Bayesian joint models for longitudinal and survival data
Methodology
2020-05-27 v1
Abstract
This paper takes a quick look at Bayesian joint models (BJM) for longitudinal and survival data. A general formulation for BJM is examined in terms of the sampling distribution of the longitudinal and survival processes, the conditional distribution of the random effects and the prior distribution. Next a basic BJM defined in terms of a mixed linear model and a Cox survival regression models is discussed and some extensions and other Bayesian topics are briefly outlined.
Cite
@article{arxiv.2005.12822,
title = {Bayesian joint models for longitudinal and survival data},
author = {Carmen Armero},
journal= {arXiv preprint arXiv:2005.12822},
year = {2020}
}