Regression Calibration in Semiparametric Accelerated Failure Time Models |
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Authors: | Menggang Yu Bin Nan |
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Affiliation: | 1. Department of Medicine, Division of Biostatistics, Indiana University School of Medicine, 410 West 10th Street, Suite 3000, Indianapolis, Indiana 46202, USA;2. Department of Biostatistics, University of Michigan, Ann Arbor, 1420 Washington Heights, Ann Arbor, Michigan 48109, USA |
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Abstract: | Summary 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. |
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Keywords: | Accelerated failure time model Measurement error Regression calibration Two‐phase sampling Validation data |
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