Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile.
Plant Protection and Biotechnology Centre, Instituto Valenciano de Investigaciones Agrarias, Valencia, Spain.
Stat Med. 2021 May 30;40(12):2975-3020. doi: 10.1002/sim.8933. Epub 2021 Mar 13.
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.
生存分析是医学和生物科学中统计学最重要的领域之一。此外，过去几十年的计算进展促进了贝叶斯方法在这种情况下的使用，为传统的频率派方法提供了灵活而强大的替代方法。本文的目的是总结一些最流行的贝叶斯生存模型，如加速失效时间、比例风险、混合治愈、竞争风险、多状态、脆弱性和纵向与生存数据的联合模型。此外，还使用可以使用 R 编程语言中的 JAGS 运行的 BUGS 语法提供了每个呈现模型的实现。还讨论了其他贝叶斯 R 包的引用。
