Statistical Methods for Survival Data Analysis / Edition 3by Elisa T. Lee, John Wang
Pub. Date: 04/04/2003
The study of survival data attempts to predict the probability of response, survival, or mean lifetime; compare; the survival distributions of experimental animals or of human patients; and identify risk and/or prognosis factors. Statistical Methods for Survival Data Analysis, Third Edition examines the statistical methods for analyzing survival data from
The study of survival data attempts to predict the probability of response, survival, or mean lifetime; compare; the survival distributions of experimental animals or of human patients; and identify risk and/or prognosis factors. Statistical Methods for Survival Data Analysis, Third Edition examines the statistical methods for analyzing survival data from laboratory studies of animals, clinical and epidemiological studies of humans, and other appropriate applications.
Emphasizing applications over rigorous mathematics, this extremely useful reference provides thorough discussions of the most commonly used parametric and nonparametric methods in survival analysis, as well as guidelines for the planning and design of clinical trials. The authors give special consideration to the study of survival data in biomedical sciences, though the methods are suitable for applications in industrial reliability, the social sciences, and business.
This Third Edition brings this standard in the field up to date with new material and revised references including:
- A new introduction to left and interval censored data
- The generalized gamma and log-logistic distribution
- Estimation procedures for left and interval censored data
- Parametric models with covariates
- Cox's proportional hazards model including stratification and time-dependent covariates, and some non-proportional hazards models
- Goodness-of-Fit tests and model selection methods
- Multiple responses to the logistic regression model
- Numerous real-life examples which illustrate key concepts
- Computer programming codes in SAS, BMDP, and SPSS for most examples
- Related FTP site providing large data sets
These additions and revisions make Statistical Methods for Survival Data Analysis, Third Edition, more valuable than ever as an essential reference for biomedical investigators, statisticians, epidemiologists, and researchers in other disciplines involved or interested in the analysis of survival data.
Author Biography: 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.
Table of ContentsPreface.
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 Distribution and Their Applications.
Estimation Procedures for Parametric Survival Distributions Without Covariates.
Graphical Methods in Survival Distribution Fitting.
Tests of Goodness-of-Fit and Distributon 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: Non-Proportional Hazards Models.
Identification of Rich Factors Related to Dichotomous or Polychotomous Outcomes.
Appendix A: The Newton-Raphson Method.
Appendix B: Statistical Tables.
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