Brenner H
Department of Medical Sociology, University of Ulm, Germany.
Epidemiology. 1992 May;3(3):216-22. doi: 10.1097/00001648-199205000-00006.
Recently, several authors have encouraged the use of dual measurement strategies to correct for bias due to nondifferential misclassification in epidemiologic research. Among these, latent class techniques, which give unbiased results if both measurements are independent conditional on the true value, have become most popular. In practice, however, measurement errors are usually more likely to be positively correlated, and hence reliability studies cannot replace studies of validity. I offer here a quantitative illustration and a comparison of the performance of latent class approaches and other dual response strategies in situations of positive error covariance. I conclude that, except under very special circumstances, dual measurement strategies are likely to remove only part, if any, of the effect attenuation due to nondifferential exposure misclassification. Under certain conditions, it may make sense, however, to estimate a "minimum" effect of the exposure on the basis of the strongest association found in various dual and single measurement strategies.
最近，几位作者鼓励在流行病学研究中使用双重测量策略来校正因非差异性错误分类导致的偏差。其中，潜在类别技术如果两个测量值在真实值条件下都是独立的，就能给出无偏结果，该技术已变得最为流行。然而在实际中，测量误差通常更可能呈正相关，因此可靠性研究无法替代有效性研究。在此，我给出一个定量说明，并比较正误差协方差情况下潜在类别方法和其他双重反应策略的性能。我得出结论，除了在非常特殊的情况下，双重测量策略可能只会消除部分（如果有的话）因非差异性暴露错误分类导致的效应衰减。不过，在某些条件下，基于各种双重和单一测量策略中发现的最强关联来估计暴露的“最小”效应可能是有意义的。
