A0812
Title: Joint modeling of multivariate longitudinal data and recurrent events: Application to the urea cycle disorders study
Authors: DoHwan Park - Univ. ov Maryland -- Baltimore County (United States) [presenting]
Abstract: A joint modeling method is developed to analyze the bivariate longitudinal outcomes and time to recurrent events data. We combine the bivariate normal mixed effect model and the frailty model by including the multivariate normal random variables, which account for the dependence among the repeated measures and the dependence between two longitudinal outcomes and recurrent events. We use nonparametric maximum likelihood estimation (NPMLE) to estimate the parameters. EM algorithm was used to compute the NPMLEs and their variance estimators. The results from the simulation studies show that the NPMLEs are noticeably unbiased. The standard error estimators well reflect the true variations of the proposed estimators and the performance is better than individual models. Finally, we apply our procedure to analyzing data from the Urea Cycle Disorders study.
