Bayesian Event-Based Model for Disease Subtype and Stage Inference

Hongtao Hao, Joseph Austerweil

Keywords: Event-based model, Disease progression, Bayesian methods, Subtypes, Alzheimer's disease

TL;DR: We evaluate and improve on the state-of-the-art for estimating disease progression with subtypes

Track: Proceedings

Abstract: 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 large-scale Alzheimer's data set. We find BebmS estimates subtypes for the real-world data that are more consistent with the latest scientific consensus of Alzheimer's disease progression than SuStaIn.

General Area: Models and Methods

Specific Subject Areas: Bayesian & Probabilistic Methods, Evaluation Methods & Validity, Unsupervised Learning

Data And Code Availability: No

Ethics Board Approval: Yes

Entered Conflicts: I confirm the above

Anonymity: I confirm the above

Submission Number: 229