Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data

TL;DR

This paper introduces Mixed-SuStaIn, a novel disease progression model that integrates discrete and continuous data types within the SuStaIn framework, improving analysis of heterogeneous datasets like those in Alzheimer's disease.

Contribution

The paper presents the Mixed-SuStaIn model, extending disease progression modeling to handle mixed data types, which enhances applicability to real-world, heterogeneous datasets.

Findings

Effective on simulated data

Performs well on Alzheimer's dataset

Available code implementation

Abstract

Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable tool to gain a deeper understanding of diseases with a long disease trajectory, such as Alzheimer's disease. A key limitation of most disease progression models is that they are specific to a single data type (e.g., continuous data), thereby limiting their applicability to heterogeneous, real-world datasets. To address this limitation, we propose the Mixed Events model, a novel disease progression model that handles both discrete and continuous data types. This model is implemented within the Subtype and Stage Inference (SuStaIn) framework, resulting in Mixed-SuStaIn, enabling subtype and progression modeling. We demonstrate the effectiveness of Mixed-SuStaIn through simulation experiments and real-world data from the Alzheimer's Disease Neuroimaging Initiative, showing that it performs well on mixed datasets. The code is available at: https://github.com/ucl-pond/pySuStaIn.