Abstract:
:In large cohort studies, it often happens that some covariates are expensive to measure and hence only measured on a validation set. On the other hand, relatively cheap but error-prone measurements of the covariates are available for all subjects. Regression calibration (RC) estimation method (Prentice, 1982, Biometrika 69, 331-342) is a popular method for analyzing such data and has been applied to the Cox model by Wang et al. (1997, Biometrics 53, 131-145) under normal measurement error and rare disease assumptions. In this article, we consider the RC estimation method for the semiparametric accelerated failure time model with covariates subject to measurement error. Asymptotic properties of the proposed method are investigated under a two-phase sampling scheme for validation data that are selected via stratified random sampling, resulting in neither independent nor identically distributed observations. We show that the estimates converge to some well-defined parameters. In particular, unbiased estimation is feasible under additive normal measurement error models for normal covariates and under Berkson error models. The proposed method performs well in finite-sample simulation studies. We also apply the proposed method to a depression mortality study.
journal_name
Biometricsjournal_title
Biometricsauthors
Yu M,Nan Bdoi
10.1111/j.1541-0420.2009.01295.xsubject
Has Abstractpub_date
2010-06-01 00:00:00pages
405-14issue
2eissn
0006-341Xissn
1541-0420pii
BIOM1295journal_volume
66pub_type
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