Introduction

  • Joseph G. Ibrahim
  • Ming-Hui Chen
  • Debajyoti Sinha
Part of the Springer Series in Statistics book series (SSS)

Abstract

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.

Keywords

Posterior Distribution Markov Chain Monte Carlo Frailty Model Markov Chain Monte Carlo Sampling Accelerate Failure Time Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Joseph G. Ibrahim
    • 1
  • Ming-Hui Chen
    • 2
  • Debajyoti Sinha
    • 3
  1. 1.Department of BiostatisticsHarvard School of Public Health and Dana-Farber Cancer InstituteBostonUSA
  2. 2.Department of Mathematical SciencesWorcester Polytechnic InstituteWorcesterUSA
  3. 3.Department of Biometry and EpidemiologyMedical Universtiy of South CarolinaCharlestonUSA

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