dynamic regression models survival di martinussen torben (31 risultati)

Condizione: Fine. *Price HAS BEEN REDUCED by 10% until Monday, Oct. 6 (sale item)* 484 pp., hardcover, covers rubbed, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

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Condizione: New. pp. 488.

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Condizione: Good. [ No Hassle 30 Day Returns ][ Ships Daily ] [ Underlining/Highlighting: NONE ] [ Writing: NONE ] [ Edition: first ] Publisher: Springer Pub Date: 3/20/2006 Binding: Hardcover Pages: 484 first edition.

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Condizione: New. Series: Statistics for Biology and Health. BIC Classification: PBT. Dimension: 235 x 155. Weight in Grams: 1890. . 2006. 2006th Edition. hardcover. . . . .

Condizione: New. Series: Statistics for Biology and Health. BIC Classification: PBT. Dimension: 235 x 155. Weight in Grams: 1890. . 2006. 2006th Edition. hardcover. . . . . Books ship from the US and Ireland.

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Paperback. Condizione: new. Paperback. 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 Aalens 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Condizione: new. Hardcover. 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 Aalens 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

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Condizione: New. Series: Statistics for Biology and Health. Num Pages: 484 pages, 75 black & white illustrations, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 24. Weight in Grams: 736. . 2010. Softcover reprint of hardcover 1st ed. 2006. Paperback. . . . .

Condizione: New. Series: Statistics for Biology and Health. Num Pages: 484 pages, 75 black & white illustrations, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 24. Weight in Grams: 736. . 2010. Softcover reprint of hardcover 1st ed. 2006. Paperback. . . . . Books ship from the US and Ireland.

Paperback. Condizione: Brand New. 470 pages. 9.00x6.00x1.09 inches. In Stock.

Paperback. Condizione: new. Paperback. 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 Aalens 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condizione: new. Hardcover. 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 Aalens 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condizione: 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.