Norges Teknisk-naturvitenskapelige Universitet Approximate Bayesian Inference for Survival Models Approximate Bayesian Inference for Survival Models
Norges Teknisk-naturvitenskapelige Universitet Approximate Bayesian Inference for Survival Models Approximate Bayesian Inference for Survival Models
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Norges Teknisk-naturvitenskapelige Universitet 生存模型的近似贝叶斯推理 生存模型的近似贝叶斯推理
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通讯作者：
H. Rue
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作者：
S. Martino;R. Akerkar;H. Rue
事件发生时间数据的贝叶斯分析，通常称为生存分析，近年来受到越来越多的关注。在Cox型模型中，它允许使用来自完全似然而不是部分似然的信息，以便可以联合估计基线风险函数和模型参数。一般来说，贝叶斯方法允许对任何参数或感兴趣的预测量进行完整和精确的后验推断。另一方面，贝叶斯推理通常依赖于马尔可夫链蒙特卡罗（MCMC）技术，从用户的角度来看，该技术在提供答案时可能显得缓慢。在本文中，我们展示了一个新的推理工具，称为集成嵌套拉普拉斯近似（INLA）可以适应和应用于许多生存模型，使贝叶斯分析快速和准确，而不必依赖于MCMC为基础的推理。
Bayesian analysis of time-to-event data, usually called survival analysis, has received increasing attention in the last years. In Cox-type models it allows to use information from the full likelihood instead of from a partial likelihood, so that the baseline hazard function and the model parameters can be jointly estimated. In general, Bayesian methods permit a full and exact posterior inference for any parameter or predictive quantity of interest. On the other side, Bayesian inference often relies on Markov Chain Monte Carlo (MCMC) techniques which, from the user point of view, may appear slow at delivering answers. In this paper, we show how a new inferential tool named Integrated Nested Laplace approximations (INLA) can be adapted and applied to many survival models making Bayesian analysis both fast and accurate without having to rely on MCMC based inference.