Statistical methods for survival data analysis continues to flourish in modern research, with applications expanding from their historical use in cancer and reliability research to the areas of business, epidemiology, and the social and behavioral sciences. Statistical Methods for Survival Data Analysis, Fourth Editionprovides a comprehensive introduction to the most commonly-used methods for analyzing survival data. The book begins with basic definitions and interpretations of survival functions and goes on to guide readers through parametric and nonparametric methods for estimating and comparing these functions and the search for a theoretical distribution to fit the data. Also discussed are the parametric and nonparametric approaches to the identification of prognostic factors that are related to survival as well as the regression methods for identifying risk factors for dichotomous and polychotomous outcomes. The Fourth Edition continues to provide an application-oriented approach, with a minimum level of mathematics. The new edition now features coverage of marginal models and random effect models for analyzing correlated censored or uncensored data;  a completely updated chapter on goodness of fit and revised treatment of parametric methods for regression model fitting that now includes a focus on accelerated failure time models; expanded discussion on the Cox proportional hazards model. A large number of practical examples are given that utilize SAS, SPSS, and R software to assist readers with understanding the methods and in interpreting the results.