Statistical Methods for Causal Mediation Analysis

Abstract

Mediation analysis is a popular approach in the social an biomedical sciences to examine the extent to which the effect of an exposure on an outcome is through an intermediate variable (mediator) and the extent to which the effect is direct. We first develop statistical methods and software for the estimation of direct and indirect causal effects in generalized linear models when exposure-mediator interaction may be present. We then study the bias of direct and indirect effects estimators that arise in this context when a continuous mediator is measured with error or a binary mediator is misclassified. We develop methods of correction for measurement error and misclassification coupled with sensitivity analyses for which no auxiliary information on the mediator measured with error is needed. The proposed methods are applied to a lung cancer study to evaluate the effect of genetic variants mediated through smoking on lung cancer risk and to a perinatal epidemiological study on the determinants of preterm birth.