Associate Professor Derrick Bennett
BSc MSc PhD CStat
Derrick has a BSc (Hons) in Mathematics and Statistics, an MSc in Medical Statistics, and a PhD in Epidemiology and Statistics. He has been a Royal Statistical Society accredited Chartered Statistician since 2005.
His research is interdisciplinary, integrative and collaborative and uses large-scale observational studies and randomised trials to generate reliable evidence for the prevention of premature deaths and disability from chronic diseases. His work involves applying statistical, epidemiological, computational, and genetic tools to understand associations of exposures with chronic diseases. His research aims to drive improvements in population health by identifying novel treatment targets and implementing precision strategies for primary and secondary prevention of major disease outcomes such as cardiovascular disease, stroke, diabetes and cancer.
He co-leads the Statistical Group in the China Kadoorie Biobank and oversees a portfolio of research related to aging, cardiovascular, respiratory, and lifestyle factors. He is responsible for ensuring that the study design methodology is robust, appropriate and deliverable as well as for securing grant income as the statistical lead.
Derrick co-leads the Principles of Data Science module of the MSc in Global Health Science and Epidemiology, and leads the curriculum development for data science teaching. He is currently supervising several MSc and DPhil students.
He has also contributed chapters to four textbooks and was named as a highly cited researcher in 2018 for papers that rank in the top 1% in his field of research. In 2022 he was listed among the top 1000 scientists in the UK in the Research.com Medicine rankings.
An overview of methods and exemplars of the use of Mendelian Randomisation in nutritional research
BENNETT D. and DU H., (2022), Nutrients
Resting heart rate and risk of left and right heart failure in 0.5 million Chinese adults.
Agbor VN. et al, (2022), Open Heart, 9
Development, validation and comparison of multivariable risk scores for prediction of total stroke and stroke types in Chinese adults: a prospective study of 0.5 million adults.
Chun M. et al, (2022), Stroke Vasc Neurol
Frequency and types of clusters of major chronic diseases in 0.5 million adults in urban and rural China.
Hariri P. et al, (2022), J Multimorb Comorb, 12
Improved prediction of fracture risk leveraging a genome-wide polygenic risk score
Lu T. et al, (2021), Genome Medicine, 13