An opportunity has arisen for a talented statistician or health data scientist at the MRC Biostatistics Unit, Cambridge University, within the Statistical methods Using data Resources to improve Population Health theme. The MRC Biostatistics Unit is a leading centre of innovative biostatistics research.
Multimorbidity is when people suffer from more than one long-term illness. It is increasingly common as people live longer. It is important because: individual illnesses have knock-on effects on others, it is more complex managing multiple than single illnesses, and multimorbid patients are heavy users of medications and health services. Electronic health records (EHRs) are a good source of information on multimorbidity because they include information on the same patient over many years.
We are seeking an ambitious and motivated individual to work with Dr Steven Kiddle on modelling the development of multimorbidity (multiple long-term diseases), and its relation to risk factors, treatment and outcomes. How to prevent and manage multiple diseases is a challenge that doctors face every day, and yet there is almost no evidence on how to do this most effectively. This project will involve the secondary use of large (N > 50,000) longitudinal electronic health record datasets, with the option to also study linked genomic data (e.g. genetic, protein, metabolite). Methodology will be applied to address clinical questions in close collaboration with clinicians and health informaticians.
The successful candidate will have a PhD in in a strongly quantitative discipline, ideally statistics or health data science, and experience of casual inference (e.g. from observational data or mendelian randomisation) and/or time-to-event models (e.g. survival or multi-state models). Past experience with EHRs and/or other "big data" sources would be highly advantageous, but not essential; training will be given on the basic concepts necessary to the post. A desire to address questions of substantive biomedical and societal importance is essential. Good communication skills and an enthusiasm for collaborating with others (including non-statisticians) are also essential. Strong programming ability would be desirable, and experience of computational statistical methods would be highly advantageous. The successful applicant will be supported in their career development with a range of formal courses and on-the-job training.
For an informal discussion about this post please contact: firstname.lastname@example.org
Fixed-term: The funds for this post are available for 2 years in the first instance.
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Closing date: 8 March 2020
Interview date: 19 March 2020
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