6533b7d7fe1ef96bd12690b0

RESEARCH PRODUCT

Bayesian survival analysis with BUGS

Carmen ArmeroDanilo AlvaresVirgilio Gómez-rubioElena Lázaro

subject

Statistics and ProbabilityFOS: Computer and information sciencesEpidemiologyComputer scienceBayesian probabilityContext (language use)Accelerated failure time modelMachine learningcomputer.software_genreBayesian inference01 natural sciencesStatistics - Applications010104 statistics & probability03 medical and health sciences0302 clinical medicineFrequentist inferenceHumansApplications (stat.AP)030212 general & internal medicine0101 mathematicsModels StatisticalSyntax (programming languages)business.industryR Programming LanguageBayes TheoremSurvival AnalysisMedical statisticsArtificial intelligencebusinesscomputer

description

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.

yearjournalcountryeditionlanguage
2020-05-12
http://arxiv.org/abs/2005.05952