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arXiv · 2602.22018

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

Sterre de Jonge·Elisabeth J. Vinke·Meike W. Vernooij·Daniel C. Alexander·Alexandra L. Young·Esther E. Bron
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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.

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Sterre de Jonge, Elisabeth J. Vinke, Meike W. Vernooij, Daniel C. Alexander, Alexandra L. Young, Esther E. Bron. 2026-02-25. Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data. https://arxiv.org/abs/2602.22018

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