Tel: +44 (0)1223 761950
Michael completed his BSc in Mathematics and Statistics at the University of Warwick in 2000 and an MSc in Medical Statistics at the University of Leicester in 2001. He has worked in the MRC Biostatistics Unit, Cambridge from 2002 – 2013, and completed a PhD at the University of Cambridge in 2008. Michael joined the CEU in September 2013.
Michael’s research interests include risk prediction using longitudinal and survival analysis, meta-analysis, cost-effectiveness analyses for public health interventions and novel designs for early phase trials.
Dr Sweeting hasworked on the joint modelling of growth and rupture rates in patients with abdominal aortic aneurysm (AAA) and has used the models to predict future risk of rupture for various AAA size categories. Through the synthesis of estimates from a large individual patient data meta-analysis we have been able to recommend cost-effective surveillance intervals for the NHS AAA Screening Programme.
At the CEU he is working on statistical methods in the development and assessment of cardiovascular risk scores and their predictive utility. Michael is particularly interested in the comparison of traditional risk scores with dynamic (time-updated) predictions using repeatedly measured risk factors.
RESCAN Collaborators. Surveillance intervals for small abdominal aortic aneurysms: a meta-analysis. Journal of the American Medical Association. 2013;309:806-813. [Pubmed ID:23443444]
Lunn D, Barrett J, Sweeting M, Thompson S. Fully Bayesian hierarchical modelling in two stages, with application to meta-analysis. Journal of the Royal Statistical Society Series C: Applied Statistics. 2013;62:551-572. [Pubmed ID: 24223435] [Pubmed ID: 24223435]
Sweeting MJ, Thompson SG, Brown LC, Powell JT, on behalf of the RESCAN collaborators. Meta-analysis of individual patient data to examine factors affecting growth and rupture of small abdominal aortic aneurysms. British Journal of Surgery. 2012;99:655-665. [Pubmed ID: 22389113]
Sweeting M, Thompson S. Making predictions from complex longitudinal data, with application to planning monitoring intervals in a national screening programme. Journal of the Royal Statistical Society Series A: Statistics in Society. 2012;175:596-586. [Pubmed: 22879705]
Sweeting M, Thompson S. Joint modelling of longitudinal and time-to-event data with application to abdominal aortic aneurysm growth and rupture. Biometrical Journal. 2011;53:750-763. [Pubmed ID: 21834127]
Sweeting MJ, De Angelis D, Parry J, Suligoi, B. Estimating the distribution of the window period for recent HIV infections: a comparison of statistical methods. Statistics in Medicine. 2010;29:3194-3202. [Pubmed 21170913]
Sweeting MJ, Thompson SG, Brown LC, Greenhalgh RM, Powell JT. Use of angiotensin converting enzyme inhibitors is associated with increased growth rate of abdominal aortic aneurysms.Journal of Vascular Surgery. 2010;52:1-4. [Pubmed ID: 20494541]
Sweeting MJ, Farewell VT, De Angelis D. Multi-state Markov models for disease progression in the presence of informative examination times: An application to hepatitis C. Statistics in Medicine. 2010;29:1161-1174. [Pubmed ID: 20437454]
Sweeting MJ, Hope VD, Hickman M, Parry JV, Ncube F, Ramsay ME, De Angelis D. Hepatitis C infection among injecting drug users in England and Wales (1992-2006): there and back again? American Journal of Epidemiology. 2009;170:352-360. [Pubmed ID: 19546152]
Sweeting MJ, De Angelis D, Hickman M, Ades AE. Estimating hepatitis C prevalence in England and Wales by synthesizing evidence from multiple data sources. Assessing data conflict and model fit. Biostatistics. 2008;9:715-734. [Pubmed ID: 18349037]