Praise for the Third Edition

“. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject.” —Statistics in Medical Research

Updated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, Fourth Edition continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences.

Emphasizing the latest research and providing the most up-to-date information regarding software applications in the field, Statistical Methods for Survival Data Analysis, Fourth Edition also includes:

• Marginal and random effect models for analyzing correlated censored or uncensored data
• Multiple types of two-sample and K-sample comparison analysis
• Updated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time models
• Expanded coverage of the Cox proportional hazards model
• Exercises at the end of each chapter to deepen knowledge of the presented material

Statistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis. The book is also an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.

ELISA T. LEE, PhD, is Regents Professor and George Lynn Cross Research Professor of Biostatistics and Epidemiology and Director of the Center for American Indian Health Research at the University of Oklahoma Health Sciences Center.

JOHN Wenyu WANG, PhD, is Professor of Research at the Center for American Indian Health Research at the University of Oklahoma Health Sciences Center.

Preface

Chapter 1 Introduction

1.1 Preliminaries

1.2 Censored Data

1.3 Scope of the Book

Chapter 2 Functions of Survival Time

2.1 Definitions

2.2 Relationships of the Survival Functions

Exercises

Chapter 3 Examples of Survival Data Analysis

3.1 Example 3.1: Comparison of Two Treatments and Three Diets

3.2 Example 3.2: Comparison of Two Survival Patterns Using Life Tables

3.3 Example 3.3: Fitting Survival Distributions to Tumor Free Times

3.4 Example 3.4: Comparing Survival of A Cohort With That Of A General population - Relative Survival

3.5 Example 3.5: Identification of Risk Factors For Incident Events

3.6 Example 3.6: Identification of Risk Factors for the Prevalence of Age-Related Macular Degeneration

3.7 Example 3.7: Identification of Significant Risk Factors For Incident Hypertension Using Related Data (Repeated Measurements) In A Longitudinal Study

Exercises

Chapter 4 Nonparametric Methods of Estimating Survival Functions

4.1 Product-Limit Estimates of Survivorship Function

4.2 Nelson-Aalen Estimates Of Survivorship Function

4.3 Life-Table Analysis

4.4 Relative Survival Rates

4.5 Standardized Rates and Ratios

Exercises

Chapter 5 Nonparametric Methods for Comparing Survival Distributions

5.1 Comparison of Two Survival Distributions

5.2 The Mantel and Haenszel Test

5.3 Comparison of K (K>2) Samples

Exercises

Chapter 6 Some Well-Known Parametric Survival Distribution and Their Applications

6.1 The Exponential Distribution

6.2 The Weibull Distribution

6.3 The Lognormal Distribution

6.4 Gamma, Generalized Gamma And Extended Generalized Gamma Distributions

6.5 The Log-Logistic Distribution

6.6 Other survival Distributions

Exercises

Chapter 7 Estimation Procedures for Parametric Survival Distributions Without Covariates

7.1 General Maximum Likelihood Estimation Procedures

7.2 The Exponential Distribution

7.3 The Weibull Distribution

7.4 The Lognormal Distribution

7.5 The Extended Generalized Gamma Distribution

7.6 The Log-Logistic Distribution

7.7 Gompertz Distribution

7.8 Graphical Methods

Exercises

Chapter 8 Tests of Goodness-of-Fit and Distribution Selection

8.1 Goodness-of-Fit Test Statistics Based on Asymptotic Likelihood Inference

8.2 Tests for Appropriateness of a Family of Distributions

8.3 Selection of A Family of Distributions By Using BIC or AIC Procedure

8.4 Tests For A Specific Distribution With Known Parameters

8.5 Hollander and Proschan’s Test for Appropriateness of a Given Distribution with Known Parameters

Exercises

Chapter 9 Parametric Methods for Comparing Two Survival Distributions

9.1 Log-Likelihood Ratio Test for Comparing Two Survival Distributions

9.2 Comparison of Two Exponential Distributions

9.3 Comparison of Two Weibull Distributions

9.4 Comparison of Two Gamma Distributions

Exercises

Chapter 10 Parametric Methods for Regression Model Fitting and Identification of Prognostic Factors

10.1 Preliminary Examination of Data

10.2 General Structure of Parametric Regression Models and Their Asymptotic Likelihood Inference

40.3 The Exponential AFT Model

10.4 The Weibull AFT Model

10.5 The Lognormal AFT Model

10.6 The Extended Generalized Gamma AFT Model

10.7 The Log-logistic AFT Model

10.8 Other Parametrical Regression Models

10.9 Model Selection Methods

Exercises

Chapter 11 Identification of Risk Factors Related to Survival Time: Cox Proportional Hazards Model

11.1 The Proportional Hazards Model

11.2 Partial Likelihood Function

11.3 Identification of Significant Covariates

11.4 Estimation of the Survivorship Function with Covariates

11.5 Adequacy assessment of the Proportional Hazards Model

Exercises

Chapter 12 Identification of Prognostic Factors Related to Survival Time: Non-Proportional Hazards Models

12.1 Models with Time-Dependent Covariates

12.2 Stratified Proportional Hazards Models

12.3 Competing Risks Model

12.4 Recurrent Event Models

12.5 Models for Related Observations

Exercises

Chapter 13 Identification of Risk Factors Related to Dichotomous and Polychotomous Outcomes

13.1 Univariate Analysis

13.2 Logistic, Conditional Logistic, and Other Regression Models For Dichotomous Outcomes

13.3 Models for Polychotomous Outcomes

13.4 Models for Related Observations

Exercises

Appendix Statistical Tables

References