A fusion learning method to subgroup analysis of Alzheimer's disease

TL;DR

This paper introduces a fusion learning approach using concave fusion and B-splines to identify and analyze latent subgroups in Alzheimer's disease progression, enabling better understanding and targeted treatment development.

Contribution

It applies a novel concave fusion method to longitudinal Alzheimer's data, automatically identifying subgroups and estimating their heterogeneous disease trajectories with statistical inference support.

Findings

Successfully identified latent subgroups in Alzheimer's progression

Estimated subgroup-specific disease trajectories with theoretical backing

Provided a framework for statistical inference in subgroup analysis

Abstract

Uncovering the heterogeneity in the disease progression of Alzheimer's is a key factor to disease understanding and treatment development, so that interventions can be tailored to target the subgroups that will benefit most from the treatment, which is an important goal of precision medicine. However, in practice, one top methodological challenge hindering the heterogeneity investigation is that the true subgroup membership of each individual is often unknown. In this article, we aim to identify latent subgroups of individuals who share a common disorder progress over time, to predict latent subgroup memberships, and to estimate and infer the heterogeneous trajectories among the subgroups. To achieve these goals, we apply a concave fusion learning method proposed in Ma and Huang (2017) and Ma et al. (2019) to conduct subgroup analysis for longitudinal trajectories of the Alzheimer's disease data. The heterogeneous trajectories are represented by subject-specific unknown functions which are approximated by B-splines. The concave fusion method can simultaneously estimate the spline coefficients and merge them together for the subjects belonging to the same subgroup to automatically identify subgroups and recover the heterogeneous trajectories. The resulting estimator of the disease trajectory of each subgroup is supported by an asymptotic distribution. It provides a sound theoretical basis for further conducting statistical inference in subgroup analysis..