Using Regression Calibration Equations That Combine Self-Reported Intake and Biomarker Measures to Obtain Unbiased Estimates and More Powerful Tests of Dietary Associations
Using Regression Calibration Equations That Combine Self-Reported Intake and Biomarker Measures to Obtain Unbiased Estimates and More Powerful Tests of Dietary Associations
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DOI：
10.1093/aje/kwr248
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发表时间：
2011-12-01
影响因子：
5
通讯作者：
Kipnis, Victor
中科院分区：
文献类型：
--
作者：
Freedman, Laurence S.;Midthune, Douglas;Kipnis, Victor
作者描述了一种结合自我报告和生物标志物的统计方法，在充分控制混杂因素的情况下，将提供饮食-疾病关联的几乎无偏估计和无关联零假设的有效检验。该方法是基于回归校准。在饮食-疾病关联由生物标志物介导的情况下，需要在介导模型中将该关联估计为总饮食效应。然而，无关联的假设最好通过边际模型进行检验，该模型包括回归校准估计摄入量作为暴露量，但不包括生物标志物。作者用来自类胡萝卜素和眼相关疾病研究（2001-2004）的数据说明了该方法，并表明在回归校准估计摄入量中包含生物标志物增加了统计功效。这一发展揭示了以前的饮食疾病协会在文献中报道的分析。
The authors describe a statistical method of combining self-reports and biomarkers that, with adequate control for confounding, will provide nearly unbiased estimates of diet-disease associations and a valid test of the null hypothesis of no association. The method is based on regression calibration. In cases in which the diet-disease association is mediated by the biomarker, the association needs to be estimated as the total dietary effect in a mediation model. However, the hypothesis of no association is best tested through a marginal model that includes as the exposure the regression calibration-estimated intake but not the biomarker. The authors illustrate the method with data from the Carotenoids and Age-Related Eye Disease Study (2001-2004) and show that inclusion of the biomarker in the regression calibration-estimated intake increases the statistical power. This development sheds light on previous analyses of diet-disease associations reported in the literature.