Chronic diseases often progress differently across patients. Rather than randomly varying, there are typically a small number of subtypes for how a disease progresses across patients. To capture this structured heterogeneity, the Subtype and Stage Inference Event-Based Model (SuStaIn) estimates the number of subtypes, the order of disease progression for each subtype, and assigns each patient to a subtype from primarily cross-sectional data. It has been widely applied to uncover the subtypes of many diseases and inform our understanding of them. But how robust is its performance? In this paper, we develop a principled Bayesian subtype variant of the event-based model (BEBMS) and compare its performance to SuStaIn in a variety of synthetic data experiments with varied levels of model misspecification. BEBMS substantially outperforms SuStaIn across ordering, staging, and subtype assignment tasks. Further, we apply BEBMS and SuStaIn to a real-world Alzheimer's data set. We find BEBMS has results that are more consistent with the scientific consensus of Alzheimer's disease progression than SuStaIn.

翻译：慢性疾病在不同患者中的进展方式往往存在差异。这种差异并非随机波动，而是通常存在少数几种疾病进展的亚型。为捕捉这种结构化异质性，亚型与阶段推断事件模型（SuStaIn）可从主要基于横截面数据中估计亚型数量、每种亚型的疾病进展顺序，并将每位患者分配到特定亚型。该模型已被广泛应用于揭示多种疾病的亚型并加深对疾病的理解。但其性能的稳健性如何？本文基于事件模型开发了一种严谨的贝叶斯亚型变体（BEBMS），并在包含不同程度模型误设的合成数据实验中，将其与SuStaIn的性能进行比较。BEBMS在排序、分期和亚型分配任务上显著优于SuStaIn。此外，我们将BEBMS和SuStaIn应用于真实世界的阿尔茨海默病数据集，发现BEBMS得出的结果比SuStaIn更符合阿尔茨海默病进展的科学共识。