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Clinical trials are increasingly costly and, given advances in medicine, the plausible treatment benefits are generally modest. Large-scale well phenotyped biobank studies (with 100K to >1M participants) are emerging and can offer opportunities to improve trial cost-effectiveness and enhance clinical trial design. Harnessing the power of genetic instruments that mimic the biological drug target and assessing them in biobank populations in advance of large Phase 3 trials could help us to understand the anticipated effects of new compounds better, refine endpoint definitions and estimate study power more accurately, and thereby have considerable impact on future trials.

This project will offer the opportunity to work with large-scale biobank and cardiovascular clinical trial data. Initially, you will undertake a review of current and early phase cardiovascular drug targets that parallel genetic effects (for example, the CETP gene parallels the main mechanism by which CETP inhibitors exert their effects), the availability of biobank data and the ways in which such resources can help inform clinical trial design. You will use genetic tools to investigate the likely impact of the main mechanism of action of such drugs on intermediate phenotypes (e.g. blood based biomarkers), different trial endpoints (e.g. heart attack, stroke, revascularisations) and on potential side-effects, and consider their implications for trial design.

Research Experience, Research Methods and Training

The project will provide an extensive range of training opportunities in the design and conduct of clinical trials as well as statistical aspects of trials methodology research and pharmacogenetics. You will learn from an experienced multi-disciplinary team of statisticians, clinicians, statistical geneticists and programmers who will provide ongoing training in the various aspects of handling and analysing ‘Big Data’.

Field Work, Secondments, Industry Placements and Training

Additional training in statistical programming and cardiovascular clinical trials and epidemiology and in pharmacogenetics will be provided. The successful applicant will have opportunities to attend and present work at relevant meetings.

Prospective Candidate

The project involves statistical analysis of big data and requires previous statistical programming training/experience (e.g. R, SAS) and a keen interest in developing these skills. Examples of desirable prior qualifications are an MMath or MSc in Medical/Applied Statistics. In addition, an understanding of genetic epidemiology, clinical trials and prospective studies would be a considerable advantage.



  • Jemma Hopewell
    Jemma Hopewell

    Associate Professor & Senior Scientist in Genetic Epidemiology and Clinical Trials

  • Jane Armitage
    Jane Armitage

    Professor of Clinical Trials and Epidemiology, and Honorary Consultant in Public Health Medicine

  • Sarah Parish
    Sarah Parish

    Professor of Medical Statistics and Epidemiology