Dynamic Regression Models for Survival Data - Couverture souple

Martinussen, Torben; Scheike, Thomas H.

9781441919045: Dynamic Regression Models for Survival Data

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ISBN 10 : 144191904X ISBN 13 : 9781441919045

Editeur : Springer, 2010

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This book shows the usefulness of counting processes and marked poit processes when dealing with non-parametric and semi-parametric statistical problems.

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Revue de presse

"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

"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

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�diteurSpringer
Date d'�dition2010
ISBN 10 144191904X
ISBN 13 9781441919045
ReliureBroch�
Langueanglais
Nombre de pages484
Coordonn�es du fabricantnon disponible

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9780387202747: Dynamic Regression Models for Survival Data

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Editeur : Springer-Verlag New York Inc., 2006
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Dynamic Regression Models for Survival Data

Torben Martinussen|Thomas H. Scheike

Edit� par Springer New York, 2010

ISBN 10 : 144191904X ISBN 13 : 9781441919045

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A key issue in this book is extensions of the Cox model and alternative models with most of them having the specific aim to be able to deal with time-varying effects of covariates in regression analysisThis book studies and applies modern flexible re. N� de r�f. du vendeur 4172473

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Dynamic Regression Models for Survival Data

Thomas H. Scheike

Edit� par Springer New York Nov 2010, 2010

ISBN 10 : 144191904X ISBN 13 : 9781441919045

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 484 pp. Englisch. N� de r�f. du vendeur 9781441919045

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Dynamic Regression Models for Survival Data

Thomas H. Scheike

Edit� par Springer New York, 2010

ISBN 10 : 144191904X ISBN 13 : 9781441919045

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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N� de r�f. du vendeur 9781441919045

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Dynamic Regression Models for Survival Data

Thomas H. Scheike

Edit� par Springer New York, Springer New York Nov 2010, 2010

ISBN 10 : 144191904X ISBN 13 : 9781441919045

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 484 pp. Englisch. N� de r�f. du vendeur 9781441919045

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