Marc Delord has a background in applied statistics and quantitative research. He passed his PhD in public health / epidemiology in 2015 at Paris Orsay University. His research area includes empirical Bayes methods in genomics research, translational research, statistical modelling in the framework of the generalized linear model, the design of experiments including early phase clinical trial and phase III clinical trial. He extended multiple imputation methods used to handle interval censored time-to-event data in the framework of the Cox proportional hazard model, to the competing risk framework. This method allows to compute cumulative incidences and propose estimate derived from the Fine and Gray model when time-to-event data are interval censored. These methods are available in the R library MIICD. He proposed also a dynamic modelling of the prevalence of chronic diseases which accounting relative survival of patients. This method has been use to estimate the long term prevalence of chronic myeloid leukaemia in France. Lately Marc Delord proposed a clustering method allowing to produce typologies of the main life course trajectories of chronic diseases associated to a given condition. 

Teaching

• Analysis of gene expression data
• Phase I dose-finding trials: new designs versus traditional designs
• Applied logisitc regression
• Introduction to Bayesian thinking and data analysis in epidemiology