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There is conflicting evidence about the role of shift work, sleep patterns and circadian disruption on the subsequent risk of common diseases such as cancer and vascular disease, and on overall mortality.


This is an exciting opportunity for a graduate student to contribute to the evidence on potentially modifiable risk factors for common diseases and death. The project will involve analysis of data from three large prospective cohort studies, UK Biobank, the Million Women Study, and EPIC-Oxford. These studies have collected extensive data on participant characteristics and the cohorts have been followed through linkage to national electronic health records for incidence of cancer, heart disease, stroke and other outcomes, including deaths.

The UK Biobank study includes 500,000 men and women recruited between 2006 and 2010 with collection of detailed information on lifestyle, diet, and clinical measurements, as well as genotyping and selected biomarker data on all participants and accelerometer data on a subset, and has established linkage to other health records in the UK.

The Million Women Study includes over a million UK women recruited more than 20 years ago. Information on behaviours, including duration of sleep, naps during the day, use of sleeping pills, preference for mornings or evenings, and many other factors was collected at recruitment and at subsequent study questionnaires. Further information can be found on the study website (

EPIC-Oxford includes 65,000 men and women living in the UK, many of whom are vegetarian. EPIC-Oxford is run from CEU and is a subcohort within the European Prospective Investigation into Cancer and Nutrition (EPIC), a European collaborative study established in the 1990s to investigate the links between diet, hormones, other characteristics and health. 

The main aim of the project is to examine risk of cancer, vascular disease, other common diseases and mortality associated with various factors associated with shift work, sleep and activity patterns and circadian disruption. The exact project will be shaped by the students with the supervisors.


Training will be provided within the Department of data analysis, record linkage between multiple research databases, and statistical methods and, if necessary, by external courses.

The student will review the literature and the data available, and then define a set of hypotheses to investigate through their DPhil research. The research may involve a range of methodological techniques including prospective cohort, nested case-control and cross-sectional analyses, the analysis of questionnaire and biomarker data, and the incorporation of wearable device and genetic data in epidemiological analyses. The student may conduct Mendelian randomisation analyses of selected risk factors and biomarkers in relation to disease risk.


The candidate should have an MSc degree in epidemiology or statistics.

This doctoral programme will suit someone with an interest in epidemiology and the aetiology of non-communicable diseases, who is looking to expand their skills and experience in epidemiological study design, the statistical analysis of questionnaire and other epidemiological data, which may include biomarker and genetic data, as well as data from wearable devices.


  • Ruth Travis
    Ruth Travis

    Professor of Epidemiology and Senior Molecular Epidemiologist