Update cookies preferences

E-book: Dynamic Regression Models for Survival Data

Other books in subject:
  • Format - PDF+DRM
  • Price: 159,93 €*
  • * the price is final i.e. no additional discount will apply
  • Add to basket
  • Add to Wishlist
  • This ebook is for personal use only. E-Books are non-refundable.
Other books in subject:

DRM restrictions

  • Copying (copy/paste):

    not allowed

  • Printing:

    not allowed

  • Usage:

    Digital Rights Management (DRM)
    The publisher has supplied this book in encrypted form, which means that you need to install free software in order to unlock and read it.  To read this e-book you have to create Adobe ID More info here. Ebook can be read and downloaded up to 6 devices (single user with the same Adobe ID).

    Required software
    To read this ebook on a mobile device (phone or tablet) you'll need to install this free app: PocketBook Reader (iOS / Android)

    To download and read this eBook on a PC or Mac you need Adobe Digital Editions (This is a free app specially developed for eBooks. It's not the same as Adobe Reader, which you probably already have on your computer.)

    You can't read this ebook with Amazon Kindle

In survival analysis there has long been a need for models that goes beyond the Cox model as the proportional hazards assumption often fails in practice. 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 specific aim of describing time-varying effects of explanatory variables. One model that receives special attention is Aalen's additive hazards model that is particularly well suited for dealing with time-varying effects. The book covers the use of residuals and resampling techniques to assess the fit of the models and also points out how the suggested models can be utilised for clustered survival data. The authors demonstrate the practically important aspect of how to do hypothesis testing of time-varying effects making backwards model selection strategies possible for the flexible models considered.The use of the suggested models and methods is illustrated on real data examples. The methods are available in the R-package timereg developed by the authors, which is applied throughout the book with worked examples for the data sets. This gives the reader a unique chance of obtaining hands-on experience.This book is well suited for statistical consultants as well as for those who would like to see more about the theoretical justification of the suggested procedures. It can be used as a textbook for a graduate/master course in survival analysis, and students will appreciate the exercises included after each chapter. The applied side of the book with many worked examples accompanied with R-code shows in detail how one can analyse real data and at the same time gives a deeper understanding of the underlying theory.

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.

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 isaimed 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)

Preface vii
Introduction
1(16)
Survival data
1(13)
Longitudinal data
14(3)
Probabilistic background
17(32)
Preliminaries
17(3)
Martingales
20(3)
Counting processes
23(7)
Marked point processes
30(4)
Large-sample results
34(10)
Exercises
44(5)
Estimation for filtered counting process data
49(32)
Filtered counting process data
49(13)
Likelihood constructions
62(8)
Estimating equations
70(4)
Exercises
74(7)
Nonparametric procedures for survival data
81(22)
The Kaplan-Meier estimator
81(5)
Hypothesis testing
86(9)
Comparisons of groups of survival data
86(7)
Stratified tests
93(2)
Exercises
95(8)
Additive Hazards Models
103(72)
Additive hazards models
108(8)
Inference for additive hazards models
116(10)
Semiparametric additive hazards models
126(9)
Inference for the semiparametric hazards model
135(11)
Estimating the survival function
146(3)
Additive rate models
149(2)
Goodness-of-fit procedures
151(8)
Example
159(6)
Exercises
165(10)
Multiplicative hazards models
175(74)
The Cox model
181(12)
Goodness-of-fit procedures for the Cox model
193(12)
Extended Cox model with time-varying regression effects
205(8)
Inference for the extended Cox model
213(5)
A semiparametric multiplicative hazards model
218(6)
Inference for the semiparametric multiplicative model
224(2)
Estimating the survival function
226(1)
Multiplicative rate models
227(1)
Goodness-of-fit procedures
228(6)
Examples
234(6)
Exercises
240(9)
Multiplicative-Additive hazards models
249(44)
The Cox-Aalen hazards model
251(22)
Model and estimation
252(3)
Inference and large sample properties
255(5)
Goodness-of-fit procedures
260(6)
Estimating the survival function
266(4)
Example
270(3)
Proportional excess hazards model
273(17)
Model and score equations
274(2)
Estimation and inference
276(4)
Efficient estimation
280(3)
Goodness-of-fit procedures
283(1)
Examples
284(6)
Exercises
290(3)
Accelerated failure time and transformation models
293(20)
The accelerated failure time model
294(4)
The semiparametric transformation model
298(11)
Exercises
309(4)
Clustered failure time data
313(34)
Marginal regression models for clustered failure time data
314(20)
Working independence assumption
315(12)
Two-stage estimation of correlation
327(3)
One-stage estimation of correlation
330(4)
Frailty models
334(4)
Exercises
338(9)
Competing Risks Model
347(28)
Product limit estimator
351(5)
Cause specific hazards modeling
356(5)
Subdistribution approach
361(9)
Exercises
370(5)
Marked point process models
375(36)
Nonparametric additive model for longitudinal data
380(9)
Semiparametric additive model for longitudinal data
389(4)
Efficient estimation
393(4)
Marginal models
397(11)
Exercises
408(3)
Khmaladze's transformation 411(4)
Matrix derivatives 415(2)
The Timereg survival package for R 417(36)
Bibliography 453(14)
Index 467