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B0374
Title: Measure, model and estimation on the dependence structure of bivariate recurrent event processes Authors:  Jing Ning - The University of Texas MD Anderson Cancer Center (United States) [presenting]
Abstract: Bivariate or multivariate recurrent event processes are often encountered in longitudinal studies in which more than one type of event is of interest. There has been much research on regression analysis for such data, but little has been done to measure and model the dependence between recurrent event processes. We propose a time-dependent measure, termed the rate ratio, to assess the local dependence between two types of recurrent event processes, and then extend it to allow for covariate adjustments. A two-level semiparametric regression model is proposed for jointly modeling the frequency and dependence of bivariate recurrent events: the first level is a proportional rates model for the marginal rates, and the second level is a proportional rate ratio model for the dependence structure. The proposed models and methods are illustrated by a soft tissue sarcoma study to examine the effects of the initial treatments on the marginal frequencies of local/distant sarcoma recurrence and dependence structure between two types of recurrence.