Dynamic Regression Models for Survival Data by Torben MartinussenDynamic Regression Models for Survival Data by Torben Martinussen

Dynamic Regression Models for Survival Data

byTorben Martinussen, Thomas H. Scheike

Paperback | November 23, 2010

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This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the aim of describing time-varying effects of explanatory variables. Use of the suggested models and methods is illustrated on real data examples, using the R-package timereg developed by the authors, which is applied throughout the book with worked examples for the data sets.
Title:Dynamic Regression Models for Survival DataFormat:PaperbackDimensions:484 pages, 9.25 × 6.1 × 0.03 inPublished:November 23, 2010Publisher:Springer New YorkLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:144191904X

ISBN - 13:9781441919045

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Table of Contents

Introduction.- Probabilistic background.- Estimation for filtered counting process data.- Nonparametric procedures for survival data.- Additive hazards models.- Multiplicative hazards models.- Multiplicative-additive hazards models.- Accelerated failure time and transformation models.- Clustered failure time data.- Competing risks model.- Marked point process models.

Editorial Reviews

From the reviews:"This book is a welcome addition to the literature on survival analysis for several reasons. The coverage of both multiplicative and, especially, additive models with time-varying covariates is well beyond that found in other books. There is also more emphasis on model checking than in most books. . the book is enjoyable to read. . This book is an important resource for anyone with an interest in survival or event history analysis." (J. F. Lawless, Short Book Reviews, Vol. 26 (2), 2006)"'Dynamic regression models' . are able to capture time-varying dynamics of covariate effects. . this book provides a timely summary of the results for topics which are important to practical applications. The readers who are interested in further research in these areas will find the detailed derivations of mathematical results helpful. . The rich exercises at the end of each chapter make this book an excellent choice as a textbook for an advanced survival analysis course." (Dongsheng Tu, Zentrablatt MATH, Vol. 1096 (22), 2006)"Survival data analysis has been a very active research field for several decades. An important contribution that stimulated the entire field was the counting process formulation . . that is also used in this monograph. . There are exercises at the end of each chapter . . The practical aspects of survival analysis are illustrated with a set of worked out examples using R. . The book is primarily aimed at the biostatistical community . . It is well written . ." (Rainer Schlittgen, Statistical Papers, Vol. 48 (3), 2007)"The book under review is a welcome addition to existing excellent books on survival analysis . . It should be a useful reference to both applied as well as theoretical bio-statisticians. Perhaps it could also be used as a text for a graduate level course in survival analysis." (Subhash C. Kochar, Mathematical Reviews, Issue 2007 b)"This book is aimed at advanced graduate students and statistical researchers in statistics/biostatistics departments. . The inspiration and influence of Andersen et al. (1993) on the presentation style, terminology, and approach to the subject are very visible in many parts of the book. . In summary, this book definitely deserves a place in the collection of any serious survival analyst. It is also recommended to theoretically sound data analysts interested in dynamic and semiparametric survival models beyond the class of multiplicative models." (Debajyoti Sinha, Journal of the American Statistical Association, Vol. 102 (480), 2007)