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Controlling for time-dependent confounding using marginal structural models


  • Zoe Fewell

    () (Department of Social Medicine, University of Bristol)

  • M. A. Hernan

    (Harvard School of Public Health)

  • F. Wolfe

    (National Data Bank for Rheumatic Diseases, USA)

  • K. Tilling

    (Department of Social Medicine, University of Bristol)

  • H. Choi

    (Harvard Medical School)

  • J. A. C. Sterne

    (Department of Social Medicine, University of Bristol)


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 randomised 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 talk describes the use of marginal structural models (described by Robins et al.) 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.

Suggested Citation

  • 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.
  • Handle: RePEc:boc:usug04:13

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    References listed on IDEAS

    1. Nicholas J. Cox, 2004. "Speaking Stata: Graphing categorical and compositional data," Stata Journal, StataCorp LP, vol. 4(2), pages 190-213, June.
    2. Roger Newson, 2003. "Confidence intervals and p-values for delivery to the end user," Stata Journal, StataCorp LP, vol. 3(3), pages 245-269, September.
    3. Nicholas J. Cox & Ulrich Kohler, 2003. "Speaking Stata: On structure and shape: the case of multiple responses," Stata Journal, StataCorp LP, vol. 3(1), pages 81-99, March.
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    Cited by:

    1. Xiao , Jing, 2014. "Ownership Change, Multinationals, and Growth of New Technology-Based Firms," Papers in Innovation Studies 2014/24, Lund University, CIRCLE - Center for Innovation, Research and Competences in the Learning Economy.
    2. 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.
    3. 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.
    4. 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.
    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. 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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