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.

翻译：贝叶斯剖面回归混合模型（BPRM）可用于评估多暴露人群的健康风险。这些混合模型根据个体的暴露剖面和健康风险对其进行聚类。然而，基于蒙特卡洛马尔可夫链（MCMC）算法的结果在不同应用案例中表现出不稳定性。我们认为这种不稳定性源于两个原因：MCMC算法可能陷入后验分布的局部模态，且对MCMC算法输出所采用的后处理程序的选择会导致不同的聚类结构。本研究改进了先前工作中提出的MCMC算法，旨在避免后验分布的局部模态同时减少计算时间。我们还通过模拟研究比较了不同MCMC算法与后处理程序的性能，以提供其使用指南。最后探讨了在辐射流行病学中的应用案例。