Dynamic Regression Models for Survival Data

Torben Martinussen|Thomas H. Scheike

ISBN 10: 144191904X / ISBN 13: 9781441919045

Editore: Springer New York, 2010

Riguardo questo articolo

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.

Codice articolo 4172473

Dati bibliografici

• Titolo: Dynamic Regression Models for Survival Data
• Editore: Springer New York
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 144191904X
• ISBN 13: 9781441919045
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

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

Dalla quarta di copertina

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.

Torben Martinussen is at the Department of Natural Sciences at the Royal Veterinary and Agricultural University. He has a Ph.D. from University of Copenhagen and is associate editor of the Scandinavian Journal of Statistics. Thomas Scheike is at the Department of Biostatistics at University of Copenhagen. He has a Ph.D. from University of California at Berkeley and is Doctor of Science at the University of Copenhagen. He is the editor of the Scandinavian Journal of Statistics and associate editor of several other journals.