Joint analysis for multivariate longitudinal and event time data with a change point anchored at interval-censored event time

2026-02-16

AI Summary

This study addresses the challenge of modeling the bidirectional association between interval-censored onset times and multivariate longitudinal biomarkers in Huntington’s disease clinical research. The authors propose a novel joint model that, for the first time, incorporates an anchored change-point mechanism within an interval-censored event time framework, dynamically coupling biomarker trajectories with the timing of disease onset. This approach not only quantifies the influence of biomarkers on disease risk but also captures structural shifts in biomarker trajectories following event occurrence, thereby enabling bidirectional causal inference between longitudinal processes and event time. Simulation studies demonstrate favorable performance under finite-sample settings, and application to the PREDICT-HD cohort reveals dynamic interactions between cognitive and motor dysfunction throughout disease progression.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder characterized by motor dysfunction, psychiatric disturbances, and cognitive decline. The onset of HD is marked by severe motor impairment, which may be predicted by prior cognitive decline and, in turn, exacerbate cognitive deficits. Clinical data, however, are often collected at discrete time points, so the timing of disease onset is subject to interval censoring. To address the challenges posed by such data, we develop a joint model for multivariate longitudinal biomarkers with a change point anchored at an interval-censored event time. The model simultaneously assesses the effects of longitudinal biomarkers on the event time and the changes in biomarker trajectories following the event. We conduct a comprehensive simulation study to demonstrate the finite-sample performance of the proposed method for causal inference. Finally, we apply the method to PREDICT-HD, a multisite observational cohort study of prodromal HD individuals, to ascertain how cognitive impairment and motor dysfunction interact during disease progression.

Problem

• joint modeling
• multivariate longitudinal data
• interval-censored event time
• change point
• Huntington's disease

Innovation

• joint modeling
• change point
• interval-censored event time
• multivariate longitudinal data
• causal inference