Bayesian survival analysis with BUGS. (13th March 2021)

• Record Type: Journal Article
• Authors: Alvares, Danilo; Lázaro, Elena; Gómez‐Rubio, Virgilio; Armero, Carmen
• Is Part Of: Statistics in medicine. Volume 40:Number 12(2021)
• Page Start: 2975
• Page End: 3020
• Publication Date: 2021-03-13
• Subjects: Bayesian inference -- JAGS -- R‐packages -- time‐to‐event analysis; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8933
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi‐state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R‐packages is also discussed.