Semiparametric Estimation of the Accelerated Failure Time Model with Partly Interval-Censored Data

• Activity Number: 250
• Type: Contributed
• Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Fei Gao* and Donglin Zeng and Danyu Lin
• Companies: and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
• Keywords: Buckley-James estimator ; Kernel estimation ; One-step estimator ; Resampling ; Self-consistency ; Semiparametric efficiency

Abstract

Partly interval-censored (PIC) data arise when some failure times are exactly observed while others are only known to lie within certain intervals. In this paper, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with PIC data. We first generalize the Buckley and James (1979) estimator for right-censored data to PIC data. Then, we develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We show that, under mild regularity conditions, the generalized Buckley-James estimator is consistent and asymptotically normal, and the one-step estimator achieves the semiparametric efficiency bound. We conduct extensive sim- ulation studies to examine the performance of the proposed estimators in finite samples and apply our methods to data derived from an AIDS study.