T1 Bayesian survival analysis with BUGS
A1 Alvares, Danilo
A1 Lázaro, Elena
A1 Gómez-Rubio, Virgilio
A1 Armero, Carmen
K1 Bayesian inference, JAGS, R-packages, time-to-event analysis
K1 U10  Mathematical and statistical methods
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.
PB Wiley
SN 1097-0258
YR 2021
FD 2021
LK http://hdl.handle.net/20.500.11939/7400
UL http://hdl.handle.net/20.500.11939/7400
LA en
NO Alvares, D., Lazaro, E., Gomez‐Rubio, V. & Armero, C. (2021). Bayesian survival analysis with BUGS. Statistics in Medicine, 40(12), 2975-3020.
DS MINDS@UW
RD Aug 10, 2022