Asymptotic theory for the semiparametric accelerated failure time model with missing data
Bin Nan, John D. Kalbfleisch, and Menggang Yu
Source: Ann. Statist. Volume 37, Number 5A
(2009), 2351-2376.
Abstract
We consider a class of doubly weighted rank-based estimating methods for the transformation (or accelerated failure time) model with missing data as arise, for example, in case-cohort studies. The weights considered may not be predictable as required in a martingale stochastic process formulation. We treat the general problem as a semiparametric estimating equation problem and provide proofs of asymptotic properties for the weighted estimators, with either true weights or estimated weights, by using empirical process theory where martingale theory may fail. Simulations show that the outcome-dependent weighted method works well for finite samples in case-cohort studies and improves efficiency compared to methods based on predictable weights. Further, it is seen that the method is even more efficient when estimated weights are used, as is commonly the case in the missing data literature. The Gehan censored data Wilcoxon weights are found to be surprisingly efficient in a wide class of problems.
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Keywords: Accelerated failure time model; case-cohort study; censored linear regression; Donsker class; empirical processes; Glivenko–Cantelli class; pseudo Z-estimator; nonpredictable weights; rank estimating equation; semiparametric method
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Permanent link to this document: http://projecteuclid.org/euclid.aos/1247663758
Digital Object Identifier: doi:10.1214/08-AOS657
Zentralblatt MATH identifier: 05596904
Mathematical Reviews number (MathSciNet): MR2543695
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