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Applied Survival Analysis
Regression Modeling of Time to Event Data
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Dieser Text ist das auf etwas niedrigerem fachlichen Niveau gehaltene Begleitbuch zu "Applied Logistic Regression" vom gleichen Autor. Eine Einführung in die Analyse von Datensätzen zeitkorrelierter Ereignisse, mit dem Schwerpunkt auf Aspekten des Gesundheitswesens, der Modellierung von Daten und der Interpretation von Ergebnissen. Insbesondere für angewandte Biostatistiker konzipiert, ist der Text verständlich und anschaulich gehalten, d.h. es wird verstärkt auf Methoden eingegangen und weniger Wert gelegt auf mathematische Theorie. (01/99)A Practical, Up-To-Date Guide To Modern Methods ...
Dieser Text ist das auf etwas niedrigerem fachlichen Niveau gehaltene Begleitbuch zu "Applied Logistic Regression" vom gleichen Autor. Eine Einführung in die Analyse von Datensätzen zeitkorrelierter Ereignisse, mit dem Schwerpunkt auf Aspekten des Gesundheitswesens, der Modellierung von Daten und der Interpretation von Ergebnissen. Insbesondere für angewandte Biostatistiker konzipiert, ist der Text verständlich und anschaulich gehalten, d.h. es wird verstärkt auf Methoden eingegangen und weniger Wert gelegt auf mathematische Theorie. (01/99)
A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data.
The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature.
Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered 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 additive models.
* Commercially available statistical software and getting the most out of it.
Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields.
"This is actually a great book to read. It has a wealth of examples and applications." (Technometrics, February, 2001)
...the book is an ideal textbook for people with knowledge of regression analysis who want to become acquainted with the methods of survival analysis. (International Journal of Epidemiology, Volume 30 No 2 2001)
"...highly recommended..." (Statistical Methods in Medical Research, August 1999)
"...the goal of this book is to provide a focused text on regression modeling for the time to event data typically encountered in health related studies...a good description of its contents." (Zentralblatt MATH, Vol. 966, 2001/16)
Introduction to Regression Modeling of Survival Data.
Descriptive Methods for Survival Data.
Regression Models for Survival Data.
Interpretation of a Fitted Proportional Hazards Regression Model.
Model Development.
Assessment of Model Adequacy.
Extensions of the Proportional Hazards Model.
Parametric Regression Models.
Other Models and Topics.
Appendices.
References.
Index.
A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data.
The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature.
Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered 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 additive models.
* Commercially available statistical software and getting the most out of it.
Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields.
"This is actually a great book to read. It has a wealth of examples and applications." (Technometrics, February, 2001)
...the book is an ideal textbook for people with knowledge of regression analysis who want to become acquainted with the methods of survival analysis. (International Journal of Epidemiology, Volume 30 No 2 2001)
"...highly recommended..." (Statistical Methods in Medical Research, August 1999)
"...the goal of this book is to provide a focused text on regression modeling for the time to event data typically encountered in health related studies...a good description of its contents." (Zentralblatt MATH, Vol. 966, 2001/16)
Introduction to Regression Modeling of Survival Data.
Descriptive Methods for Survival Data.
Regression Models for Survival Data.
Interpretation of a Fitted Proportional Hazards Regression Model.
Model Development.
Assessment of Model Adequacy.
Extensions of the Proportional Hazards Model.
Parametric Regression Models.
Other Models and Topics.
Appendices.
References.
Index.