Statistical Methods for Survival Data Analysis / Edition 3

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Overview

Third Edition brings the text up to date with new material and updated references.
  • New content includes an introduction to left and interval censored data; the log-logistic distribution; estimation procedures for left and interval censored data; parametric methods iwth covariates; Cox's proportional hazards model (including stratification and time-dependent covariates); and multiple responses to the logistic regression model.
  • Coverage of graphical methods has been deleted.
  • Large data sets are provided on an FTP site for readers' convenience.
  • Bibliographic remarks conclude each chapter.
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Editorial Reviews

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)

Booknews
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 (booknews.com)
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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 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. She received a master’s degree from the University of California at Berkeley and her doctorate from New York University. The author of the previous editions of Statistical Methods for Survival Data Analysis, Professor Lee is a Fellow of the American Statistical Association and member of the Society for Epidemiological Research and the American Diabetes Association.

JOHN WENYU WANG, PhD, is an Associate Professor of Biostatistics at the University of Oklahoma Health Sciences Center. He received a master’s degree from the Academy of Sciences of China and his doctorate from the University of Maryland.

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Table of Contents

Preface.

Introduction.

Functions of Survival Time.

Examples of Survival Data Analysis.

Nonparametric Methods of Estimating Survival Functions.

Nonparametric Methods for Comparing Survival Distributions.

Some Well-Known Parametric Survival Distributions and Their Applications.

Estimation Procedures for Parametric Survival Distributions Without Covariates.

Graphical Methods in Survival Distribution Fitting.

Tests of Goodness of Fit and Distribution Selection.

Parametric Methods for Comparing Two Survival Distributions.

Parametric Methods for Regression Model Fitting and Identification of Prognostic Factors.

Identification of Prognostic Factors Related to Survival Time: Cox Proportional Hazards Model.

Identification of Prognostic Factors Related to Survival Time: Nonproportional Hazards Models.

Identification of Risk Factors Related to Dichotomous and Polychotomous Outcomes.

Appendix A: Newton-Raphson Method.

Appendix B: Statistical Tables.

References.

Index.

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