Cambridge Centre for Research Associate (Fixed Term)
MRC Biostatistics Unit
The MRC Biostatistics Unit is one of Europe's leading biostatistics research institutions. Our focus is to deliver new analytical and computational strategies based on sound statistical principles for the challenging tasks facing biomedicine and public health. The Unit is situated on the Cambridge Biomedical Campus, one of the world's most vibrant centres of biomedical research, which includes the University of Cambridge's Clinical School, two major hospitals, the MRC Laboratory of Molecular Biology, and the world headquarters of Astra Zeneca.
An opportunity has arisen for a talented statistician within the Precision Medicine and Inference for Complex Outcomes theme. The successful applicant will have the opportunity to contribute to the Bringing Innovative Research Methods to Clustering Analysis of Multimorbidity (BIRM-CAM) project between the Universities of Cambridge and Birmingham, funded by the UK Medical Research Council (MRC) and the National Institute for Health Research (NIHR). The project is led by Profs. Sylvia Richardson (Cambridge) and Tom Marshall (Birmingham). Within the MRC Biostatistics Unit, Drs. Jessica Barrett, and Paul Kirk are also part of the BIRM-CAM team.
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. As part of the BIRM-CAM project, we are developing and applying sophisticated data analysis techniques to extract relevant information about multimorbidity from EHRs.
We are seeking an ambitious and motivated individual to contribute to the BIRM-CAM research team. The project will focus on the development and refinement of models that use EHRs to explore the progression of multi-morbidities over time, and how clinical outcomes such as mortality and health service usage may depend on longitudinal trajectories of multi-morbidity. Relevant modelling techniques include multi-state time-to-event modelling, prediction modelling, biomarker trajectory modelling and joint modelling.
The successful candidate will have a PhD or equivalent experience in a strongly quantitative discipline. 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.