A method to automate probabilistic sensitivity analyses of misclassified binary variables
A method to automate probabilistic sensitivity analyses of misclassified binary variables
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DOI：
10.1093/ije/dyi184
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发表时间：
2005-12-01
影响因子：
7.7
通讯作者：
Greenland, S
中科院分区：
文献类型：
--
作者：
Fox, MP;Lash, TL;Greenland, S
背景大多数研究中都存在错误分类偏差，但其幅度或方向的不确定性很少被量化。方法作者提出了一种概率敏感性分析方法，以量化二分结果、暴露或协变量错误分类的可能影响。该方法涉及在给定分类的敏感性和特异性的情况下重建如果错误分类的变量被正确分类则观察到的数据。随附的 SAS 宏实现了该方法，并允许用户指定错误分类参数的灵敏度和特异性范围，以产生包含系统误差和随机误差的模拟区间。 结果 作者通过将其应用于职业树脂暴露与肺癌死亡之间关系的研究来说明该方法和随附的 SAS 宏代码。作者将使用这种方法的结果与仅考虑随机误差的传统结果以及原始敏感性分析结果进行了比较。结论通过考虑错误分类的合理程度，研究人员可以以包含错误分类导致的偏差的不确定性的方式呈现研究结果，从而避免误导性的精确结果。
Background Misclassification bias is present in most studies, yet uncertainty about its magnitude or direction is rarely quantified.Methods The authors present a method for probabilistic sensitivity analysis to quantify likely effects of misclassification of a dichotomous outcome, exposure or covariate. This method involves reconstructing the data that would have been observed had the misclassified variable been correctly classified, given the sensitivity and specificity of classification. The accompanying SAS macro implements the method and allows users to specify ranges of sensitivity and specificity of misclassification parameters to yield simulation intervals that incorporate both systematic and random error.Results The authors illustrate the method and the accompanying SAS macro code by applying it to a study of the relation between occupational resin exposure and lung-cancer deaths. The authors compare the results using this method with the conventional result, which accounts for random error only, and with the original sensitivity analysis results.Conclusion By accounting for plausible degrees of misclassification, investigators can present study results in a way that incorporates uncertainty about the bias due to misclassification, and so avoid misleadingly precise-looking results.