Mei Sum Chan
MMORSE, FIA, CStat
Mei joined the Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) in 2017 and is based in the Big Data Institute. She is investigating biological ages as means of expressing the effects of modifiable biomarkers for chronic diseases. This involves the use of several statistical methods to characterise biological ageing pathways and biomarker associations across multiple organ systems, and is intended to aid in designing interventions for earlier disease diagnosis and targeted treatments. Her research is supported by a Nuffield Department of Population Health Scholarship.
Mei is a Fellow of the Institute and Faculty of Actuaries and the Royal Statistical Society, is a Chartered Statistician and holds a Masters in Mathematics, Operations Research, Statistics and Economics (MORSE) from the University of Warwick. She is a committee member of the Royal Statistical Society Oxford Local Group and the Oxford Medical Statistics Network (OxStat). Prior to joining the department, she worked as a Medical Statistician in a leading international insurer, within an industry-academia collaboration based in University College London, investigating socioeconomic inequalities in life expectancy with and without multimorbidity. She has also contributed to the development of statistical software for estimating state-specific life expectancies.
Biological Ageing: Statistical analysis of physical and biochemical biomarkers in UK Biobank
Professor Sarah Parish (CTSU), Dr Matthew Arnold (Department of Primary Care and Population Health, University of Cambridge) and Professor Rafael Perera (Nuffield Department of Primary Care Health Sciences)
Biological age in UK Biobank: biomarker composition and prediction of mortality, coronary heart disease and hospital admissions
Chan MS. et al, (2019)
Estimation of life expectancies using continuous-time multi-state models
van den Hout A. et al, (2019), Computer Methods and Programs in Biomedicine, 178, 11 - 18
Socio-economic inequalities in life expectancy of older adults with and without multimorbidity: a record linkage study of 1.1 million people in England.
Chan MS. et al, (2019), Int J Epidemiol