Quantile Regression for Recurrent Gap Time Data

• Activity Number: 253
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Xianghua Luo*+ and Chiung-Yu Huang and Lan Wang
• Companies: University of Minnesota and National Institute of Allergy and Infectious Diseases and University of Minnesota
• Keywords: Bootstrap ; Clustered survival times ; Data perturbation ; Recurrent events ; Within-cluster resampling

Abstract

Evaluating covariate effects on gap times between successive recurrent events is of interest in many medical and public health studies. While most existing methods for recurrent gap time analysis focus on modeling the hazard function of gap times, a direct interpretation of the covariate effects on the gap times is not available through these methods. In this paper, we consider quantile regression that can provide direct assessment of covariate effects on the quantiles of the gap time distribution. Following the spirit of the weighted risk-set method by Luo and Huang (2011, Statistics in Medicine 30, 301-311), we extend the martingale-based estimating equation method proposed by Peng and Huang (2008, Journal of American Statistical Association 103, 637-649) for univariate survival data to recurrent gap time data. The proposed estimation procedure can be easily implemented in existing software for univariate censored quantile regression. Uniform consistency and weak convergence of the proposed estimators are established. Monte-Carlo studies demonstrate the effectiveness of the proposed method. An application to data from the Danish Psychiatric Central Register is presented.