Semiparametric Regression Analysis of Survival Data with Dependent Interval Censoring

• Activity Number: 131
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: International Chinese Statistical Association
• Author(s): Chyong-Mei Chen*
• Companies: Providence University
• Keywords: Dependent interval censoring ; Frailty ; Recurrent event data ; Self-consistency ; Survival data

Abstract

Interval-censored failure-time data arise when subjects are examined or observed periodically such that the failure time of interest is not examined exactly but only known to be bracketed between two adjacent observation times. The commonly used approaches assume that the examination times and the failure time are independent or conditionally independent given covariates. However, in many practical applications, subjects often selectively miss their visits or return at non-scheduled times according to symptoms of diseases. Hence, the frequency and timing of visits may be informative with respect to the failure time, which results in dependent interval censored data. To deal with dependent censoring, we characterize the examination process as recurrent event process and propose a joint frailty model to account for the association. By considering examination process as a recurrent event, we incorporate generally irregular observations. We propose a semiparametric maximum likelihood approach for estimating model parameters and show the consistency of the proposed estimators. Extensive simulation studies and real data analysis are conducted for investigating finite-sample properties.