Joint Analysis of Multivariate Longitudinal Data and Interval-Censored Event Time Data with Application to Huntington’s Disease Progression

Huntington's disease is an autosomal dominant neurodegenerative disorder, characterized by motor dysfunction, psychiatric disturbances, and cognitive decline. The onset of Huntington's disease is diagnosed by severe motor impairment, which can be predicted by cognitive decline and may also worsen cognitive impairment. However, clinical data are often collected at discrete times, and therefore, disease onset is subject to interval censoring. We develop a joint model of multivariate longitudinal biomarkers with a change point anchored at an interval-censored event time. Our model allows us to simultaneously study the effect of longitudinal biomarkers on the event time and the changes in trajectories of the longitudinal biomarkers post the event. We conduct a comprehensive simulation study to demonstrate the satisfactory finite-sample performance of the proposed method for making inferences. We applied the method to the PREDICT-HD data from a multisite observational cohort study of prodromal Huntington’s disease individuals to ascertain how cognitive impairment and motor dysfunction interact during disease progression.