Semiparametric Odds Ratio Model for Multivariate Survival Times

• Activity Number: 135 - Novel Non/Semiparametric Developments for Risk Perception with Censored and/or Missing Data
• Type: Invited
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Risk Analysis
• Author(s): Hua Yun Chen*
• Companies: University of Illinois at Chicago
• Keywords: Bivariate survival distribution ; Case-control sample ; Copula model ; Plackett distribution ; Semiparametric likelihood

Abstract

We propose an odds ratio model for the analysis of multivariate survival times. In comparison with the existing models for multivariate survival times such as the Clayton-Oakes model, the proposed model is more flexible in modeling the relationship of multivariate survival times. Like the Clayton-Oakes model, the parameter in the model has natural interpretations, and the association parameters can be estimated from the case-control data. We study the relationship of the proposed model with the general copula model and propose the maximum likelihood approach for estimation and inference on the model parameters. The properties of the maximum likelihood estimator are studied. Simulation studies are conducted to assess the performance of the proposed method for inference. The proposed model is applied to a real data example.