考虑并行退火和比较后处理程序在贝叶斯轮廓回归模型中的应用：用于生存结果和相关暴露的分析
Considering parallel tempering and comparing post-treatment procedures in Bayesian Profile Regression Models for a survival outcome and correlated exposures
AI总结：
本文提出改进MCMC算法以避免后验分布局部模式并减少计算时间，通过模拟研究比较不同后处理程序的性能，应用于辐射流行病学中的生存结果分析。
摘要：
贝叶斯轮廓回归混合模型（BPRM）允许在多暴露人群中评估健康风险。这些混合模型根据个体的暴露概况和健康风险对个体进行聚类。然而，基于马尔可夫链蒙特卡洛（MCMC）算法的结果在不同应用案例中表现出不稳定。我们假设存在两个原因。MCMC算法可能被困在后验分布的局部模式中，而对MCMC算法输出使用的后处理程序会导致不同的聚类结构。在本工作中，我们提出改进之前工作中提出的MCMC算法，以避免后验分布的局部模式，同时减少计算时间。我们还进行了一项模拟研究，以比较MCMC算法和不同后处理程序的性能，以提供其使用的指导方针。考虑了辐射流行病学中的一个应用。
英文摘要：
Bayesian profile regression mixture models (BPRM) allow to assess a health risk in a multi-exposed population. These mixture models cluster individuals according to their exposure profile and their health risk. However, their results, based on Monte-Carlo Markov Chain (MCMC) algorithms, turned out to be unstable in different application cases. We suppose two reasons for this instability. The MCMC algorithm can be trapped in local modes of the posterior distribution and the choice of post-treatment procedures used on the output of the MCMC algorithm leads to different clustering structures. In this work, we propose improvements of the MCMC algorithms proposed in previous works in order to avoid the local modes of the posterior distribution while reducing the computation time. We also carry out a simulation study to compare the performances of the MCMC algorithms and different post-processing in order to provide guidelines on their use. An application in radiation epidemiology is considered.
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