Introduction: In this article, we introduce Marginal Structural Models, which yield unbiased estimates of causal effects of exposures in the presence of time-varying confounding variables that also act as mediators.
Objectives: We describe estimation via inverse probability weighting; estimation may also be accomplished by g-computation (Robins in Latent Variable Modeling and Applications to Causality, Springer, New York, pp 69-117, 1997; van der Wal et al. in Stat Med 28:2325-2337, 2009) or targeted maximum likelihood (Rosenblum and van der Laan in Int J Biostat 6, 2010).
Conclusions: When both time-varying confounding and mediation are present in a longitudinal setting data, Marginal Structural Models are a useful tool that provides unbiased estimates.