A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables
This work provides a method for clustering clinical data with mixed-type variables, addressing specific bottlenecks like censored biomarkers and variable importance, but it is incremental as it builds on existing Bayesian and mixture model frameworks.
The authors tackled the challenges of applying finite mixture models to data with mixed-type variables, including sensitivity to initial values, handling censored variables due to limits of detection, and variable selection, by proposing a Bayesian finite mixture model that simultaneously addresses these issues, with performance validated through simulations and real data application.
Finite mixture model is an important branch of clustering methods and can be applied on data sets with mixed types of variables. However, challenges exist in its applications. First, it typically relies on the EM algorithm which could be sensitive to the choice of initial values. Second, biomarkers subject to limits of detection (LOD) are common to encounter in clinical data, which brings censored variables into finite mixture model. Additionally, researchers are recently getting more interest in variable importance due to the increasing number of variables that become available for clustering. To address these challenges, we propose a Bayesian finite mixture model to simultaneously conduct variable selection, account for biomarker LOD and obtain clustering results. We took a Bayesian approach to obtain parameter estimates and the cluster membership to bypass the limitation of the EM algorithm. To account for LOD, we added one more step in Gibbs sampling to iteratively fill in biomarker values below or above LODs. In addition, we put a spike-and-slab type of prior on each variable to obtain variable importance. Simulations across various scenarios were conducted to examine the performance of this method. Real data application on electronic health records was also conducted.