Functional clustering in nested designs: Modeling variability in reproductive epidemiology studies

TL;DR

This paper introduces a flexible mixture model based on nested Dirichlet processes for functional clustering in nested designs, effectively capturing variability in reproductive epidemiology data.

Contribution

It extends existing functional clustering methods by incorporating a generalized Dirichlet process, enabling more adaptable clustering in nested data structures.

Findings

Improved clustering flexibility over traditional methods

Application to hormone profile data from menstrual cycles

Demonstrated effectiveness in reproductive epidemiology studies

Abstract

We discuss functional clustering procedures for nested designs, where multiple curves are collected for each subject in the study. We start by considering the application of standard functional clustering tools to this problem, which leads to groupings based on the average profile for each subject. After discussing some of the shortcomings of this approach, we present a mixture model based on a generalization of the nested Dirichlet process that clusters subjects based on the distribution of their curves. By using mixtures of generalized Dirichlet processes, the model induces a much more flexible prior on the partition structure than other popular model-based clustering methods, allowing for different rates of introduction of new clusters as the number of observations increases. The methods are illustrated using hormone profiles from multiple menstrual cycles collected for women in the Early Pregnancy Study.