The estimation of direct and indirect causal effects in the presence of misclassified binary mediator

Mediation analysis serves to quantify the effect of an exposure on an outcome mediated by a certain intermediate and to quantify the extent to which the effect is direct. When the mediator is misclassified, the validity of mediation analysis can be severely undermined. The contribution of the present work is to study the effects of non-differential misclassification of a binary mediator in the estimation of direct and indirect causal effects when the outcome is either continuous or binary and exposure-mediator interaction can be present, and to allow the correction of misclassification. A hybrid of likelihood-based and predictive value weighting method for misclassification correction coupled with sensitivity analysis is proposed and a second approach using the expectation-maximization algorithm is developed. The correction strategy requires knowledge of a plausible range of sensitivity and specificity parameters. The approaches are applied to a perinatal epidemiological study of the determinants of pre-term birth.