Bayesian survival analysis with BUGS

Danilo Alvares; Elena Lázaro; Virgilio Gómez-Rubio; Carmen Armero

doi:10.1002/sim.8933

Bayesian survival analysis with BUGS

Stat Med. 2021 May 30;40(12):2975-3020. doi: 10.1002/sim.8933. Epub 2021 Mar 13.

Authors

Danilo Alvares¹, Elena Lázaro², Virgilio Gómez-Rubio³, Carmen Armero⁴

Affiliations

¹ Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile.
² Plant Protection and Biotechnology Centre, Instituto Valenciano de Investigaciones Agrarias, Valencia, Spain.
³ Department of Mathematics, School of Industrial Engineering-Albacete, Universidad de Castilla-La Mancha, Ciudad Real, Spain.
⁴ Department of Statistics and Operational Research, Universitat de València, Valencia, Spain.

PMID: 33713474
DOI: 10.1002/sim.8933

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.

Keywords: Bayesian inference; JAGS; R-packages; time-to-event analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Humans
Models, Statistical*
Survival Analysis