Regression calibration in studies with correlated variables measured with error
Regression calibration in studies with correlated variables measured with error
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DOI：
10.1093/aje/154.9.836
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发表时间：
2001-11-01
影响因子：
5
通讯作者：
Stram, DO
中科院分区：
文献类型：
--
作者：
Fraser, GE;Stram, DO
回归校准是一种在暴露变量测量有误差的情况下校正回归结果偏差的技术。假设存在校准子研究，其中通过第二次回归分析将准确和粗略的测量方法联系起来。在多变量分析中，测量误差的代价是统计功效的损失。在本文中，校准数据从加州基督复临安息日会被用来模拟研究人群和新的校准研究。应用回归校准逻辑分析，作者估计了营养变量对的功效。结果表明，大量的权力损失，如果测量误差的变量是强相关的。在未进行回归校准的情况下，估计效应的偏差可能很大，并通过回归校准进行校正。当真实系数为零时，原油分析中相应的系数通常会有一个非零的期望值。然后，第一类错误概率不是名义上的，统计显著性的错误外观很容易发生，特别是在大型研究中。使用回归校准的功效的主要决定因素是测量误差的变量之间的共线性以及原始变量和相应真实变量之间的相关性大小。如果存在重要的共线性，则校准研究规模高达1，000例受试者时，功效会增加。
Regression calibration is a technique that corrects biases in regression results in situations where exposure variables are measured with error. The existence of a calibration substudy, where accurate and crude measurement methods are related by a second regression analysis, is assumed. The cost of measurement error in multivariate analyses is loss of statistical power. In this paper, calibration data from California Seventh-day Adventists are used to simulate study populations and new calibration studies. Applying regression calibration logistic analyses, the authors estimate power for pairs of nutritional variables. The results demonstrate substantial loss of power if variables measured with error are strongly correlated. Biases in estimated effects in cases where regression calibration is not performed can be large and are corrected by regression calibration. When the true coefficient has zero value, the corresponding coefficient in a crude analysis will usually have a nonzero expected value. Then type I error probabilities are not nominal, and the erroneous appearance of statistical significance can readily occur, particularly in large studies. Major determinants of power with use of regression calibration are collinearity between the variables measured with error and the size of correlations between crude and corresponding true variables. Where there is important collinearity, useful gains in power accrue with calibration study size up to 1,000 subjects.