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Controlling for time-dependent confounding using marginal structural models

Author

Listed:
  • Zoe Fewell

    () (University of Bristol)

  • Frederick Wolfe

    () (National Data Bank for Rheumatic Diseases)

  • Hyon Choi

    () (Harvard Medical School)

  • Miguel A. Hernán

    () (Harvard School of Public Health)

  • Kate Tilling

    () (University of Bristol)

  • Jonathan A. C. Sterne

    () (University of Bristol)

Abstract

Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time have the potential to allow causal inferences about the effects of exposure on outcome. There is particular interest in estimating the causal effects of medical treatments (or other interventions) in circumstances in which a randomized controlled trial is difficult or impossible. However, standard methods for estimating exposure effects in longitudinal studies are biased in the presence of time-dependent confounders affected by prior treatment. This article describes the use of marginal structural models (described by Robins, Hernán, and Brumback [2000]) to estimate exposure or treatment effects in the presence of time-dependent confounders affected by prior treatment. The method is based on deriving inverse-probability-of-treatment weights, which are then used in a pooled logistic regression model to estimate the causal effect of treatment on outcome. We demonstrate the use of marginal structural models to estimate the effect of methotrexate on mortality in persons suffering from rheumatoid arthritis. Copyright 2004 by StataCorp LP.

Suggested Citation

  • 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.
  • Handle: RePEc:tsj:stataj:v:4:y:2004:i:4:p:402-420
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    Citations

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    Cited by:

    1. Buenstorf, Guido, 2009. "Is commercialization good or bad for science? Individual-level evidence from the Max Planck Society," Research Policy, Elsevier, vol. 38(2), pages 281-292, March.
    2. 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.
    3. Yana Kucheva, 2014. "The Receipt of Subsidized Housing across Generations," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 33(6), pages 841-871, December.
    4. Do, D. Phuong & Wang, Lu & Elliott, Michael R., 2013. "Investigating the relationship between neighborhood poverty and mortality risk: A marginal structural modeling approach," Social Science & Medicine, Elsevier, vol. 91(C), pages 58-66.
    5. Jing Xiao, 2015. "The effects of acquisition on the growth of new technology-based firms: Do different types of acquirers matter?," Small Business Economics, Springer, vol. 45(3), pages 487-504, October.
    6. repec:spr:demogr:v:55:y:2018:i:2:d:10.1007_s13524-018-0656-9 is not listed on IDEAS
    7. Pirani, Elena & Salvini, Silvana, 2015. "Is temporary employment damaging to health? A longitudinal study on Italian workers," Social Science & Medicine, Elsevier, vol. 124(C), pages 121-131.
    8. Elena Pirani & Silvana Salvini, 2014. "Is temporary employment damaging to health? A longitudinal study on Italian workers," Econometrics Working Papers Archive 2014_08, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".

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