Causal effect estimation in the presence of misclassified binary mediators

Causal 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 causal effect 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. An expectation-maximization algorithm is presented to estimate the model and open-source software is provided to implement the proposed methods. We apply our misclassification correction strategies to investigate the mediating role of gestational hypertension on the association between maternal age and preterm birth.

Expectation-maximization algorithm

Gestational hypertension

Measurement error

Preterm birth

Sensitivity analysis