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摘要
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The Accelerated Failure Time (AFT) model is a widely used framework in survival
analysis, providing an intuitive interpretation of the effects of covariates on loglifetime. This paper addresses the estimation problem when covariates are subject to
measurement error. We begin by correcting the bias in the estimating function using
the traditional approach, assuming no censoring, and then extend the method by
computing its conditional expectation given the censoring indicator, in line with the
Buckley–James estimation framework. However, the computation requires estimating
the distribution of the adjusted lifetime. Since measurement error induces dependence
between the adjusted lifetime and censoring time, we propose employing Beran’s
estimator to address this complication. Simulation results demonstrate that the
proposed method outperforms conventional regression calibration and remains
consistent under completely random censoring. |
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