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

Thomas M. Menino Convention & Exhibition Center 

We develop a joint model of multivariate longitudinal biomarkers with a change point at an interval-censored event time. Our model allows us to simultaneously understand the causal effect of longitudinal biomarkers on the event time and the causal effect of event time on the changes of longitudinal biomarkers post the event. A simulation study is carried out to demonstrate the satisfactory finite-sample performance of the proposed method for making inferences. Finally, the method is applied to PREDICT-HD data from a multisite observational cohort study of prodromal Huntington's disease individuals to ascertain the effects of cognitive impairments on the onset of Huntington's disease that subjects to interval censoring and how the disease onset accelerates the cognitive impairments.

Joint Model

Survival Analysis

Longitudinal Biomarker

Change Point

Interval-censored Data