贝叶斯概率
拉普拉斯法
计算机科学
生存分析
语法
加速失效时间模型
贝叶斯推理
比例危险模型
事件(粒子物理)
人工智能
机器学习
统计
数学
协变量
量子力学
物理
作者
Danilo Alvares,Janet van Niekerk,Elias Teixeira Krainski,Håvard Rue,Denis Rustand
摘要
This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.
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