Epidemiology. 11(5):550-560, SEPTEMBER 2000
PMID: 10955408
Issn Print: 1044-3983
Publication Date: September 2000
Marginal Structural Models and Causal Inference in Epidemiology
James Robins;Miguel Hernán;Babette Brumback;
+ Author Information
From the Departments of 1Epidemiology and 2Biostatistics, Harvard School of Public Health, Boston, MA.
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.
