Improving Bayesian Profile Regression for Survival Analysis

This paper addresses the instability issues in Bayesian profile regression mixture models (BPRM) used for assessing health risks in multi-exposed populations. It focuses on improving the MCMC algorithm to avoid local modes and comparing post-treatment procedures to stabilize clustering results. The research is relevant to fields like radiation epidemiology and offers practical guidelines for using these models.