Empirical Likelihood Method in Survival Analysis : With R Implementation, Second Edition

This book systematically covers empirical likelihood methods in most important topics in survival analysis: the Kaplan–Meier and the Nelson–Aalen estimator, the log rank test, the Cox proportional hazards model and the accelerated failure time models.

This book systematically covers empirical likelihood methods in most important topics in survival analysis: the Kaplan–Meier and the Nelson–Aalen estimator, the log rank test, the Cox proportional hazards model and the accelerated failure time models. In addition, it also covers an extension of the Cox model–the short term/long term hazard ratio model of Yang and Prentice. Finally, empirical likelihood methods with current status data or type I interval censored data are investigated: estimation/test for the mean/hazard/probability and regression models are discussed. The author of this book is also the author of several R packages for empirical likelihood calculations with survival data. Every topic discussed gets immediately put into action with R code in examples that users can replicate and experiment with. Includes more than 70 examples illustrating the use of empirical likelihood, many with real data. Provides complete R computational codes that reader can replicate the results in the book. Includes over 80 exercise problems making it suitable to be adopted as a textbook. Newly added materials now cover more general types of censored survival data. Mai Zhou is a professor emeritus at the University of Kentucky. He received his Ph.D. in Statistics from Columbia University.