Statistical methods for survival data analysis have continued to flourish in the last two decades. Applications of the methods have been widened from their historical use in cancer and reliability research to business, criminology, epidemiology, and social and behavioral sciences. The third edition of Statistical Methods for Survival Data Analysis is intended to provide a comprehensive introduction of the most commonly used methods for analyzing survival data. It begins with basic definitions and interpretations of survival functions. From there, the reader is guided through methods, parametric and nonparametric, for estimating and comparing these functions and the search for a theoretical distribution (or model) to fit the data. Parametric and nonparametric approaches to the identification of prognostic factors that are related to survival are then discussed. Finally, regression methods, primarily linear logistic regression models, to identify risk factors for dichotomous and polychotomous outcomes are introduced. The third edition continues to be application-oriented, with a minimum level of mathematics. In a few chapters, some knowledge of calculus and matrix algebra is needed. The few sections that introduce the general mathematical structure for the methods can be skipped without loss of continuity. A large number of practical examples are given to assist the reader in understanding the methods and applications and in interpreting the results. Readers with only college algebra should find the book readable and understandable.

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