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

TL;DR

This paper introduces a joint modeling approach for multivariate longitudinal data with a change point at interval-censored event times, specifically applied to Huntington's disease progression, enabling better understanding of biomarker and symptom interactions.

Contribution

It develops a novel joint model that handles multivariate longitudinal biomarkers with a change point at interval-censored event times, advancing analysis of disease progression data.

Findings

Model accurately captures biomarker changes post-event

Method demonstrates good finite-sample performance in simulations

Application reveals interactions between cognition and motor decline in HD

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.