A Bayesian Finite Mixture Model Approach for Clustering Correlated Mixed-type Variables and Censored Biomarkers

Music City Center 

Clustering mixed-type data is a major challenge in biopharmaceutical research, particularly for phenotyping complex diseases where patient heterogeneity complicates treatment. Existing methods often assume local independence or fail to handle high-dimensional datasets with correlated continuous and categorical variables and censored biomarkers. We propose a Bayesian finite mixture model (BFMM) that integrates flexible dependence structures, spike-and-slab priors for variable importance, and a specialized Gibbs sampling step for imputing censored biomarkers. BFMM enables stable clustering and provides interpretable importance weights for both variable types, offering insights into cluster assignments. Simulations show BFMM outperforms existing methods, particularly for correlated data with varying censoring levels. Application to real-world datasets further validates its effectiveness. Our findings underscore BFMM's potential as a robust, interpretable tool for biomedical data analysis, with implications for precision medicine and targeted interventions.