Dr Marc Delord is a Research Fellow in Medical Statistics in the Department of Population Health Sciences, School of Life Course & Population Sciences. He has a background in applied statistics and epidemiology and obtained a PhD in Public Health, Epidemiology, and Biostatistics in 2015 from Paris-Saclay University. His research spans applied statistical methodology and its translation to clinical and population health research, including empirical Bayes methods, statistical modelling within the generalised linear model framework, and methodological development.

Dr Delord’s recent research focuses on the longitudinal analysis of electronic health records, including unsupervised learning methods for clustering life-course health trajectories in patients with multiple long-term conditions, as well as causal inference and real-world evidence generation. His work aims to improve the use of routinely collected health data for understanding disease trajectories and informing clinical and public health decision-making.

He is a co-investigator on the CPRD study “Patterning Mental and Physical Multimorbidity across the Life Course and Mediators for Progression Towards Burdensome Functional Outcomes”.

Dr Delord is actively involved in teaching biostatistics and statistical computing. He teaches on the module Further Epidemiology and Statistics for Public Health (7MHPH109), and is course leader for Basic Epidemiology and Statistics for Public Health (7MHPH108), which introduces statistical thinking, estimation and hypothesis testing and an introduction to the R environment. He also delivers intensive seminar training in R for quantitative research for health sciences researchers.

He has authored two statistical software packages for the analysis of complex time-to-event data, alongside associated methodological research publications. He is a Chartered Statistician and Fellow of the Royal Statistical Society (CStat) and a Fellow of the Higher Education Academy (FHEA).