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![]( "Buchcover ISBN 9780471681502")
„This is actually a great book to read. It has a wealth of examplesand applications.“ (Technometrics, February, 2001)
... the book is an ideal textbook for people with knowledge ofregression analysis who want to become acquainted with the methodsof survival analysis. (International Journal of Epidemiology, Volume 30 No 2 2001)
„... highly recommended...“ (Statistical Methods in MedicalResearch, August 1999)
„... the goal of this book is to provide a focused text onregression modeling for the time to event data typicallyencountered in health related studies... a good description of itscontents.“ (Zentralblatt MATH, Vol. 966, 2001/16)
A Practical, Up-To-Date Guide To Modern Methods In The Analysis OfTime To Event Data.
The rapid proliferation of powerful and affordable statisticalsoftware packages over the past decade has inspired the developmentof an array of valuable new methods for analyzing survival timedata. Yet there continues to be a paucity of statistical modelingguides geared to the concerns of health-related researchers whostudy time to event data. This book helps bridge this important gapin the literature.
Applied Survival Analysis is a comprehensive introduction toregression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike othertexts on the subject, it focuses almost exclusively on practicalapplications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplementedwith real-world examples and case studies. While the authorsemphasize the proportional hazards model, descriptive methods andparametric models are also considered in some detail. Key topicscovered in depth include:
* Variable selection.
* Identification of the scale of continuous covariates.
* The role of interactions in the model.
* Interpretation of a fitted model.
* Assessment of fit and model assumptions.
* Regression diagnostics.
* Recurrent event models, frailty models, and additivemodels.
* Commercially available statistical software and getting the mostout of it.
Applied Survival Analysis is an ideal introduction for graduatestudents in biostatistics and epidemiology, as well as researchersin health-related fields.
The rapid proliferation of powerful and affordable statisticalsoftware packages over the past decade has inspired the developmentof an array of valuable new methods for analyzing survival timedata. Yet there continues to be a paucity of statistical modelingguides geared to the concerns of health-related researchers whostudy time to event data. This book helps bridge this important gapin the literature.
Applied Survival Analysis is a comprehensive introduction toregression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike othertexts on the subject, it focuses almost exclusively on practicalapplications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplementedwith real-world examples and case studies. While the authorsemphasize the proportional hazards model, descriptive methods andparametric models are also considered in some detail. Key topicscovered in depth include:
* Variable selection.
* Identification of the scale of continuous covariates.
* The role of interactions in the model.
* Interpretation of a fitted model.
* Assessment of fit and model assumptions.
* Regression diagnostics.
* Recurrent event models, frailty models, and additivemodels.
* Commercially available statistical software and getting the mostout of it.
Applied Survival Analysis is an ideal introduction for graduatestudents in biostatistics and epidemiology, as well as researchersin health-related fields.
