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PubMed · 10955408

Marginal structural models and causal inference in epidemiology.

J M Robins·M A Hernán·B Brumback
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Abstract

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.

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Anti-HIV AgentsCausalityConfounding Factors, EpidemiologicEpidemiologic MethodsHIV InfectionsHumansModels, StatisticalRisk FactorsTime FactorsZidovudineJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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J M Robins, M A Hernán, B Brumback. 2000. Marginal structural models and causal inference in epidemiology.. https://doi.org/10.1097/00001648-200009000-00011

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