🤖 AI Summary
This work addresses key limitations of traditional Bayesian profile regression in handling hierarchical or longitudinal data, its inability to effectively model interactions between latent profile clusters and covariates, and its reduced statistical efficiency under highly correlated covariate structures. To overcome these challenges, the authors integrate generalized linear mixed models (GLMMs) into the Bayesian profile regression framework, incorporating random effects to accommodate multilevel and longitudinal designs. The latent clusters derived from profile clustering are explicitly included as predictors in the outcome model, enabling direct modeling of cluster–covariate interactions. This approach substantially extends the applicability of profile regression and enhances both modeling efficiency and predictive performance for complex dependency structures. An open-source R package implementing this method combines Bayesian inference, GLMMs, and profile clustering, supporting continuous or binary outcomes and mixed-type covariates, with broad utility in epidemiology, social sciences, and clinical research.
📝 Abstract
ProfileGLMM is an R package integrating Generalised Linear Mixed Models (GLMMs) as the outcome model for Bayesian profile regression. This statistical framework simultaneously i) explains the variation in the outcome and ii) clusters the observations based on a specified set of interdependent clustering covariates. The derived cluster memberships are then incorporated, alongside others, as explanatory variables in the regression to model the outcome. This framework efficiently handles complex, highly correlated covariate structures whose direct inclusion in a standard regression model would be statistically sub-optimal. ProfileGLMM significantly extends Bayesian profile regression's scope by resolving two key constraints of previous implementations: 1) it allows the analysis of hierarchical and longitudinal data structures through the inclusion of random effects, and 2) it enables the study of interactions between latent clusters and other observable covariates. ProfileGLMM accommodates various data types, supporting both continuous or binary outcomes and both categorical and continuous clustering covariates. Built on fast Rcpp code with minimal mandatory parameters, ProfileGLMM offers a flexible analytical tool. It significantly enhances the utility of profile regression for researchers in fields such as epidemiology, social sciences, and clinical studies dealing with complex data.