Measurement error correction for logistic regression models with an "alloyed gold standard".
Measurement error correction for logistic regression models with an "alloyed gold standard".
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DOI：
10.1093/oxfordjournals.aje.a009089
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发表时间：
1997-01
影响因子：
5
通讯作者：
D. Spiegelman;S. Schneeweiss;A. McDermott
中科院分区：
文献类型：
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作者：
D. Spiegelman;S. Schneeweiss;A. McDermott
最近，一些作者质疑当用于获取测量误差过程信息的“黄金标准”本身不完善时，纠正测量误差和错误分类的相对风险估计的方法的有效性。当使用这种“合金”金标准来验证通常的暴露测量时，会导出“回归校准”（Rosner等人，Stat Med 1989；8：1051-69）中的偏差，用于根据逻辑回归模型估计的相对风险的测量误差校正因子。该量是“合金”金标准（X）和通常暴露评估方法（Z）与真实值的相关性、X和Z的方差之比以及“合金”金标准中的误差与通常暴露评估方法中的误差之间的相关性的函数。本文证明，如果Z和X之间的误差不相关，则即使金标准是“合金化”的，回归校准方法也没有偏差。当第三种暴露评估方法可用并且可以合理地假设该方法中的误差与其他两种暴露评估方法中的误差不相关时，得出X和Z中的误差之间的相关性的点估计和区间估计。这里通过体力活动、维生素 A 和 E、以及多不饱和脂肪和单不饱和脂肪的测量数据来说明这些方法。此外，当第三种暴露评估方法可用时，可以对标准回归校准进行修改，可用于计算相对风险的点和区间估计，并针对 X 和 Z 中的测量误差进行校正。此处使用健康专业人员随访研究的数据说明了这种新方法，该研究调查了体力活动与结肠癌发病率之间以及维生素 E 摄入量与冠心病之间的关联。结果表明，在这些示例中，X 和 Z 的误差相关性往往较小。即使是适度的，对 X 和 Z 中的误差进行修正后的相对风险估计与假设 X 是真正的黄金标准的估计也没有太大差异。
Recently, some authors have questioned the validity of methods which correct relative risk estimates for measurement error and misclassification when the "gold standard" used to obtain information about the measurement error process is itself imperfect. When such an "alloyed" gold standard is used to validate the usual exposure measurement, the bias in the "regression calibration" (Rosner et al., Stat Med 1989; 8:1051-69) measurement-error correction factor for relative risks estimated from logistic regression models is derived. This quantity is a function of the correlations of the "alloyed" gold standard (X) and the usual exposure assessment method (Z) with the truth, of the ratio of the variances of X and Z, and of the correlation between the errors in the "alloyed" gold standard and the errors in the usual exposure assessment method. In this paper, it is proven that if the errors between Z and X are uncorrelated, the regression calibration method has no bias even when the gold standard is "alloyed." When a third method of exposure assessment is available and it is reasonable to assume that the errors in this method are uncorrelated with the errors in the other two exposure assessment methods, point and interval estimates of the correlation between the errors in X and Z are derived. These methods are illustrated here with data on the measurement of physical activity, vitamins A and E, and poly- and monounsaturated fat. In addition, when a third exposure assessment method is available, a modification of standard regression calibration is derived which can be used to calculate point and interval estimates of relative risk that are corrected for measurement error in both X and Z. This new method is illustrated here with data from the Health Professionals Follow-up Study, a study investigating the associations between physical activity and colon cancer incidence and between vitamin E intake and coronary heart disease. It is shown that in these examples, correlations of the errors in X and Z tended to be small. Even when moderate, estimates of relative risk corrected for error in both X and Z were not very different from the estimates which assumed that X was a true gold standard.