Multiple Frailty Model for Clustered Interval-Censored

• Activity Number: 357
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Chun Pan* and Bo Cai and Lianming Wang
• Companies: Novartis and University of South Carolina and University of South Carolina
• Keywords: frailty ; heterogeneity test ; interval-censored ; proportional hazards model ; semiparametric regression

Abstract

Interval-censored time-to-event data often occur in studies of diseases where the symptoms of interest are not directly observable but require lab examinations for detection. Furthermore, the independence assumption among observations may not be valid if they are from clusters. Some methods have been developed for analyzing clustered interval-censored data with a shared frailty to account for overall heterogeneity. In this paper, we propose a multiple frailty proportional hazards model, where we not only account for the baseline heterogeneity and effect variation across clusters for predictors, but also quantify the probabilities of the existence of such frailties. This proposed model will be especially useful for analyzing multi-center randomized clinical trials for HIV, infections, or progression-free survival in oncology studies.