Dynamic Regression Models for Survival Data

Name: Dynamic Regression Models for Survival Data
Brand: Springer-Verlag New York Inc.
SKU: 9780387202747
Price: 57295 HUF
Availability: InStock

Szerző: Torben Martinussen, Thomas Scheike

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer-Verlag New York Inc.

Elérhetőség: Beszállítói készleten

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In survival analysis there has long been a need for models that goes beyond the Cox model as the pro...

Információk a könyvről

Szerző

Torben Martinussen, Thomas Scheike

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2006

oldal

470

EAN

9780387202747

ISBN

0387202749

Enbook ID

01380902

Kiadó

Springer-Verlag New York Inc.

Súly

1890

Méretek

155 x 235 x 241

Teljes leírás

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 theexercises 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.

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Dynamic Regression Models for Survival Data

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