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Henrik Ravn is senior statistical director at Novo Nordisk A/S, Denmark. He graduated with an MSc in theoretical statistics in 1992 from University of Aarhus, Denmark and completed a PhD in biostatistics in 2002 from the University of Copenhagen, Denmark. He joined Novo Nordisk in late 2015 after more than 22 years of experience from biostatistical and epidemiological research, at Statens Serum Institut, Denmark and in Guinea-Bissau, West Africa. He has co-authored more than 160 papers, mainly within epidemiology and application of survival analysis and has taught several courses as external lecturer at Section of Biostatistics, University of Copenhagen. Per Kragh Andersen is professor of biostatistics at the Department of Public Health, University of Copenhagen, Denmark since 1998. He graduated in mathematical statistics from University of Copenhagen in 1978, got his PhD in 1982 and a DMSc degree in 1997. From 1993 to 2002 he worked half time as chief statistician at Danish Epidemiology Science. He is an author or co-author of more than 125 papers on statistical methodology and more than 250 papers in the medical literature. His research has concentrated on survival analysis and he is a co-author of the 1993 book ‘Statistical Models Based on Counting Processes’. He has taught several courses both nationally and internationally both for students with a mathematical background and for students in medicine or public health.
"""The book provides a thorough overview of recent developments in the field of time-to-event analyses. It covers models for competing risks, multi-state models and recurrent events. The main distinction in the book is between intensity-based models and marginal models. Intensity-based models are based on the rate or hazard, which is a quantity conditional on previous development of the event process (e.g. conditional on being event-free). Examples are the Cox model, Poisson models and Aalen's additive hazard model. Marginal models are based on the (cumulative) risk or probability, and include models for state occupation probabilities and restricted mean time lost. Marginal models do not involve conditioning on the past. These models make use of newer techniques like inverse probability weighting and pseudo-values for estimation. One of the authors is a leading expert in the field of pseudo-values and a separate chapter is devoted to the intuition behind and the use of pseudo-values. Given the recent discussion on the lack of interpretability of intensity based effect measures, the use and importance of marginal models is likely to increase. This is the first book to give an in-depth coverage of marginal models. The book devotes one chapter to intuition for each of intensity-based models and marginal models, and the models are illustrated by several examples. The book can be used by applied researchers as well as by those with a more theoretical interest. The more theoretical sections are clearly separated from the applied ones via asterisks. Software code to run the example is provided on a companion github page."" -Ronald Geskus, Associate Professor in Biostatistics, University of Oxford"
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