Applied causal inference methods for sequential mediators
Abstract Background: Mediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the exposure and the outcome. The total effect of the exposure on the outcome can be decomposed into an indirect effect, i.e. the effect explained by the mediators jointly, and a direct effect, i.e. the effect unexplained by the mediators. However finer decompositions are possible in presence of independent or sequential mediators. Methods: We review four statistical methods to analyse multiple sequential mediators , the inverse odds ratio weighting approach, the inverse probability weighting approach, the imputation approach and the extended imputation approach. These approaches are compared and implemented using a case-study with the aim to investigate the role of adverse reproductive outcomes and infant respiratory infections on infant wheezing in the Ninfea birth cohort. Results: Using the inverse odds ratio weighting approach, the direct effect of maternal depression or anxiety in pregnancy is equal to a 59% (95% CI: 27%-94%) increased prevalence of infant wheezing and the mediated effect through adverse reproductive outcomes is equal to a 3% (95% CI:-6%-12%) increased prevalence of infant wheezing. When including infant lower respiratory infections in the mediation pathway, the direct effect decreases to 57% (95% CI: 25%-92%) and the indirect effect increases to 5% (95% CI:-5%,15%). The estimates of the effects obtained using the weighting and the imputation approaches are similar. The extended imputation approach suggests that the small joint indirect effect through adverse reproductive outcomes and lower respiratory infections is due entirely to the contribution of infant lower respiratory infections, independently from the increased prevalence of adverse reproductive outcomes. Conclusions: The use of these methods allows the study of multiple mechanisms underlying the association between an exposure and an outcome and provides a solution for the problem of intermediate confounding by considering the intermediate confounder as a sequential mediator. The choice of the method may depend on what is the effect of main interest, the nature of the variables involved in the analysis and the truthfulness of the underlying assumptions.