MULTI-STATE SURVIVAL DATA

Multi-state models are used to study longitudinal data on subjects, focusing on the occurrence of events over time. This book is aimed at biostatisticians and readers with an applied background, including epidemiology, and covers intensity-based and marginal models, survival data, competing risks, illness-death models, recurrent events, and pseudo-values. It includes practical examples and software code.

Format: Hardback
Publication date: 08 March 2022
Publisher: TAYLOR & FRANCIS

When analyzing longitudinal data on subjects, multi-state models offer a statistical framework that is particularly useful for examining the occurrence of events that the subjects may experience over time. These models find wide-ranging applications in fields such as biostatistics, medicine, and public health. The book provides comprehensive coverage of the subject matter, including mathematical details that can be selectively skipped by readers who are more interested in practical examples. It is designed to appeal to biostatisticians as well as readers with a background in applied fields like epidemiology who are interested in gaining a deeper understanding of the topic.

The book is structured into several chapters, each focusing on different aspects of multi-state models. The first chapter introduces the basic concepts and theoretical foundations of the models, while subsequent chapters delve into more advanced topics such as intensity-based and marginal models, survival data analysis, competing risks, illness-death models, and recurrent events. A full chapter is dedicated to pseudo-values, which are important in certain applications. Intuitive introductions are provided to facilitate a smooth understanding of the mathematical concepts, while detailed mathematical explanations are also included to cater to readers with a strong mathematical background.

Practical examples of event history data are provided throughout the book to illustrate the application of the models. These examples cover various real-world scenarios and help readers to understand how multi-state models can be used to analyze and interpret data. Each example includes software code in R and SAS, as well as the data used in the book, allowing readers to follow along and replicate the analyses. Additionally, solutions to the exercises are provided, allowing readers to test their understanding and apply the concepts learned in the book.

Overall, this book is an invaluable resource for anyone interested in learning and applying multi-state models in biostatistics, medicine, and public health. Its comprehensive coverage, intuitive explanations, and practical examples make it accessible to a wide range of readers, from beginners to advanced practitioners. Whether you are working with longitudinal data or interested in exploring the theoretical foundations of multi-state models, this book will provide you with the knowledge and tools you need to succeed in your research endeavors.

ISBN-13: 9780367140021