Semiparametric Modeling of Bivariate Recurrent Events

• Activity Number: 499
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Jing Yang* and Limin Peng
• Companies: Emory University and Emory University
• Keywords: bivariate recurrent events processes ; dynamic association

Abstract

Recurrent events are frequently observed in biomedical studies, and often they consist of more than one type of events of interests. Marginal analysis of each type of recurrent event is useful but cannot address questions on the relationship between different types of recurrent events. In this work, we study a dynamic association model that extends a recently developed quantile association model. Our estimating equations are constructed based on the stochastic processes embedded with bivariate recurrent events data. The proposed estimation can be implemented by an efficient and stable algorithm. We investigate the asymptotic properties of the proposed estimator, and develop proper inference procedures. Our proposals are illustrated via simulation studies and an application to a registry dataset.