Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
This work addresses the problem of modeling disease progression from heterogeneous datasets for researchers and clinicians in fields like Alzheimer's disease, though it is incremental as it extends an existing framework.
The authors tackled the limitation of disease progression models being specific to single data types by proposing the Mixed Events model, which handles both discrete and continuous data, and demonstrated its effectiveness through simulation and real-world Alzheimer's disease data.
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.