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The Effect of Error-in-Confounders on the Estimation of the Causal Parameter When Using Marginal Structural Models and Inverse Probability-of-Treatment Weights: A Simulation Study

Author

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  • Regier Michael D.

    (West Virginia University, Morgantown, WV, USA)

  • Moodie Erica E. M.

    (McGill University, Montréal, QC, Canada)

  • Platt Robert W.

    (McGill University, Montréal, QC, Canada)

Abstract

We performed an empirical study to evaluate the effect of mismeasured continuous confounders on the estimation of the causal parameter when using marginal structural models and inverse probability-of-treatment weighting. By executing an extensive simulation using 500 randomly generated parameter value combinations within a defined space, we observed the well-understood effects of attenuation and augmentation, and two unanticipated effects: null effects and sign reversals. We implemented a secondary empirical study to further investigate the sign reversal effect. We use the results of our study to identify conceptual similarities between the analytic and empirical results for multivariable linear and logistic regression, and our empirical results. Through this synthesis, we have been able to suggest feasible directions of research as well as outline the form of expected results.

Suggested Citation

  • Regier Michael D. & Moodie Erica E. M. & Platt Robert W., 2014. "The Effect of Error-in-Confounders on the Estimation of the Causal Parameter When Using Marginal Structural Models and Inverse Probability-of-Treatment Weights: A Simulation Study," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 1-15, May.
    • Handle: RePEc:bpj:ijbist:v:10:y:2014:i:1:p:15:n:3
    DOI: 10.1515/ijb-2012-0039
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Di Shu & Grace Y. Yi, 2018. "Estimation of Causal Effect Measures in the Presence of Measurement Error in Confounders," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 233-254, April.

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