Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. Regression calibration (RC) is a common approach to correct for bias in regression analysis with covariate measurement error. In survival analysis with covariate measurement error, it is well known that the RC estimator may be biased when the hazard is an exponential function of the covariates. In the paper, we investigate the RC estimator with general hazard functions, including exponential and linear functions of the covariates. When the hazard is a linear function of the covariates, we show that a risk set regression calibration (RRC) is consistent and robust to a working model for the calibration function. Under exponential hazard models, there is a trade-off between bias and efficiency when comparing RC and RRC. However, one surprising finding is that the trade-off between bias and efficiency in measurement error research is not seen under linear hazard when the unobserved covariate is from a uniform or normal distribution. Under this situation, the RRC estimator is in general slightly better than the RC estimator in terms of both bias and efficiency. The methods are applied to the Nutritional Biomarkers Study of the Women's Health Initiative. This article is protected by copyright. All rights reserved.

中文翻译：

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具有协变量测量误差的生存分析中一般风险模型的半参数回归校准；线性危险下的惊人表现


当未准确测量暴露情况时，观察流行病学研究经常面临估计暴露与疾病关系的问题。回归校准 (RC) 是纠正具有协变量测量误差的回归分析中的偏差的常用方法。在具有协变量测量误差的生存分析中，众所周知，当风险是协变量的指数函数时，RC 估计量可能会出现偏差。在本文中，我们研究了具有一般风险函数的 RC 估计器，包括协变量的指数函数和线性函数。当风险是协变量的线性函数时，我们表明风险集回归校准（RRC）对于校准函数的工作模型是一致且稳健的。在指数风险模型下，比较 RC 和 RRC 时，需要在偏差和效率之间进行权衡。然而，一个令人惊讶的发现是，当未观察到的协变量来自均匀或正态分布时，在线性风险下，测量误差研究中的偏差和效率之间的权衡不会出现。在这种情况下，RRC估计器在偏差和效率方面总体上略优于RC估计器。这些方法应用于妇女健康倡议的营养生物标志物研究。本文受版权保护。版权所有。