Event History and Survival Analysis

NEW TO THIS EDITION:

• Provides a more complete treatment of censoring, including a worked example of how to test for sensitivity to informative censoring (in Chapter 2 on Discrete-Time Methods).
• Gives more attention to accelerated failure time models. There is also more emphasis on interpretation of results and methods for assessing model fit (in Chapter 3 on Parametric Methods for Continuous-Time Data).
• Includes more detail on the treatment of time-varying explanatory variables, using both the programming statements method and the episode splitting method. There is also more extensive discussion of methods for testing the proportional hazards assumption and methods for handling tied data. Finally, there is a brief section on using the Cox model to generate predictions (in Chapter 4 on Cox Regression).
• Provides worked examples on testing differences in coefficients across different kinds of events. There is also a new section on cumulative incidence functions, an alternative and increasingly popular approach to competing risks (in Chapter 5 on Multiple Kinds of Events).
• Describes several new methods that were unavailable when the first edition published, including negative binomial models for event-count data, robust standard errors, and shared frailty (random effects) models.  Methods based on gap times are distinguished from methods based on origin times (in Chapter 6 on Repeated Events).

KEY FEATURES:

• Provides a user-friendly, non-mathematical approach that is accessible, clear, and easy-to-read
• Emphasizes logistic regression models