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Bayesian survival analysis with BUGS

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arxiv 2005.05952 v2 pith:24YVMFIL submitted 2020-05-12 stat.AP

Danilo Alvares , Elena Lázaro , Virgilio Gómez-Rubio , Carmen Armero This is my paper · ORCID
classification stat.AP
keywords bayesiansurvivalanalysisbugsmodelsacceleratedadditionadvances
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Survival analysis is one of the most important fields of statistics in medicine and the biological sciences. In addition, the computational advances in the last decades have favoured the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this paper is to summarise some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, 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 are also discussed.

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