David W. Hosmer & Stanley Lemeshow 
Applied Survival Analysis 
Regression Modeling of Time-to-Event Data

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.

Содержание

Preface xi

1. Introduction to Regression Modeling of Survival Data 1

2. Descriptive Methods for Survival Data 16

3. Regression Models for Survival Data 67

4. Interpretation of a Fitted Proportional Hazards Regression Model 92

5. Model Development 132

6. Assessment of Model Adequacy 169

7. Extensions of the Proportional Hazards Model 207

8. Parametric Regression Models 244

9. Other Models and Topics 286

Appendix 1: The Delta Method 355

Appendix 2: An Introduction to the Counting Process Approach to Survival Analysis 359

Appendix 3: Percentiles for Computation of the Hall and Wellner Confidence Band 364

References 365

Index 383

Об авторе

David W. Hosmer, Ph D, 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, Ph D, 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, Ph D, 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.