ProfileGLMM: a R Package Extending Bayesian Profile Regression using Generalised Linear Mixed Models

TL;DR

ProfileGLMM is an R package that extends Bayesian profile regression by incorporating Generalised Linear Mixed Models, allowing for hierarchical, longitudinal, and interaction analyses with complex covariate structures.

Contribution

It introduces a flexible R package that handles hierarchical and longitudinal data, enabling interaction studies within Bayesian profile regression frameworks.

Findings

Supports various data types including continuous and binary outcomes.

Handles complex, correlated covariate structures effectively.

Allows analysis of hierarchical and longitudinal data with random effects.

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.