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Bayesian network modelling of the factors influencing the development of cancer in patients with type 2 diabetes Dr Andrew Renehan & Professor Iain Buchan The objective of this project is to test the hypothesis that cancer incidence and outcomes in type 2 diabetics is determined by a finite number of pathways, co-varying with socio-demographic, lifestyle, metabolic disease control and treatment factors - all of which may be time dependent. The studentship provides full support for tuition fees, associated research costs and an annual tax-free stipend at Research Council rates (anticipated to be £13, 490). The project is due to commence October 2010 and is open to UK/EU nationals only due to the nature of the funding. Rationale: The increasing prevalence of obesity and related diseases, such as type-2 diabetes, is a substantial public health problem. It has been established over the past five years that both obesity and type-2 diabetes are associated with an increased cancer burden and adverse outcome to cancer therapy. This project will aim to address cancer outcomes among type-2 diabetics, by employing realistically complex, state of-the-art modelling methods and under-used routine clinical data. The course of type-2 diabetes involves a complex interplay of clinical interventions with social, environmental and behavioural factors. As health e-records become more complete, more of these factors are captured in a form that can be researched. Conventional research focuses on relatively small numbers of the factors that determine disease risk and treatment outcomes, and the resulting statistical models are narrowly generalizable. To address the gap in the evidence base that exists between clinical outcomes 'in the wild' and relative risk reductions from clinical trials, or relative risks from epidemiological studies, the investigator must combine multiple small models into a larger model. In this proposal we bridge a research modelling gap between diabetes and cancer research, by taking a unified modelling approach. This work builds on existing activities to introduce machine learning techniques into clinical research. It is ideally suited to incorporating factors that are difficult to observe or highly inter-dependent, which is typical of social, behavioural and environmental determinants of health. The study would suit a highly numerate individual from a non-clinical background with the ability to apply themselves to complex clinical problems through mathematical modelling. The study will be conducted within the North West Institute for Bio-Health Informatics and Paterson Institute for Cancer Research. The successful candidate will develop expertise in biostatistics, Bayesian inference, statistical software engineering and health systems modelling. Upon completion, progression into post-doctoral research posts in health services research, biostatistics, health informatics or epidemiology would be anticipated. Applicants should hold, or expect to obtain, a minimum upper-second honours degree (or equivalent) in statistics, mathematics, physics, computer science or health-related science (with a strong quantitative component). A Masters qualification in a similar area is essential. Aptitude for computational thinking and basic software engineering skills are desirable attributes. Please direct applications in the following format to mailto:[log in to unmask] * A CV, including full details of all University course grades to date. * Contact details for two academic or professional referees. * A personal statement (750 words maximum) outlining your suitability for the study, what you hope to achieve from the PhD and your research experience to date. Any enquiries relating to the project and/or suitability should be directed to Dr Andrew Renehan at mailto:[log in to unmask] Applications are invited up to and including Thursday 18 February 2010. https://www.nibhi.org.uk/default.aspx Prof. Iain E. Buchan Director NIBHI & NWeH Science, University of Manchester 1.311, Jean Macfarlane Building, Oxford Road, Manchester M13 9PL, UK. Tel +44 (0)161 275 5160 www.nibhi.org.uk You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.