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The book

Short description of the content.

Competing risks and multistate models are an extension of standard survival analysis to more complex event histories. Survival analysis studies the time until some endpoint. The endpoint often combines different event types, like different causes of death or disease occurrence and death without prior disease, into one single endpoint. Competing risks distinguish between different endpoint types. Multistate models allow to model quite complex event patterns as transitions between different states over the course of time. These models have applications in fields such as medicine, biology, economics, demography, social science and reliability theory.Survival analysis is based on hazards. Hazard-based analyses are still available for competing risks and inhomogeneous Markov multistate models. However, the interpretation becomes more challenging, as there are as many hazards as there are possible transitions in the model.

This book explains hazard-based analyses of competing risks and multistate data with R. Special emphasis is placed on the interpretation of the results. A unique feature of this book is that readers are encouraged to simulate their own data based on the transition hazards only, which are the key quantities of the subsequent analyses. This simulation-based approach is supplemented with real data examples from studies in clinical medicine where the authors have been involved.

Competing risks are approached from a multistate perspective throughout. The multistate approach disposes of the difficulties that arose from the more classical competing risks model based on hypothetical risk-specific event times. The competing risks multistate model also serves as an intermediate link between single endpoint survival analysis and more general multistate models.

This book is aimed at data analysts with a background in standard survival analysis, who wish to understand, analyse and interpret more complex event histories with R. It is also suitable for graduate courses in biostatistics, statistics and epidemiological methods. The real data examples, R packages, and the entire R code used in the book are available on this website.

The authors are affiliated with the Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg
and the Freiburg Center for Data Analysis and Modelling, University of Freiburg, Germany. Jan Beyersmann is Senior Statistician and serves on the editorial board of Statistics in Medicine. Arthur Allignol is Statistician and one of the maintainers of the task view `Survival Analysis' at the Comprehensive R Archive Network. Martin Schumacher is Professor of Biostatistics and director of the Institute of Medical Biometry and Medical Informatics, Freiburg. He has been involved in theoretical developments as well as in practical applications of survival analyses and their extensions over many years.

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