Semiparametric Functional Multistate Modeling of Alzheimer's Disease Progression with Imaging Biomarkers

2026-08-06

AI Summary

This study addresses the limitations of existing Alzheimer’s disease imaging-based prediction methods, which typically focus on a single endpoint and struggle with interval-censored multi-state transitions commonly encountered in clinical settings. The authors model disease progression as an intermittently observed multi-state process and, for the first time, incorporate functional imaging biomarkers into a semiparametric proportional intensity framework tailored for interval-censored transition times. Their approach integrates functional principal component analysis with nonparametric maximum pseudo-likelihood estimation and introduces a profile score test to assess overall association. Applied to ADNI data, the model demonstrates that baseline lateral ventricle morphology is significantly associated with disease progression, outperforming current methods in predictive accuracy while exhibiting strong theoretical properties and favorable performance in simulations.

Abstract

Medical imaging provides rich information for predicting Alzheimer's disease progression, but existing imaging-based methods typically focus on a single survival endpoint and treat transition times as exactly observed or right-censored. Motivated by the Alzheimer's Disease Neuroimaging Initiative (ADNI), we develop a predictive framework that represents disease progression as an intermittently observed multistate process with interval-censored transition times and predicts future progression from any current disease state. We incorporate imaging biomarkers as functional covariates in a semiparametric proportional intensity model and combine functional principal component analysis with nonparametric maximum pseudo likelihood estimation. We further develop a profile score test for assessing the overall association between the imaging covariate and the multistate process. We establish the asymptotic properties of the proposed estimators and test statistic, and simulation studies demonstrate satisfactory finite-sample performance. In the ADNI application, baseline lateral ventricular morphology is strongly associated with Alzheimer's disease progression. The proposed functional multistate model also achieves the best overall predictive performance among the competing methods.

Problem

• Alzheimer's disease
• multistate modeling
• imaging biomarkers
• interval-censored data
• disease progression

Innovation

• functional multistate model
• interval-censored transition times
• imaging biomarkers
• semiparametric proportional intensity
• functional principal component analysis