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Controlling for time-dependent confounding using marginal structural models

Author

Listed:
  • 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)

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 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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    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. Li, Ang & Baker, Emma & Bentley, Rebecca, 2022. "Understanding the mental health effects of instability in the private rental sector: A longitudinal analysis of a national cohort," Social Science & Medicine, Elsevier, vol. 296(C).
    3. Xiao, Jing, 2018. "Post-acquisition dynamics of technology start-ups: drawing the temporal boundaries of post-acquisition restructuring process," Papers in Innovation Studies 2018/12, Lund University, CIRCLE - Centre for Innovation Research.
    4. 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.
    5. Nam Kyoon N. Kim & Simon C. Parker, 0. "Entrepreneurial homeworkers," Small Business Economics, Springer, vol. 0, pages 1-25.
    6. 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.
    7. 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.
    8. Stefania Fontana & Giorgio d’Agostino, 2024. "Anti-mafia policies and public goods in Italy," Public Choice, Springer, vol. 198(3), pages 493-529, March.
    9. Allison Milner & Yamna Taouk & George Disney & Zoe Aitken & Jerome Rachele & Anne Kavanagh, 2018. "Employment predictors of exit from work among workers with disabilities: A survival analysis from the household income labour dynamics in Australia survey," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-14, December.
    10. Nam Kyoon N. Kim & Simon C. Parker, 2021. "Entrepreneurial homeworkers," Small Business Economics, Springer, vol. 57(3), pages 1427-1451, October.
    11. 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".
    12. Raffoul, Amanda & Beccia, Ariel L. & Jackson, Destiny A. & Sarda, Vishnudas & Hart, Jaime E. & Chavarro, Jorge E. & Austin, S. Bryn, 2023. "Associations between weight discrimination and the use of potentially harmful dietary supplements during the COVID-19 pandemic in the United States," Social Science & Medicine, Elsevier, vol. 335(C).
    13. Matteo Prato & Fabrizio Ferraro, 2018. "Starstruck: How Hiring High-Status Employees Affects Incumbents’ Performance," Organization Science, INFORMS, vol. 29(5), pages 755-774, October.
    14. Mudrazija, Stipica & Butrica, Barbara A., 2023. "How does debt shape health outcomes for older Americans?," Social Science & Medicine, Elsevier, vol. 329(C).
    15. 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.
    16. Geraldine A. Wu, 2012. "The Effect of Going Public on Innovative Productivity and Exploratory Search," Organization Science, INFORMS, vol. 23(4), pages 928-950, August.
    17. 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.
    18. Yana Kucheva, 2018. "Subsidized Housing and the Transition to Adulthood," Demography, Springer;Population Association of America (PAA), vol. 55(2), pages 617-642, April.

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