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

Publication ,  Journal Article
Bodnar, LM; Davidian, M; Siega-Riz, AM; Tsiatis, AA
Published in: Am J Epidemiol
May 15, 2004

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.

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Published In

Am J Epidemiol

DOI

ISSN

0002-9262

Publication Date

May 15, 2004

Volume

159

Issue

10

Start / End Page

926 / 934

Location

United States

Related Subject Headings

  • Time Factors
  • Random Allocation
  • Pregnancy Complications, Hematologic
  • Pregnancy
  • North Carolina
  • Models, Statistical
  • Logistic Models
  • Iron
  • Humans
  • Female
 

Citation

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Bodnar, L. M., Davidian, M., Siega-Riz, A. M., & Tsiatis, A. A. (2004). Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology. Am J Epidemiol, 159(10), 926–934. https://doi.org/10.1093/aje/kwh131
Bodnar, Lisa M., Marie Davidian, Anna Maria Siega-Riz, and Anastasios A. Tsiatis. “Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology.Am J Epidemiol 159, no. 10 (May 15, 2004): 926–34. https://doi.org/10.1093/aje/kwh131.
Bodnar LM, Davidian M, Siega-Riz AM, Tsiatis AA. Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology. Am J Epidemiol. 2004 May 15;159(10):926–34.
Bodnar, Lisa M., et al. “Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology.Am J Epidemiol, vol. 159, no. 10, May 2004, pp. 926–34. Pubmed, doi:10.1093/aje/kwh131.
Bodnar LM, Davidian M, Siega-Riz AM, Tsiatis AA. Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology. Am J Epidemiol. 2004 May 15;159(10):926–934.
Journal cover image

Published In

Am J Epidemiol

DOI

ISSN

0002-9262

Publication Date

May 15, 2004

Volume

159

Issue

10

Start / End Page

926 / 934

Location

United States

Related Subject Headings

  • Time Factors
  • Random Allocation
  • Pregnancy Complications, Hematologic
  • Pregnancy
  • North Carolina
  • Models, Statistical
  • Logistic Models
  • Iron
  • Humans
  • Female