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Use of Generalized Propensity Scores for Assessing Effects of Multiple Exposures

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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.

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Acknowledgements

We thank Neil Dodge, Ph.D., for his assistance with data management support: This research was funded by grants to Sandra W. Jacobson and Joseph L. Jacobson from the National Institutes of Health/National Institute on Alcohol Abuse and Alcoholism (NIH/NIAAA; R01-AA025905) and the Lycaki-Young Fund from the State of Michigan. Richard J. Cook was supported by the Natural Sciences and Engineering Research Council of Canada through grants RGPIN 155849 and RGPIN 04207. Louise Ryan was supported by the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) CE140100049. Data collection for the Detroit Longitudinal Study was supported by grants from NIH/NIAAA (R01-AA06966, R01-AA09524, and P50-AA07606) and NIH/National Institute on Drug Abuse (R21- DA021034).

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Authors and Affiliations

  1. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada

    Kecheng Li, Tugba Akkaya-Hocagil & Richard J. Cook

  2. School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW, Australia

    Louise M. Ryan

  3. Departments of Emergency Medicine and Pediatrics, Institute of Human Nutrition, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA

    R. Colin Carter

  4. School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, Australia

    Khue-Dung Dang

  5. Department of Psychiatry and Behavioral Neurosciences, School of Medicine, Wayne State University, Detroit, MI, USA

    Joseph L. Jacobson & Sandra W. Jacobson

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  1. Kecheng Li
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  2. Tugba Akkaya-Hocagil
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  3. Richard J. Cook
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  7. Joseph L. Jacobson
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  8. Sandra W. Jacobson
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Correspondence to Kecheng Li.

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Li, K., Akkaya-Hocagil, T., Cook, R.J. et al. Use of Generalized Propensity Scores for Assessing Effects of Multiple Exposures. Stat Biosci (2023). https://doi.org/10.1007/s12561-023-09403-8

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  • DOI: https://doi.org/10.1007/s12561-023-09403-8

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