Robust sandwich covariance estimation for regression calibration estimator in Cox regression with measurement error
Robust sandwich covariance estimation for regression calibration estimator in Cox regression with measurement error
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DOI：
10.1016/s0167-7152(99)00079-6
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发表时间：
1999-12-15
影响因子：
0.8
通讯作者：
Wang, CY
中科院分区：
文献类型：
--
作者：
Wang, CY
普伦蒂斯（1982）在考克斯回归中提出了协变量有误差时的回归校正估计。然而，这个估计量的协方差估计尚未在文献中讨论。回归校正估计量是在给定观测协变量的情况下，用未观测协变量的条件期望来代替未观测协变量。因此，可以将基于部分似然估计方程（例如S-plus中的coxreg函数）的标准考克斯（1972）回归程序应用于替换数据以进行参数估计。然而，基于标准考克斯回归程序的回归估计量的协方差估计可能导致偏倚估计。本文给出了一个简单的夹心式协方差估计公式。在可能错误指定的考克斯比例风险模型下，当重复测量可用于测量误差的协变量时，协方差估计量有效。这种方法在实践中很重要，因为它易于实现，并且如果误测协变量的风险比参数不大，则基于它的推断是有效的。从密集的模拟研究的结果。(C)1999 Elsevier Science B. V.保留所有权利。
Prentice (1982) proposed a regression calibration estimator in Cox regression when covariate variables are measured with error. However, estimation of the covariance of this estimator has not yet been discussed in the literature. The regression calibration estimator is to replace an unobserved covariate variable by its conditional expectation given observed covariate variables. Therefore, a standard Cox (1972) regression program based on the partial-likelihood estimating equation (such as the coxreg function in S-plus) may be applied to the replacement data for parameter estimation. However, covariance estimation of the regression estimator based on a standard Cox regression program may lead to bias estimation. This paper provides a simple sandwich formula for the covariance estimation. The covariance estimator is valid under a possibly misspecified Cox proportional hazards model when repeated measurements are available for the covariate that is measured with error. This method is important in practice since it is easy to implement, and inference based on it is valid if the hazard ratio parameter for the mismeasured covariate is not large. Results from intensive simulation studies are given. (C) 1999 Elsevier Science B.V. All rights reserved.