Joint Progression Modeling (JPM): A Probabilistic Framework for Mixed-Pathology Progression

Hongtao Hao, Joseph Austerweil

Keywords: Neurodegenerative diseases, Disease progression, Event-based model, Rank aggregations, Bayesian inference, Mixed Pathology

TL;DR: We proposed a model to generate and infer from mixed-pathology data for disease progression.

Track: Proceedings

Abstract: Event-based models (EBMs) infer disease progression from cross-sectional data, and standard EBMs assume a single underlying disease per individual. In contrast, mixed pathologies are common in neurodegeneration. We introduce the Joint Progression Model (JPM), a probabilistic framework that treats single-disease trajectories as partial rankings and builds a prior over joint progressions. We study several JPM variants (Pairwise, Bradley--Terry, Plackett--Luce, and Mallows) and analyze three properties: (i) calibration---whether lower model energy predicts smaller distance to the ground truth ordering; (ii) separation---the degree to which sampled rankings are distinguishable from random permutations; and (iii) sharpness---the stability of sampled aggregate rankings. All variants are calibrated, and all achieve near-perfect separation; sharpness varies by variant and is well-predicted by simple features of the input partial rankings (number and length of rankings, conflict, and overlap). In synthetic experiments, JPM improves ordering accuracy by roughly 21\% over a strong EBM baseline (SA-EBM) that treats the joint disease as a single condition. Finally, using NACC, we find that the Mallows variant of JPM and the baseline model (SA-EBM) have results that are more consistent with prior literature on the possible disease progression of the mixed pathology of AD and VaD.

General Area: Models and Methods

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

Data And Code Availability: No

Ethics Board Approval: Yes

Entered Conflicts: I confirm the above

Anonymity: I confirm the above

Submission Number: 286