Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Bodnar, LM; Davidian, M; Siega-Riz, AM; Tsiatis, AA

Published Date

  • May 15, 2004

Published In

Volume / Issue

  • 159 / 10

Start / End Page

  • 926 - 934

PubMed ID

  • 15128604

Pubmed Central ID

  • 15128604

International Standard Serial Number (ISSN)

  • 0002-9262

Digital Object Identifier (DOI)

  • 10.1093/aje/kwh131

Language

  • eng

Conference Location

  • United States