Am J Epidemiol 2004; 159:926-934.
Copyright © 2004 by the Johns
Hopkins Bloomberg School of Public Health
PRACTICE OF EPIDEMIOLOGY |
Marginal Structural Models for Analyzing Causal Effects of Time-dependent Treatments: An Application in Perinatal Epidemiology
1 Department of Nutrition, School of Public Health and School of Medicine, University of North Carolina, Chapel Hill, NC.
2 Carolina Population Center, University of North Carolina, Chapel Hill, NC.
3 Department of Statistics, North Carolina State University, Raleigh, NC.
4 Department of Maternal and Child Health, School of Public Health, University of North Carolina, Chapel Hill, NC.
Marginal structural models (MSMs) are causal models designed to adjust for time-dependent confounding in observational studies of time-varying treatments. MSMs are powerful tools for assessing causality with complicated, longitudinal data sets but have not been widely used by practitioners. The objective of this paper is to illustrate the fitting of an MSM for the causal effect of iron supplement use during pregnancy (time-varying treatment) on odds of anemia at delivery in the presence of time-dependent confounding. Data from pregnant women enrolled in the Iron Supplementation Study (Raleigh, North Carolina, 1997–1999) were used. The authors highlight complexities of MSMs and key issues epidemiologists should recognize before and while undertaking an analysis with these methods and show how such methods can be readily interpreted in existing software packages, including SAS and Stata. The authors emphasize that if a data set with rich information on confounders is available, MSMs can be used straightforwardly to make robust inferences about causal effects of time-dependent treatments/exposures in epidemiologic research.
causality; confounding factors (epidemiology); epidemiologic methods; longitudinal studies; models, structural
Abbreviations: Abbreviation: MSM(s), marginal structural model(s).
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