Marginal Structural Models and Causal Inference in Epidemiology

Epidemiology. 2000 Sep;11(5):550-60. doi: 10.1097/00001648-200009000-00011.

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.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Anti-HIV Agents / therapeutic use
  • Causality*
  • Confounding Factors, Epidemiologic
  • Epidemiologic Methods*
  • HIV Infections / drug therapy
  • HIV Infections / mortality
  • Humans
  • Models, Statistical*
  • Risk Factors
  • Time Factors
  • Zidovudine / therapeutic use

Substances

  • Anti-HIV Agents
  • Zidovudine