6533b7d3fe1ef96bd12600f8

RESEARCH PRODUCT

Bayesian joint models for longitudinal and survival data

Carmen Armero

subject

Methodology (stat.ME)FOS: Computer and information sciencesSampling distributionBayesian probabilityPrior probabilityStatisticsRegression analysisConditional probability distributionRandom effects modelJoint (geology)Statistics - MethodologyGeneralized linear mixed modelMathematics

description

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.

yearjournalcountryeditionlanguage
2020-05-26
https://dx.doi.org/10.48550/arxiv.2005.12822