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.

翻译：疾病进展建模为从短期生物标志物数据中识别长期疾病轨迹提供了一个稳健的框架。对于理解具有漫长病程的疾病（如阿尔茨海默病），它是一个有价值的工具。大多数疾病进展模型的一个关键局限在于其仅适用于单一数据类型（例如连续数据），从而限制了其在异质性真实世界数据集上的适用性。为解决这一局限，我们提出了混合事件模型，这是一种能够同时处理离散和连续数据类型的新型疾病进展模型。该模型在亚型与分期推断框架内实现，形成了混合亚型与分期推断模型，从而能够进行亚型与进展建模。我们通过模拟实验和来自阿尔茨海默病神经影像学计划的真实世界数据，证明了混合亚型与分期推断模型在混合数据集上的良好性能。代码可在以下网址获取：https://github.com/ucl-pond/pySuStaIn。