Bayesian Event-Based Model for Disease Subtype and Stage Inference
This work addresses the need for robust subtype and stage inference in chronic diseases like Alzheimer's, but it is incremental as it builds on an existing model with Bayesian improvements.
The authors tackled the problem of modeling disease progression subtypes and stages by developing a Bayesian variant (BEBMS) of an existing model, which substantially outperformed the baseline (SuStaIn) in synthetic experiments and provided more consistent results with scientific consensus in Alzheimer's disease data.
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.