Applied Survival Analysis: Regression Modeling of Time to Event Data

Applied Survival Analysis: Regression Modeling of Time to Event Data

by David W. Hosmer Jr., Stanley Lemeshow, Susanne May
     
 

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THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION

Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical

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Overview

THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION

Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research.

This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data.

Features of the Second Edition include:

  • Expanded coverage of interactions and the covariate-adjusted survival functions
  • The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques
  • New discussion of variable selection with multivariable fractional polynomials
  • Further exploration of time-varying covariates, complex with examples
  • Additional treatment of the exponential, Weibull, and log-logistic parametric regression models
  • Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values
  • New examples and exercises at the end of each chapter

Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.

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Editorial Reviews

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...
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.
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.

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Product Details

ISBN-13:
9781118211588
Publisher:
Wiley
Publication date:
09/23/2011
Series:
Wiley Series in Probability and Statistics , #618
Sold by:
Barnes & Noble
Format:
NOOK Book
Pages:
416
File size:
13 MB
Note:
This product may take a few minutes to download.

Meet the Author

David W. Hosmer, PhD, is Professor Emeritus of Biostatistics in the School of Public Health and Heatlth Sciences at the University of Massachusetts Amherst. Dr. Hosmer is the coauthor of Applied Logistic Regression, published by Wiley.

Stanley Lemeshow, PhD, is Professor and Dean of the College of Public Health at The Ohio State University. Dr. Lemeshow has over thirty-five years of academic experience in the areas of regression, categorical data methods, and sampling methods. He is the coauthor of Sampling of Population: Methods and Application and Applied Logistic Regression, both published by Wiley.

Susanne May, PhD, is Assistant Professor of Biostatistics at the University of California, San Diego. Dr. May has over twelve years of experience in providing statistical support for health-related research projects.

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