Overview

Praise for the Third Edition . . . an easy-to read introduction to survival analysiswhich covers the major concepts and techniques of thesubject. --Statistics in Medical Research Updated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, FourthEdition continues to deliver a comprehensive introduction tothe most commonly-used methods for analyzing survival data.Authored by a uniquely well-qualified author team, the FourthEdition is a critically acclaimed guide to statistical methods withapplications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examplesto illustrate applications within these various fields, althoughspecial consideration is given to the study of survival data inbiomedical sciences. Emphasizing the latest research and providing the mostup-to-date information regarding software applications in thefield, Statistical Methods for Survival Data Analysis, FourthEdition also includes: Marginal and random effect models for analyzing correlatedcensored or uncensored data Multiple types of two-sample and K-sample comparisonanalysis Updated treatment of parametric methods for regression modelfitting 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 thepresented material Statistical Methods for Survival Data Analysis is anideal text for upper-undergraduate and graduate-level courses onsurvival data analysis. The book is also an excellent resource forbiomedical investigators, statisticians, and epidemiologists, aswell as researchers in every field in which the analysis ofsurvival data plays a role.

Full Product Details

Author:   Elisa T. Lee (University of Oklahoma Health Sciences Center) ,  John Wenyu Wang
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
Edition:   4th Revised edition
ISBN:  

9781118593110

ISBN 10:   1118593111
Pages:   512
Publication Date:   19 September 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Electronic book text
Publisher's Status:   Active
Availability:   Available To Order  
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Table of Contents

Preface xi 1 Introduction 1 1.1 Preliminaries 1 1.2 Censored Data 2 1.3 Scope of the Book 5 2 Functions of Survival Time 8 2.1 Definitions 8 2.2 Relationships of the Survival Functions 15 Exercises 16 3 Examples of Survival Data Analysis 19 3.1 Example 3.1: Comparison of Two Treatments and Three Diets 19 3.2 Example 3.2: Comparison of Two Survival Patterns Using Life Tables 26 3.3 Example 3.3: Fitting Survival Distributions to Tumor-Free Times 28 3.4 Example 3.4: Comparing Survival of a Cohort with that of a General Population - Relative Survival 30 3.5 Example 3.5: Identification of Risk Factors for Incident Events 33 3.6 Example 3.6: Identification of Risk Factors for the Prevalence of Age-Related Macular Degeneration 38 3.7 Example 3.7: Identification of Significant Risk Factors for Incident Hypertension Using Related Data (Repeated Measurements) in a Longitudinal Study 46 Exercises 54 4 Nonparametric Methods of Estimating Survival Functions 68 4.1 Product-Limit Estimates of Survivorship Function 69 4.2 N elson-Aalen Estimates of Survivorship Function 82 4.3 Life-Table Analysis 83 4.4 Relative Survival Rates 96 4.5 Standardized Rates and Ratios 98 Exercises 104 5 Nonparametric Methods for Comparing Survival Distributions 108 5.1 Comparison of Two Survival Distributions 108 5.2 The Mantel and Haenszel Test 123 5.3 Comparison of K (K > 2) Samples 128 Exercises 130 6 Some Well-Known Parametric Survival Distributions And Their Applications 133 6.1 Exponential Distribution 133 6.2 Weibull Distribution 138 6.3 Lognormal Distribution 143 6.4 Gamma, Generalized Gamma, and Extended Generalized Gamma Distributions 148 6.5 Log-Logistic Distribution 153 6.6 O ther Survival Distributions 155 Exercises 159 7 Estimation Procedures for Parametric Survival Distributions Without Covariates 161 7.1 General Maximum Likelihood Estimation Procedure 161 7.2 Exponential Distribution 165 7.3 Weibull Distribution 178 7.4 Lognormal Distribution 180 7.5 The Extended Generalized Gamma Distribution 183 7.6 The Log-Logistic Distribution 184 7.7 Gompertz Distribution 185 7.8 Graphical Methods 186 Exercises 203 8 Tests of Goodness-of-Fit and Distribution Selection 206 8.1 Goodness-of-Fit Test Statistics Based on Asymptotic Likelihood Inferences 207 8.2 Tests for Appropriateness of a Family of Distributions 210 8.3 Selection of a Distribution by Using BIC or AIC Procedure 216 8.4 Tests for a Specific Distribution with Known Parameters 217 8.5 Hollander and Proschan's Test for Appropriateness of a Given Distribution with Known Parameters 220 Exercises 224 9 Parametric Methods for Comparing Two Survival Distributions 226 9.1 Log-Likelihood Ratio Test for Comparing Two Survival Distributions 226 9.2 Comparison of Two Exponential Distributions 229 9.3 Comparison of Two Weibull Distributions 234 9.4 Comparison of Two Gamma Distributions 236 Exercises 237 10 Parametric Methods for Regression Model Fitting and Identification of Prognostic Factors 239 10.1 Preliminary Examination of Data 240 10.2 General Structure of Parametric Regression Models and Their Asymptotic Likelihood Inference 242 10.3 Exponential AFT Model 246 10.4 Weibull AFT Model 255 10.5 Lognormal AFT Model 258 10.6 The Extended Generalized Gamma AFT Model 261 10.7 Log-Logistic AFT Model 264 10.8 O ther Parametric Regression Models 268 10.9 Model Selection Methods 270 Exercises 279 11 Identification of Risk Factors Related to Survival Time: Cox Proportional Hazards Model 282 11.1 The Proportional Hazards Model 282 11.2 The Partial Likelihood Function 285 11.3 Identification of Significant Covariates 302 11.4 Estimation of the Survivorship Function with Covariates 309 11.5 Adequacy Assessment of the Proportional Hazards Model 317 Exercises 334 12 Identification of Prognostic Factors Related to Survival Time: Non-Proportional Hazards Models 337 12.1 Models with Time-Dependent Covariates 337 12.2 Stratified Proportional Hazards Model 346 12.3 Competing Risks Model 350 12.4 Recurrent Event Models 353 12.5 Models for Related Observations 370 Exercises 382 13 Identification of Risk Factors Related to Dichotomous and Polychotomous Outcomes 384 13.1 Univariate Analysis 385 13.2 Logistic and Conditional Logistic Regression Model for Dichotomous Outcomes 392 13.3 Models for Polychotomous Outcomes 421 13.4 Models for Related Observations 432 Exercises 440 Appendix 443 References 466 Index 477

Reviews

In summary, this book continues to improve, and the fourth edition is a welcome addition to the available books on survival analysis. The expanded sections on modelling and the addition of R software examples are particularly helpful. (International Statistical Review, 1 October 2015)

-In summary, this book continues to improve, and the fourth edition is a welcome addition to the available books on survival analysis. The expanded sections on modelling and the addition of R software examples are particularly helpful.- (International Statistical Review, 1 October 2015) In summary, this book continues to improve, and the fourth edition is a welcome addition to the available books on survival analysis. The expanded sections on modelling and the addition of R software examples are particularly helpful. (International Statistical Review, 1 October 2015) In summary, this book continues to improve, and the fourth edition is a welcome addition to the available books on survival analysis. The expanded sections on modelling and the addition of R software examples are particularly helpful. ( International Statistical Review, 1 October 2015)

Author Information

ELISA T. LEE, PhD, is Regents Professor and George LynnCross Research Professor of Biostatistics and Epidemiology andDirector of the Center for American Indian Health Research at theUniversity of Oklahoma Health Sciences Center. JOHN Wenyu WANG, PhD, is Professor of Research at theCenter for American Indian Health Research at the University ofOklahoma Health Sciences Center.

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