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MORE ABOUT THIS BOOK
Main description:
Clustered survival data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. Frailty models provide a powerful tool to analyse clustered survival data. In contrast to the large number of research publications on frailty models, relatively few statistical software packages contain frailty models.
It is demanding for statistical practitioners and graduate students to grasp a good knowledge on frailty models from the existing literature. This book provides an in-depth discussion and explanation of the basics of frailty model methodology for such readers. The discussion includes parametric and semiparametric frailty models and accelerated failure time models. Common techniques to fit frailty models include the EM-algorithm, penalised likelihood techniques, Laplacian integration and Bayesian techniques. More advanced frailty models for hierarchical data are also included.
Real-life examples are used to demonstrate how particular frailty models can be fitted and how the results should be interpreted. The programs to fit all the worked-out examples in the book are available from the Springer website with most of the programs developed in the freeware packages R and Winbugs. The book starts with a brief overview of some basic concepts in classical survival analysis, collecting what is needed for the reading on the more complex frailty models.
Feature:
Introduces Frailty Models from a basic level that is missing in other books
Back cover:
Clustered survival data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. Frailty models provide a powerful tool to analyse clustered survival data. In contrast to the large number of research publications on frailty models, relatively few statistical software packages contain frailty models.
It is demanding for statistical practitioners and graduate students to grasp a good knowledge on frailty models from the existing literature. This book provides an in-depth discussion and explanation of the basics of frailty model methodology for such readers. The discussion includes parametric and semiparametric frailty models and accelerated failure time models. Common techniques to fit frailty models include the EM-algorithm, penalised likelihood techniques, Laplacian integration and Bayesian techniques. More advanced frailty models for hierarchical data are also included.
Real-life examples are used to demonstrate how particular frailty models can be fitted and how the results should be interpreted. The programs to fit all the worked-out examples in the book are available from the Springer website with most of the programs developed in the freeware packages R and Winbugs. The book starts with a brief overview of some basic concepts in classical survival analysis, collecting what is needed for the reading on the more complex frailty models.
Luc Duchateau is Associate Professor of Statistics at the Faculty of Veterinary Medicine of the Ghent University, Belgium. He is board member of the Quetelet Society (Belgian Region of the International Biometric Society) and of the International Biometric Society Channel Network. He has collaborated extensively with physicians in oncology and allergy, public health workers and veterinarians, and is an author of numerous papers in statistical, medical and veterinarian journals.
Paul Janssen is Professor of Statistics at the Centre for Statistics of the Hasselt University, Diepenbeek, Belgium. He is an elected member of the International Statistical Institute. He spent research visits at the Johns Hopkins University (Baltimore, USA) and the University of Washington (Seattle, USA). His research interests include survival analysis, nonparametric estimation, resampling techniques and asymptotic theory.
Contents:
Parametric proportional hazards models with gamma frailty.- Alternatives for the frailty model.- Frailty distributions.- The semiparametric frailty model.- Multifrailty and multilevel models.- Extensions of the frailty model.
PRODUCT DETAILS
Publisher: Springer (Springer New York)
Publication date: December, 2007
Pages: 318
Weight: 666g
Availability: Not available (reason unspecified)
Subcategories: Epidemiology, Infectious Diseases, Oncology
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CUSTOMER REVIEWS
From the reviews:
"The book by Duchateau and Jansen is generally easy to follow. The book starts with introduction to the most popular parametric and semiparametric survival models. … this book can be recommended also for undergraduate students in statistics. … the book contains several further extensions of frailty models such as multifrailty and multilevel models with references. Therefore it is valuable also for researchers in survival analysis." (Tommi Härkänen, International Statistical Review, Vol. 76 (3), 2008)
"This book focuses on frailty models and provides an in-depth discussion of the basics of frailty model methodology using numerous real data sets. … The book is well structured and covers very nicely the material for frailty models. … The book is directed towards statistical practitioners and graduate students but it may be useful to a broad interdisciplinary readership of researchers and practitioners in applied statistics, biomedicine and biostatistics. … a reference book for a one-semester applied course in survival analysis focusing on frailties." (Filia Vonta, Journal of Applied Statistics, Vol. 36 (6), August, 2009)
"This book studies so-called frailty models intended for time-to-event data with a cluster structure. … provide a thorough presentation of the most current techniques used in this area of time-to-event analysis with emphasis on analysis of real data sets. The book is intended for students and applied statisticians. … this book gives a good description of frailty models. It is well written and its many real applications and the availability of computer code make it a valuable resource for the applied statistician … ." (Torben Martinussen, Biometrical Journal, Vol. 51 (3), 2009)