A Bayesian Nonparametric Approach for Clustering Functional Trajectories over Time

TL;DR

This paper introduces a Bayesian nonparametric method for clustering functional trajectories that evolve over time, enabling flexible analysis of time-dependent patterns in biomedical data.

Contribution

It presents a novel Bayesian nonparametric model that clusters functional data over time while capturing temporal dependence and transitions between clusters.

Findings

Successfully applied to mobile health data from smoking cessation study

Demonstrates ability to identify evolving clusters of functional trajectories

Provides insights into behavioral transitions over time

Abstract

Functional concurrent, or varying-coefficient, regression models are commonly used in biomedical and clinical settings to investigate how the relation between an outcome and observed covariate varies as a function of another covariate. In this work, we propose a Bayesian nonparametric approach to investigate how clusters of these functional relations evolve over time. Our model clusters individual functional trajectories within and across time periods while flexibly accommodating the evolution of the partitions across time periods with covariates. Motivated by mobile health data collected in a novel, smartphone-based smoking cessation intervention study, we demonstrate how our proposed method can simultaneously cluster functional trajectories, accommodate temporal dependence, and provide insights into the transitions between functional clusters over time.