (Academic futures | Graduate access | University of Oxford).