PhD Studentship: Understanding How Common Medicines Influence Cancer Immunotherapy Success Through Real-World Health Data

Primary supervisor - Dr Zoe Venables

Immune checkpoint inhibitors have transformed cancer care and are now integral to treatment pathways across multiple tumour types, with melanoma representing one of the earliest and most successful examples. However, a major evidence gap remains in understanding how commonly prescribed concomitant medications (e.g. antibiotics, metformin, proton pump inhibitors, corticosteroids) influence immunotherapy effectiveness and survival outcomes. This has been identified as a NICE-recommended research priority [1], reflecting its importance for optimising real-world cancer outcomes. 

This PhD will deliver an original, staged programme of research evaluating the impact of routine medication exposure on immunotherapy outcomes. Melanoma will serve as the initial focus, with extension to other immunotherapy-treated cancers to enable cross-cancer comparison and broader generalisability. The project will leverage two complementary data sources: the TrinetX global research network for early hypothesis generation and descriptive/comparative analyses, alongside parallel applications for access to row-level registry-linked datasets (e.g. National Disease Registration Service and other established platforms such as UK Biobank), which are essential for advanced methodological training and causal inference. 

Methodologically, the PhD will employ propensity score–matched cohort designs and Cox proportional hazards regression to estimate associations with overall and disease-specific survival. Time-varying exposure modelling and sensitivity analyses will address treatment timing, polypharmacy, and immortal time bias. A core component of the research will critically evaluate the strengths and limitations of routinely collected health data, including misclassification, missingness, residual confounding, and restricted analytic control within platform-based datasets, ensuring transparent and robust interpretation of findings. 

The project aligns strongly with current healthcare priorities through its focus on cancer outcomes, real-world evidence, and data-intensive population health research with direct relevance to NHS policy and guideline development. The supervisory team combines clinical and epidemiological expertise in immunotherapy outcomes with advanced statistical and causal inference methodology, providing a rigorous interdisciplinary training environment and ensuring delivery of high-quality, impactful doctoral research.

Entry requirements

Applicants should have a minimum of an upper second class (2:1) Honours degree, master’s degree, or equivalent in a relevant subject. A degree in Statistics/Biostatistics, Data Science, Life and Social Sciences or Medicine (with substantial component and/or experience in quantitative methods), Applied Mathematics, Computing, Quantitative biology, Bioinformatics, Statistical genetics, Physics or similar. The applicants should have an interest in ageing and dementia research and motivation for learning statistical modelling and analysing genome wide genotypic data. Appropriate training will be provided depending on the needs and interests of the successful candidate. 

Mode of study: Full-time

Start date: 1 October 2026

Additional Funding Information

This project is fully funded for 3 years. Funding includes tuition fees, an annual tax-free maintenance allowance and a research training support budget.

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