About the Project
This DPhil in Clinical Epidemiology and Medical Statistics is a 3.5-year project focusing on the development and validation of external control arms (ECAs) using routinely collected real-world healthcare data.
Randomised controlled trials (RCTs) are the gold standard for evaluating healthcare interventions because randomisation provides the strongest basis for causal inference. However, conventional RCTs are not always feasible because of high costs, practical difficulties, recruitment challenges, or ethical considerations. An increasingly considered alternative is the use of external control arms, where the comparator group is derived from data collected outside the trial, while the treatment group comes from the clinical trial itself. However, ECAs based on observational real-world data are susceptible to confounding, selection bias, differences in outcome measurement, and incomplete capture of trial eligibility criteria. Their validity therefore depends both on appropriate causal inference methods and on the quality and richness of the underlying data.
The proposed project will evaluate whether ECAs constructed from electronic healthcare records (EHR) can reproduce findings from completed RCTs. Using EHR UK data the project will benchmark ECA-derived treatment effects against completed trials and, where participant-level data linkage is available, evaluate the additional value of linking trial participants to electronic healthcare records. This will allow assessment of whether richer linked data improve covariate ascertainment, outcome capture, follow-up, and control of confounding.
The project will also extend ECA methodology to longitudinal treatment pathways, including treatment discontinuation, switching, and non-adherence. Causal inference methods for time-varying treatment and confounding, including marginal structural models, G-computation, and G-estimation, will be evaluated. Overall, the project will provide empirical evidence on when RWD-derived external controls can generate credible treatment-effect estimates and support future clinical trial design, regulatory decision-making, and health technology assessment.
Supervisors
- Dr. Martí Català Sabaté — https://www.ndorms.ox.ac.uk/team/marti-catala-sabate
- Prof. Daniel Prieto-Alhambra — https://www.ndorms.ox.ac.uk/team/daniel-prieto-alhambra
- Dr. Trishna Rathod-Mistry — https://www.ndorms.ox.ac.uk/team/trishna-rathod-mistry
- Dr. Anna Saura Lazaro — https://www.ndorms.ox.ac.uk/team/anna-saura-lazaro
- Dr. Sofia Massa — https://www.ndorms.ox.ac.uk/team/sofia-massa
Training
The Health Data Sciences Section is part of the Botnar Research Centre, University of Oxford. The Botnar Research Centre 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. Relevant training will be provided in 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 or MSc by research will commence in October 2027.
Applications should be made to one of the following programmes 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.
Applications open mid-September
Application deadline: 12:00 on 1st December
References
Lin L, Lucassen MJJ, van der Noort V, et al. The feasibility of using real world data as external control arms in oncology trials. Drug Discov Today. 2025;30(3):104324. doi: 10.1016/j.drudis.2025.104324.
Vaitsiakhovich T, Hartenstein A, Privitera S, et al. An External Control Arm for the Oral Factor XIa Inhibitor Asundexian Phase 2 Trial in Atrial Fibrillation (PACIFIC-AF) Using Electronic Health Records. Cardiol Ther. 2025;14(3):403–421. doi: 10.1007/s40119-025-00411-x.
Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550–560. doi: 10.1097/00001648-200009000-00011.
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