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Use of Generalized Propensity Scores for Assessing Effects of Multiple Exposures

Author

Listed:
  • Kecheng Li

    (University of Waterloo)

  • Tugba Akkaya-Hocagil

    (University of Waterloo)

  • Richard J. Cook

    (University of Waterloo)

  • Louise M. Ryan

    (University of Technology Sydney)

  • R. Colin Carter

    (Columbia University Vagelos College of Physicians and Surgeons)

  • Khue-Dung Dang

    (University of Melbourne)

  • Joseph L. Jacobson

    (Wayne State University)

  • Sandra W. Jacobson

    (Wayne State University)

Abstract

When interest lies in causal analysis of the effects of multiple exposures on an outcome, one may be interested in investigating the interaction between the exposures. In such settings, causal analysis requires modeling the joint distribution of exposures given pertinent confounding variables. In the most general setting, this may require modeling the effect of confounding variables on the association between exposures via a second-order regression model. We consider joint modeling of exposures for causal analysis via regression adjustment and inverse weighting. In both frameworks, we also investigate the asymptotic bias of estimators when the dependence model for the generalized propensity score incorrectly assumes conditional independence of exposures or is based on a naive dependence model which does not accommodate the effect of confounders on the conditional association of exposures. We also consider the problem of a semi-continuous bivariate exposure and propose a two-stage estimation technique to study the effects of prenatal alcohol exposure, and the effects of drinking frequency and intensity on childhood cognition.

Suggested Citation

  • Kecheng Li & Tugba Akkaya-Hocagil & Richard J. Cook & Louise M. Ryan & R. Colin Carter & Khue-Dung Dang & Joseph L. Jacobson & Sandra W. Jacobson, 2024. "Use of Generalized Propensity Scores for Assessing Effects of Multiple Exposures," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 347-376, July.
    • Handle: RePEc:spr:stabio:v:16:y:2024:i:2:d:10.1007_s12561-023-09403-8
    DOI: 10.1007/s12561-023-09403-8
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    References listed on IDEAS

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    1. Brian G. Vegetabile & Daniel L. Gillen & Hal S. Stern, 2020. "Optimally balanced Gaussian process propensity scores for estimating treatment effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 355-377, January.
    2. Tugba Akkaya Hocagil & Richard J. Cook & Sandra W. Jacobson & Joseph L. Jacobson & Louise M. Ryan, 2021. "Propensity score analysis for a semi‐continuous exposure variable: a study of gestational alcohol exposure and childhood cognition," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1390-1413, October.
    3. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
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