The analysis of time-to-event data, generally called survival analysis, arises in many fields of study, including medicine, biology, engineering, public health, epidemiology, and economics. Although the methods we present in this book can be used in all of these disciplines, our applications will focus exclusively on medicine and public health. There have been several textbooks written that address survival analysis from a frequentist perspective. These include Lawless, Cox and Oakes (1984), Fleming and Harrington (1991), Lee (1992), Andersen, Borgan, Gill, and Keiding (1993), and Klein and Moeschberger (1997). Although these books are quite thorough and examine several topics, they do not address Bayesian analysis of survival data in depth. Klein and Moeschberger (1997), however, do present one section on Bayesian nonparametric methods. Bayesian analysis of survival data has received much recent attention due to advances in computational and modeling techniques. Bayesian methods are now becoming quite common for survival data and have made their way into the medical and public health arena.
KeywordsPosterior Distribution Markov Chain Monte Carlo Frailty Model Markov Chain Monte Carlo Sampling Accelerate Failure Time Model
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