Smoothed and Corrected Score Approach to Censored Quantile Regression With Measurement Errors
Smoothed and Corrected Score Approach to Censored Quantile Regression With Measurement Errors
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DOI：
10.1080/01621459.2014.989323
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发表时间：
2015-01
影响因子：
3.7
通讯作者：
Yuanshan Wu;Yanyuan Ma;G. Yin
中科院分区：
文献类型：
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作者：
Yuanshan Wu;Yanyuan Ma;G. Yin
删失分位数回归是Cox比例风险模型在生存分析中的一种重要替代方法。与通常的中心协变量效应相比，分位数回归能够有效地刻画生存时间不同分位数的协变量效应。当协变量被测量有误差时，众所周知，天真地将被错误测量的协变量视为无误差将导致估计偏差。在删失分位数回归下，我们提出了平滑和修正的估计方程，以获得相容的估计。我们建立了分位数回归系数估计的相合性和渐近正态。与朴素估计器相比，该方法可以消除各种测量误差分布和模型误差分布下的估计偏差。我们进行了模拟研究，以检验新方法的有限样本特性，并将其应用于肺癌研究。这篇文章的补充材料可以在网上找到。
Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose smoothed and corrected estimating equations to obtain consistent estimators. We establish consistency and asymptotic normality for the proposed estimators of quantile regression coefficients. Compared with the naive estimator, the proposed method can eliminate the estimation bias under various measurement error distributions and model error distributions. We conduct simulation studies to examine the finite-sample properties of the new method and apply it to a lung cancer study. Supplementary materials for this article are available online.