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- W4287779069 abstract "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." @default.
- W4287779069 created "2022-07-26" @default.
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- W4287779069 date "2020-05-12" @default.
- W4287779069 modified "2023-09-24" @default.
- W4287779069 title "Bayesian survival analysis with BUGS" @default.
- W4287779069 doi "https://doi.org/10.48550/arxiv.2005.05952" @default.
- W4287779069 hasPublicationYear "2020" @default.
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