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Marginal Structural Models: unbiased estimation for longitudinal studies

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  • Erica Moodie
  • D. Stephens

Abstract

When both time-varying confounding and mediation are present in a longitudinal setting data, Marginal Structural Models are a useful tool that provides unbiased estimates. Copyright Swiss School of Public Health 2011

Suggested Citation

  • Erica Moodie & D. Stephens, 2011. "Marginal Structural Models: unbiased estimation for longitudinal studies," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 56(1), pages 117-119, February.
    • Handle: RePEc:spr:ijphth:v:56:y:2011:i:1:p:117-119
    DOI: 10.1007/s00038-010-0198-4
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    References listed on IDEAS

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    1. Xiao Yongling & Abrahamowicz Michal & Moodie Erica E. M., 2010. "Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-30, March.
    2. Moodie Erica E. M. & Delaney Joseph A.C. & Lefebvre Geneviève & Platt Robert W, 2008. "Missing Confounding Data in Marginal Structural Models: A Comparison of Inverse Probability Weighting and Multiple Imputation," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-25, July.
    3. Zoe Fewell & M. A. Hernan & F. Wolfe & K. Tilling & H. Choi & J. A. C. Sterne, 2004. "Controlling for time-dependent confounding using marginal structural models," United Kingdom Stata Users' Group Meetings 2004 13, Stata Users Group.
    4. Zoe Fewell & Frederick Wolfe & Hyon Choi & Miguel A. Hernán & Kate Tilling & Jonathan A. C. Sterne, 2004. "Controlling for time-dependent confounding using marginal structural models," Stata Journal, StataCorp LP, vol. 4(4), pages 402-420, December.
    5. Erica Moodie & D. Stephens, 2010. "Using Directed Acyclic Graphs to detect limitations of traditional regression in longitudinal studies," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 55(6), pages 701-703, December.
    6. Rosenblum Michael & van der Laan Mark J., 2010. "Targeted Maximum Likelihood Estimation of the Parameter of a Marginal Structural Model," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-30, April.
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    Cited by:

    1. Regier Michael D. & Moodie Erica E. M., 2016. "The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 65-77, May.
    2. Regier Michael D. & Moodie Erica E. M. & Platt Robert W., 2014. "The Effect of Error-in-Confounders on the Estimation of the Causal Parameter When Using Marginal Structural Models and Inverse Probability-of-Treatment Weights: A Simulation Study," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 1-15, May.

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