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Direct Effects under Differential Misclassification in Outcomes, Exposures, and Mediators

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  • Li Yige

    (Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA02215)

  • VanderWeele Tyler J.

    (Department of Epidemiology, Harvard T.H. Chan School of Public Health)

Abstract

Direct effects in mediation analysis quantify the effect of an exposure on an outcome not mediated by a certain intermediate. When estimating direct effects through measured data, misclassification may occur in the outcomes, exposures, and mediators. In mediation analysis, any such misclassification may lead to biased estimates in the direct effects. Basing on the conditional dependence between the mismeasured variable and other variables given the true variable, misclassification mechanisms can be divided into non-differential misclassification and differential misclassification. In this article, several scenarios of differential misclassification will be discussed and sensitivity analysis results on direct effects will be derived for those eligible scenarios. According to our findings, the estimated direct effects are not necessarily biased in intuitively predictable directions when the misclassification is differential. The bounds of the true effects are functions of measured effects and sensitivity parameters. An example from the 2018 NCHS data will illustrate how to conduct sensitivity analyses with our results on misclassified outcomes, gestational hypertension and eclampsia, when the exposure is Hispanic women versus non-Hispanic White women and the mediator is weights gain during pregnancy.

Suggested Citation

  • Li Yige & VanderWeele Tyler J., 2020. "Direct Effects under Differential Misclassification in Outcomes, Exposures, and Mediators," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 286-299, January.
    • Handle: RePEc:bpj:causin:v:8:y:2020:i:1:p:286-299:n:15
    DOI: 10.1515/jci-2019-0020
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    References listed on IDEAS

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    1. Li Tang & Robert H. Lyles & Caroline C. King & Joseph W. Hogan & Yungtai Lo, 2015. "Regression analysis for differentially misclassified correlated binary outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(3), pages 433-449, April.
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