A mixture model for subtype identification in the context of disease progression modeling

TL;DR

This paper introduces a probabilistic mixture model that effectively identifies disease progression subtypes by capturing both temporal and spatial variability, outperforming traditional methods in accuracy and interpretability.

Contribution

The novel Disease Course Mapping model extends mixed-effects models with a mixture structure at latent parameters, enabling accurate clustering of disease subtypes in longitudinal data.

Findings

Achieved over 90% classification accuracy in simulations

More accurate parameter estimates than post hoc methods

Identified meaningful clusters in CADASIL patient data

Abstract

The progression of chronic diseases often follows highly variable trajectories, and the underlying factors remain poorly understood. Standard mixed-effects models typically represent inter-patient differences as random deviations around a common reference, which may obscure meaningful subgroups. We propose a probabilistic mixture extension of a mixed effects model, the Disease Course Mapping model, to identify distinct disease progression subtypes within a population. The mixture structure is introduced at the latent individual parameters, enabling clustering based on both temporal and spatial variability in disease trajectories. We evaluated the model through simulation studies to assess classification performance and parameter recovery. Classification accuracy exceeded 90% in simpler scenarios and remained above 80% in the most complex case, with particularly high recall and precision for fast-progressing clusters. Compared to a post hoc classification approach, the proposed model yielded more accurate parameter estimates, smaller biases, lower root mean squared errors, and reduced uncertainty. It also correctly recovered the true three-cluster structure in 93% of the simulations. Finally, we applied the model to a longitudinal cohort of CADASIL patients, identifying two clinically meaningful clusters, differentiating patients with early versus late onset and fast versus slow progression, with clear spatial patterns across motor and memory scores. Overall, this probabilistic mixture framework offers a robust, interpretable approach for clustering patients based on spatiotemporal disease dynamics.