Bayesian approaches to variable selection in mixture models with application to disease clustering.

ABSTRACT: In biomedical research, cluster analysis is often performed to identify patient subgroups based on patients' characteristics or traits. In the model-based clustering for identifying patient subgroups, mixture models have played a fundamental role in modeling. While there is an increasing interest in using mixture modeling for identifying patient subgroups, little work has been done in selecting the predictors that are associated with the class assignment. In this study, we develop and compare two approaches to perform variable selection in the context of a mixture model to identify important predictors that are associated with the class assignment. These two approaches are the one-step approach and the stepwise approach. The former refers to an approach in which clustering and variable sel

SUBMITTER: Lu Z 

PROVIDER: S-EPMC9869999 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature