Controlling for time-dependent confounding using marginal structural models

Zoe Fewell (), Frederick Wolfe (), Hyon Choi (), Miguel A. Hernán (), Kate Tilling () and Jonathan A. C. Sterne ()
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Zoe Fewell: University of Bristol
Frederick Wolfe: National Data Bank for Rheumatic Diseases
Hyon Choi: Harvard Medical School
Miguel A. Hernán: Harvard School of Public Health
Kate Tilling: University of Bristol
Jonathan A. C. Sterne: University of Bristol

Stata Journal, 2004, vol. 4, issue 4, 402-420

Abstract: Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time have the potential to allow causal inferences about the effects of exposure on outcome. There is particular interest in estimating the causal effects of medical treatments (or other interventions) in circumstances in which a randomized controlled trial is difficult or impossible. However, standard methods for estimating exposure effects in longitudinal studies are biased in the presence of time-dependent confounders affected by prior treatment. This article describes the use of marginal structural models (described by Robins, Hernán, and Brumback [2000]) to estimate exposure or treatment effects in the presence of time-dependent confounders affected by prior treatment. The method is based on deriving inverse-probability-of-treatment weights, which are then used in a pooled logistic regression model to estimate the causal effect of treatment on outcome. We demonstrate the use of marginal structural models to estimate the effect of methotrexate on mortality in persons suffering from rheumatoid arthritis. Copyright 2004 by StataCorp LP.

Keywords: marginal structural models; causal models; weighted regression; survival analysis; logistic regression; confounding (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (20)

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