Controlling for time-dependent confounding using marginal structural models

Zoe Fewell (), M. A. Hernan, F. Wolfe, K. Tilling, H. Choi and J. A. C. Sterne
Additional contact information
Zoe Fewell: Department of Social Medicine, University of Bristol
M. A. Hernan: Harvard School of Public Health
F. Wolfe: National Data Bank for Rheumatic Diseases, USA
K. Tilling: Department of Social Medicine, University of Bristol
H. Choi: Harvard Medical School
J. A. C. Sterne: Department of Social Medicine, University of Bristol

United Kingdom Stata Users' Group Meetings 2004 from Stata Users Group

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 randomised 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 talk describes the use of marginal structural models (described by Robins et al.) 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.

Date: 2004-06-30
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Persistent link: https://EconPapers.repec.org/RePEc:boc:usug04:13

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