Effect estimation in the presence of a misclassified binary mediator

TL;DR

This paper introduces methods to correct bias in mediation analysis caused by misclassified binary mediators, especially when misclassification is differential and no gold standard labels are available.

Contribution

It proposes three novel correction methods leveraging related variables to recover unbiased estimates without gold standard labels, under a key sensitivity assumption.

Findings

Corrected estimates reduce bias in mediation analysis.

Methods perform well in simulations with differential misclassification.

Application to real data reveals mediating effects more accurately.

Abstract

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.