Mediation analyses allow researchers to quantify the effect of an exposure variable on an outcome variable through a mediator variable. If a binary mediator variable is misclassified, the resulting analysis can be severely biased. Misclassification is especially difficult to deal with when it is differential and when there are no gold standard labels available. Previous work has addressed this problem using a sensitivity analysis framework or by assuming that misclassification rates are known. We leverage a variable related to the misclassification mechanism to recover unbiased parameter estimates without using gold standard labels. The proposed methods require the reasonable assumption that the sum of the sensitivity and specificity is greater than 1. Three correction methods are presented: (1) an ordinary least squares correction for Normal outcome models, (2) a multi-step predictive value weighting method, and (3) a seamless expectation-maximization algorithm. We apply our misclassification correction strategies to investigate the mediating role of gestational hypertension on the association between maternal age and pre-term birth.

翻译：中介分析使研究者能够通过中介变量量化暴露变量对结果变量的影响。若二元中介变量存在误分类，所得分析结果可能产生严重偏倚。当误分类为差异性误分类且无金标准标签可用时，处理这一问题尤为困难。先前研究通过敏感性分析框架或假设误分类率已知来解决此问题。本文利用与误分类机制相关的变量，在不使用金标准标签的情况下恢复无偏参数估计。所提方法需要满足敏感性加特异性之和大于1的合理假设。我们提出了三种校正方法：(1) 针对正态结果模型的普通最小二乘校正，(2) 多步预测值加权法，以及(3) 无缝期望最大化算法。我们将误分类校正策略应用于研究妊娠期高血压在产妇年龄与早产关联中的中介作用。