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Marginal Structural Models and Causal Inference in Epidemiology
James M. Robins, Miguel Ángel Hernán and Babette Brumback
Vol. 11, No. 5 (Sep., 2000), pp. 550-560
Published by: Lippincott Williams & Wilkins
Stable URL: http://www.jstor.org/stable/3703997
Page Count: 11
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In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-probability-of-treatment weighted estimators.
Epidemiology © 2000 Lippincott Williams & Wilkins