Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software.

The book describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference.

Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is applied to real data and how to interpret the results.

About the Author

Daniel Commenges is emeritus research director at INSERM and founder of the Biostatistics Team at the University of Bordeaux. Dr. Commenges has published more than 200 papers and was editor of Biometrics and an associate editor of several other journals. His main research interests focus on statistical models in epidemiology and biology, applications of stochastic processes, statistical inference in dynamical models, and model selection.

H�l�ne Jacqmin-Gadda is research director at INSERM and head of the Biostatistics Team at the University of Bordeaux. Dr. Jacqmin-Gadda is a member of the International Biometrics Society and was an associate editor of Biometrics. Her research involves methods for analyzing longitudinal data and joint models in areas, including brain aging, HIV, and cancer.

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