Modeling the Dependence Structure for Correlated Survival Data with Covariate

• Activity Number: 210
• Type: Invited
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Bin Nan*+ and Tianle Hu and Xihong Lin and James Robins
• Companies: University of Michigan and Eli Lilly and Company and Harvard University and Harvard University

Abstract

Cross-ratio is an important local measure of the strength of dependence among correlated failure times. When covariate exists, it is of scientific interest to understand how the cross-ratio varies with the covariate as well as time components. Motivated by the Cox model, we propose a proportional cross-ratio model through a baseline cross-ratio function and a multiplicative covariate effect. Assuming a parametric model for the baseline cross-ratio, we propose a local pseudo-partial likelihood approach to estimate jointly the baseline cross-ratio and the covariate effect. We show that the proposed parameter estimator is consistent and asymptotically normal. The performance of the proposed technique in finite samples is examined using simulation studies. In addition, the proposed method is applied to the menstrual cycle data from the Tremin study as well as the Australian twin data.