Abstract
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.
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Acknowledgments
Both authors acknowledge funding from the Natural Sciences and Research Council of Canada. Moodie also acknowledges funding from the Canadian Institutes of Health Research.
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Moodie, E.E.M., Stephens, D.A. Marginal Structural Models: unbiased estimation for longitudinal studies. Int J Public Health 56, 117–119 (2011). https://doi.org/10.1007/s00038-010-0198-4
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DOI: https://doi.org/10.1007/s00038-010-0198-4