John Wang

Elisa T. Lee

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.

Statistical Methods for Survival Data Analysis

Functions of survival time; Examples of survival data analysis; Nonparametric methods of estimating survival functions; Nonparametric methods for comparing survival distributions; Some well-known survival distributions and their applications; Graphical methods for sulvival distribution fitting and goodness-of-fit tests; Analytical estimation procedures for sulvival distributions; Parametric methods for comparing two survival distribution; Identification of prognostic factors related to survival time; Identification of risk factors related to dichotomous data; Planning and design of clinical trials (I); Planning and design of clinicL trials(II).

John Wenyu Wang

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.

Elisa T. Lee

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