Bayesian survival analysis with BUGS

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...

Full description

Saved in:
Bibliographic Details
Published in:Statistics in medicine 2021-05, Vol.40 (12), p.2975-3020
Main Authors: Alvares, Danilo, Lázaro, Elena, Gómez‐Rubio, Virgilio, Armero, Carmen
Format: Article
Language:English
Subjects:
Bayesian inference
JAGS
R‐packages
Survival analysis
time‐to‐event analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.8933