THE EFFECTS OF MEASUREMENT ERRORS ON RELATIVE RISK REGRESSIONS
THE EFFECTS OF MEASUREMENT ERRORS ON RELATIVE RISK REGRESSIONS
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DOI：
10.1093/oxfordjournals.aje.a115761
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发表时间：
1990-12-01
影响因子：
5
通讯作者：
ARMSTRONG, BG
中科院分区：
文献类型：
--
作者：
ARMSTRONG, BG
本文研究了相对风险回归中风险因子（协变量）数值测量的随机误差的影响。当不依赖于结果（无差异）时，这种误差通常会削弱相对风险估计（将它们移向1），并导致虚假的狭窄置信区间。测量误差的存在也降低了估计的精度和显著性检验的功效。然而，通过使用近似测量值获得的显著性水平通常是有效的，并且在给定测量误差的情况下尽可能强大。风险估计的衰减不仅取决于大小（方差）的计量误差，而且还取决于其分布形式，取决于它是否取决于真实水平的风险因素（是否为“Berkson”型）、风险因素真实水平的方差和分布形式、回归的函数形式（指数或线性），以及模型中包含的混杂变量。测量混杂变量的误差会导致对混杂变量失去控制，留下剩余偏倚。在统计学文献中，有一种简单的方法，可以用来修正假设正态分布的简单模型中测量误差的影响。对于这些校正，需要有关测量误差方差的信息。已经提出了一些适合于更一般模型的方法，但这些方法对于常规应用来说似乎还不够成熟。
This paper concerns the effects of random error in numerical measurements of risk factors (covariates) in relative risk regressions. When not dependent on outcome (nondifferential), such error usually attenuates relative risk estimates (shifts them toward one) and leads to spuriously narrow confidence intervals. The presence of measurement error also reduces precision of estimates and power of significance tests. However, significance levels obtained by using the approximate measurements are usually valid and as powerful as possible given the measurement error. The attenuation in risk estimate depends not only on the size (variance) of the measurement error, but also on its distributional form, on whether it is dependent on the true level of the risk factor (whether it is of "Berkson" type), on the variance and distributional form of true levels of the risk factor, on the functional form of the regression (exponential or linear), and on the confounding variables included in the model. Error in measuring confounding variables leads to loss of control of confounding, leaving residual bias. Uncomplicated technique of correcting the effects of measurement error in simple models in which distributions are assumed normal are available in the statistical literature. For these corrections, information on measurement error variance is required. Some approaches appropriate for more general models have been proposed, but these appear to be insufficiently developed for routine application.