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. In this context, the total effect of the exposure on the outcome can be decomposed into the natural indirect effect, i.e. the effect explained by the mediators jointly, and the natural direct effect, i.e. the effect unexplained by the mediators. However finer decompositions are also possible in presence of independent or sequential mediators. As sequential mediation analysis is increasingly common in epidemiology, applied researchers have to interface with difficulties related to the application, implementation, and interpretation of the methods pro- posed in literature. We review four statistical methods to analyse multiple sequential mediators, all based on the counterfactual framework: the inverse odds ratio weight- ing approach, the inverse probability weighting approach, the imputation approach and the extended imputation approach. These approaches are described, 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.