Statistical Methods for Survival Data Analysis / Edition 3

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Defined in a broad sense, survival data refers to data that involve the remaining time until a certain event such as a death a relapse or the onset of a disease. Over the past decade, applications of the statistical methods for survival data analysis have extended far beyond biomedical and reliability research to a host of other fields, including criminology, sociology, marketing, and health insurance practice. Statistical Methods for Survival Data Analysis, Second Edition has been designed to meet the need for a single volume covering the many methodologies appropriate for the analysis of survival data. Emphasizing applications over rigorous mathematics, this extremely useful reference provides thorough discussion of the most commonly used parametric and nonparametric methods in survival data analysis, as well as guidelines for the planning and design of clinical trials. These statistical methods and guidelines are applicable to many types of research, including clinical investigations, epidemiologic studies, social science research, and studies in other areas. Numerous real-life examples are employed to illustrate key concepts and several large data sets are included as examples. Other features include discussion of clinical life-table analysis and population life-tables; a goodness-of-fit test for modeling survival data that involve censored observations; expanded treatment of Cox's proportional hazards model; discussion of the relationship between odds ratios and coefficients of the linear logistic regression model; discussion of computer programs for survival data analysis that generate the gamma probability plot; methods for determining sample sizes in clinical survival time trials; discussion of the issues of repeated significance testing and group; sequential design; and a reference list which includes a large number of recently published papers. Statistical Methods for Survival Data Analysis, Second Edition is an essential reference for biomedical inve
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Editorial Reviews

Defined in a broad sense, survival data refers to data that involve the remaining time until a certain event such as a death, a relapse, or the onset of a disease. Analysis of such data is applicable in such fields as criminology, sociology, marketing, and health insurance, as well as biomedical and reliability research. The many methodologies are presented here. Annotation c. Book News, Inc., Portland, OR (
From the Publisher
"…an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject." (Statistics in Medical Research, October 2005)

"The book is well written and provides explicit details of the models and methods used." (Journal of the American Statistical Association, June 2004)

"…contains thorough descriptions and illustrations of several useful nonparametric and parametric statistical methods to analyze survival data." (Journal of Statistical Computation & Simulation, May 2004) 

"...a comprehensive and clearly written textbook...a very good reference..." (Short Book Reviews, Vol 24(1), 2004)

"...continues to be application-oriented with a minimum level of mathematics..." (Zentralblatt Math, Vol 1026, 2004)

"This third edition is a significant upgrade...this remains an excellent choice or a basic textbook in survival analysis..." (Technometrics, Vol. 45, No. 4, November 2003)

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Product Details

  • ISBN-13: 9780471369974
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 4/4/2003
  • Series: Wiley Series in Probability and Statistics Series, #364
  • Edition description: REV
  • Edition number: 3
  • Pages: 534
  • Product dimensions: 6.44 (w) x 9.29 (h) x 1.29 (d)

Meet the Author

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.

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Table of Contents

1 Introduction
2 Functions of Survival Time 8
3 Examples of Survival Data Analysis 19
4 Nonparametric Methods of Estimating Survival Functions 64
5 Nonparametric Methods for Comparing Survival Distributions 106
6 Some Well-Known Parametric Survival Distributions and Their Applications 134
7 Estimation Procedures for Parametric Survival Distributions without Covariates 162
8 Graphical Methods for Survival Distribution Fitting 198
9 Tests of Goodness of Fit and Distribution Selection 221
10 Parametric Methods for Comparing Two Survival Distributions 243
11 Parametric Methods for Regression Model Fitting and Identification of Prognostic Factors 256
12 Identification of Prognostic Factors Related to Survival Time: Cox Proportional Hazards Model 298
13 Identification of Prognostic Factors Related to Survival Time: Nonproportional Hazards Models 339
14 Identification of Risk Factors Related to Dichotomous and Polychotomous Outcomes 377
App. A Newton-Raphson Method 428
App. B Statistical Tables 433
References 488
Index 511
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