A fusion learning method to subgroup analysis for longitudinal trajectories

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 paper, 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 propose a nonparametric fusion learning method that can automatically identify subgroups and recover the heterogeneous trajectories, which are represented by subject-specific unknown functions. We approximate the unknown functions by B-splines, and apply a concave fusion penalization method that can merge the estimated functions together for the subjects belonging to the same subgroup. 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. Our method is applied to a longitudinal Alzheimer's Disease data set.