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gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula

Author

Listed:
  • Rhian M. Daniel

    (London School of Hygiene and Tropical Medicine)

  • Bianca L. De Stavola

    (London School of Hygiene and Tropical Medicine)

  • Simon N. Cousens

    (London School of Hygiene and Tropical Medicine)

Abstract

This article describes a new command, gformula, that is an implementation of the g-computation procedure. It is used to estimate the causal effect of time-varying exposures on an outcome in the presence of time-varying confounders that are themselves also affected by the exposures. The procedure also addresses the related problem of estimating direct and indirect effects when the causal ef- fect of the exposures on an outcome is mediated by intermediate variables, and in particular when confounders of the mediator–outcome relationships are them- selves affected by the exposures. A brief overview of the theory and a description of the command and its options are given, and illustrations using two simulated examples are provided. Copyright 2011 by StataCorp LP.

Suggested Citation

  • Rhian M. Daniel & Bianca L. De Stavola & Simon N. Cousens, 2011. "gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula," Stata Journal, StataCorp LP, vol. 11(4), pages 479-517, December.
    • Handle: RePEc:tsj:stataj:v:11:y:2011:i:4:p:479-517
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    References listed on IDEAS

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    1. Alan E. Hubbard & Mark J. van der Laan, 2008. "Population intervention models in causal inference," Biometrika, Biometrika Trust, vol. 95(1), pages 35-47.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    3. Abbring, Jaap H., 2003. "Dynamic Econometric Program Evaluation," IZA Discussion Papers 804, Institute of Labor Economics (IZA).
    4. Jonathan A. C. Sterne & Kate Tilling, 2002. "G-estimation of causal effects, allowing for time-varying confounding," Stata Journal, StataCorp LP, vol. 2(2), pages 164-182, May.
    5. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
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    Cited by:

    1. R. M. Daniel & B. L. De Stavola & S. N. Cousens & S. Vansteelandt, 2015. "Causal mediation analysis with multiple mediators," Biometrics, The International Biometric Society, vol. 71(1), pages 1-14, March.
    2. Richard J Silverwood & Lee Williamson & Emily M Grundy & Bianca L De Stavola, 2016. "Pathways between Socioeconomic Disadvantage and Childhood Growth in the Scottish Longitudinal Study, 1991–2001," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-17, October.
    3. Philipp Baumann & Enzo Rossi & Michael Schomaker, 2022. "Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    4. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    5. Delis, Manthos D. & Dioikitopoulos, Evangelos V. & Ongena, Steven, 2023. "Population diversity and financial risk-taking," Journal of Banking & Finance, Elsevier, vol. 151(C).
    6. Soojin Park & Kevin M. Esterling, 2021. "Sensitivity Analysis for Pretreatment Confounding With Multiple Mediators," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 85-108, February.
    7. Ploubidis, George B. & Benova, Lenka & Grundy, Emily & Laydon, Daniel & DeStavola, Bianca, 2014. "Lifelong Socio Economic Position and biomarkers of later life health: Testing the contribution of competing hypotheses," Social Science & Medicine, Elsevier, vol. 119(C), pages 258-265.
    8. Marco Doretti & Sara Geneletti & Elena Stanghellini, 2016. "Tackling non-ignorable dropout in the presence of time varying confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 775-795, November.
    9. Steen, Johan & Loeys, Tom & Moerkerke, Beatrijs & Vansteelandt, Stijn, 2017. "medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i11).
    10. Sourabh Balgi & Adel Daoud & Jose M. Pe~na & Geoffrey T. Wodtke & Jesse Zhou, 2024. "Deep Learning With DAGs," Papers 2401.06864, arXiv.org.
    11. Berna Beyhan & Derya Fındık, 2022. "Selection of Sustainability Startups for Acceleration: How Prior Access to Financing and Team Features Influence Accelerators’ Selection Decisions," Sustainability, MDPI, vol. 14(4), pages 1-23, February.

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