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

• URL: http://arxiv.org/abs/2602.22018v1
• Date: Wed, 25 Feb 2026 15:31:30 GMT
• Title: Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
• Authors: Sterre de Jonge, Elisabeth J. Vinke, Meike W. Vernooij, Daniel C. Alexander, Alexandra L. Young, Esther E. Bron,
• Abstract summary: We propose a novel disease progression model that handles both discrete and continuous data types.<n>We demonstrate the effectiveness of Mixed-SuStaIn through simulation experiments and real-world data from the Alzheimer's Disease Neuroimaging Initiative.
• Score: 37.93788591991097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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