Semiparametric regression calibration for general hazard models in survival analysis with covariate measurement error; surprising performance under linear hazard.
Semiparametric regression calibration for general hazard models in survival analysis with covariate measurement error; surprising performance under linear hazard.
复制标题
一般危害模型的半参数回归校准在生存分析中具有协变量测量误差；线性危害下的表现令人惊讶。
作者：
Wang CY;Song X
观察性流行病学研究经常面临的问题是，当暴露量无法准确测量时，如何估计疾病与疾病之间的关系。回归校准（RC）是一种常见的方法来纠正回归分析中的偏差与协变量测量误差。在有协变量测量误差的生存分析中，当风险是协变量的指数函数时，RC估计量可能有偏。本文研究了一般风险函数下的RC估计，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，我们证明了风险集回归校准（RRC）对校准函数的工作模型是一致和鲁棒的。在指数风险模型下，当比较RC和RRC时，偏差和效率之间存在权衡。然而，一个令人惊讶的发现是，在测量误差研究中，当未观测协变量来自均匀分布或正态分布时，在线性风险下看不到偏差和效率之间的权衡。在这种情况下，RRC估计器在偏差和效率方面一般略优于RC估计器。该方法适用于妇女健康倡议的营养生物标志物研究。
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.
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影响因子：
5
作者：
Kipnis, V;Midthune, D;Carroll, RJ
通讯作者：
Carroll, RJ
DOI：
10.1111/1467-9868.00317
发表时间：
2001-01-01
影响因子：
5.8
作者：
Xie, SX;Wang, CY;Prentice, RL
通讯作者：
Prentice, RL
DOI：
10.1080/01621459.2014.896805
发表时间：
2014-12-01
影响因子：
3.7
作者：
Song X;Wang CY
通讯作者：
Wang CY
影响因子：
5
作者：
Kipnis, V;Subar, AF;Carroll, RJ
通讯作者：
Carroll, RJ
影响因子：
1.9
作者：
Sugar, Elizabeth A.;Wang, Ching-Yun;Prentice, Ross L.
通讯作者：
Prentice, Ross L.