TY - JOUR 
A1 - Alvares, Danilo
AU - Lázaro, Elena
AU - Gómez-Rubio, Virgilio
AU - Armero, Carmen
T1 - Bayesian survival analysis with BUGS
Y1 - 2021
SN - 1097-0258
UR - http://hdl.handle.net/20.500.11939/7400
AB - 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.
KW - Bayesian inference, JAGS, R-packages, time-to-event analysis
KW - U10  Mathematical and statistical methods
LA - en
PB - Wiley
ER -