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Dynamical Biostatistical Models : Chapman & Hall/CRC Biostatistics Series - Daniel Commenges

Dynamical Biostatistical Models

By: Daniel Commenges, Helene Jacqmin-Gadda

Paperback | 18 December 2020 | Edition Number 1

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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.

Industry Reviews

"The properties of this book may be summarized in two words: rich and concise. . . this is a very well-written book that manages to cover a lot of ground in a remarkably succinct way. . . I can highly recommend the book."
-Per Kragh Andersen, International Society for Clinical Biostatistics

"I think that those whose research is or will be in the area of dynamical biostatistics would benefit from having a copy on their shelves."
-Alice M. Richardson, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Australia

"This book aims at describing methods of biostatistics modeling, in particular, dynamical model approaches for statisticians, as well as serving as a textbook for postgraduate students... The balance between theory and application is appropriate for both researchers performing biostatistics modeling and for students taking graduate level courses... The book is concisely written so that it covers a wide range of basic and dynamic models and modeling approaches with examples. Statisticians in the industry may feel a large part of the book too technical but can use the book for reference and may also benefit from the rich examples and R-codes, some with translation to SAS, in the appendix."
-Pharmaceutical Statistics

"One of my favorite features of this book is that the same three examples are used throughout, and all the approaches discussed are applied to these examples. This allows readers to identify the similarities and differences of various techniques. Furthermore, it is a good way for readers to learn so-called data mining since diverse information can be mined by applying different statistical methods to the same dataset . . . I believe this book will be a successful text for graduate level courses focusing on dynamical biostatistical models that analyze time-dependent data. Its presentation of conventional methods is very effective."

~Hongjian Zhu,

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