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dc.contributor.authorAlvares, Danilo
dc.contributor.authorLázaro, Elena
dc.contributor.authorGómez-Rubio, Virgilio
dc.contributor.authorArmero, Carmen
dc.date.accessioned2021-06-02T12:13:08Z
dc.date.available2021-06-02T12:13:08Z
dc.date.issued2021es
dc.identifier.citationAlvares, D., Lázaro, E., Gómez‐Rubio, V., & Armero, C. (2021). Bayesian survival analysis with BUGS. Statistics in Medicine 40: 2975-3020.es
dc.identifier.issn1097-0258
dc.identifier.urihttp://hdl.handle.net/20.500.11939/7400
dc.description.abstractSurvival 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.es
dc.language.isoenes
dc.publisherWileyes
dc.subjectBayesian inference, JAGS, R-packages, time-to-event analysises
dc.titleBayesian survival analysis with BUGSes
dc.authorAddressIVIAes
dc.entidadIVIACentro de Protección Vegetal y Biotecnologíaes
dc.identifier.doi10.1002/sim.8933es
dc.identifier.urlhttps://onlinelibrary.wiley.com/doi/full/10.1002/sim.8933es
dc.journal.issueNumber0es
dc.journal.titleStatistics in Medicinees
dc.journal.volumeNumber40es
dc.page.final3020es
dc.page.initial2975es
dc.rights.accessRightsrestringedAccesses
dc.source.typeelectronicoes
dc.subject.agrisU10 Mathematical and statistical methodses
dc.type.hasVersionpublishedVersiones


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