Direct effects in mediation analysis quantify the effect of an exposure on an outcome not mediated by a certain intermediate. When estimating direct effects through measured data, misclassification may occur in the outcomes, exposures, and mediators. In mediation analysis, any such misclassification may lead to biased estimates in the direct effects. Basing on the conditional dependence between the mismeasured variable and other variables given the true variable, misclassification mechanisms can be divided into non-differential misclassification and differential misclassification. In this article, several scenarios of differential misclassification will be discussed and sensitivity analysis results on direct effects will be derived for those eligible scenarios. According to our findings, the estimated direct effects are not necessarily biased in intuitively predictable directions when the misclassification is differential. The bounds of the true effects are functions of measured effects and sensitivity parameters. An example from the 2018 NCHS data will illustrate how to conduct sensitivity analyses with our results on misclassified outcomes, gestational hypertension and eclampsia, when the exposure is Hispanic women versus non-Hispanic White women and the mediator is weights gain during pregnancy.

中文翻译：

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结果，暴露和调解者在分类错误分类下的直接作用

调解分析中的直接作用可量化暴露对未由某种中间体介导的结果的影响。通过测得的数据估算直接影响时，结果，暴露和调解者可能会发生错误分类。在调解分析中，任何此类错误分类都可能导致直接影响的估计偏差。根据错误度量的变量与给定真实变量的其他变量之间的条件依赖性，错误分类机制可以分为非差分错误分类和差分错误分类。在本文中，将讨论几种差异分类错误的情况，并针对那些符合条件的情况得出对直接影响的敏感性分析结果。根据我们的发现，当错误分类是微分的时，估计的直接影响不一定会在直观上可预测的方向上产生偏差。真实效果的界限是所测效果和灵敏度参数的函数。2018年NCHS数据中的一个例子将说明当暴露于西班牙裔女性与非西班牙裔白人女性之间并且调解是怀孕期间体重增加时，如何对我们分类错误的结局，妊娠高血压和子痫的结果进行敏感性分析。