Applied Survival Analysis: Regression Modeling of Time to Event Data

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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 ...

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Overview

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.

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Editorial Reviews

From the Publisher
"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)

Technometrics
This is actually a great book to read. It has a wealth of examples and applications.
Technometrics
This is actually a great book to read. It has a wealth of examples and applications.
International Journal of Epidemiology
...the book is an ideal textbook for people with knowledge of regression analysis who want to become acquainted with the methods of survival analysis.
Statistical Methods in Medical Research
...highly recommended...
Booknews
A textbook for an introductory course in statistical methods for analyzing data typically encountered in health related studies that include events involving an element of time. Assumes previous courses in linear and logical regression. Emphasizes practical applications rather than mathematical theory, modeling data, and interpreting results. Also highlights the importance of incomplete or censored data and how that censoring may influence the selection of models and the interpretation of results. Mostly uses examples from STATA, but the methods are fairly ubiquitous among the currently available statistical software packages. Annotation c. by Book News, Inc., Portland, Or.
Doody's Review Service
Reviewer: Sharon M. Homan, PhD (Kansas Health Institute)
Description: Since the publication of the first edition of this book in 1999, there have been significant advances in statistical methods and software applications to analyzing survival data in medical, epidemiological, and other health-related research. The approach taken in this second edition is similar to that in the first edition, but it expands coverage of interactions and covariate-adjusted survival functions, variable selection, time-dependent covariates, parametric regression models, competing risk models, and missing data methods.
Purpose: The authors emphasize practical and contemporary applications of survival data regression modeling. Their goal is to "provide a focused text on regression modeling for the time-to-event data typically encountered in health related studies." Researchers and analysts increasingly work with time-to-event data, and have many more software options to perform analyses. Choosing among various modeling and software options is critical to developing a good analysis plan and conducting optimal analyses. This book is a great resource, with its clear and accessible presentation of modeling techniques, case studies, and practical applications.
Audience: It is intended as a textbook for graduate courses in biostatistics, statistics, and epidemiologic methods, and as a reference for practitioners and researchers in health-related fields who have a foundation in linear and logistic regression methods. The authors are highly regarded biostatisticians, with enormous expertise in the development, application, and teaching of survival data analysis methods.
Features: "The book begins by building a mathematically sound and practical foundation of survival data, survival time functions, rand egression modeling. Next, the proportional hazards model is developed - estimation, model fitting, and model adequacy. Extensions of the proportional hazards model include stratified models, time-varying covariates, truncation and censoring. The final chapters cover parametric modeling, recurrent event and frailty models, and competing risk models. One of the best features of this book is the presentation of well-developed applications and excellent examples and illustrations of analyses using Stata 9.0 and SAS 9.1. Challenging exercises end each chapter, but notes and/or solutions are not provided. Data sets can be downloaded from the publisher's web site. Adding the programming code for the examples and chapter exercises in an appendix on the web site would be even better. "
Assessment: This is a superb resource - a practical guide with up-to-date applications. The authors are excellent teachers of the mathematics and application of survival data regression modeling including how to handle complexities such as time-varying covariates and correlated observations. The second edition is a significant revision, incorporating the new capabilities of Stata, easy-to-use software with good graphical and statistical analysis capabilities, and introducing contemporary applications of regression modeling of time-to-event data.
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Product Details

  • ISBN-13: 9780471154105
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 1/28/1999
  • Series: Wiley Series in Probability and Statistics Series , #317
  • Edition description: Older Edition
  • Edition number: 1
  • Pages: 408
  • Product dimensions: 8.28 (w) x 7.96 (h) x 0.76 (d)

Meet the Author

DAVID W. HOSMER, Jr., PhD, and STANLEY LEMESHOW, PhD, are both professors of biostatistics in the Department of Biostatistics and Epidemiology of the University of Massachusetts School of Public Health and Health Sciences in Amherst, Massachusetts.
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Table of Contents

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.

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