Causal Mediation Analysis in the Presence of a Misclassified Binary Exposure
Abstract: Mediation analysis is popular in examining the extent to which the effect of an exposure on an outcome is through an intermediate variable. When the exposure is subject to misclassification, the effects estimated can be severely biased. In this paper, when the mediator is binary, we first study the bias on traditional direct and indirect effect estimates in the presence of conditional non-differential misclassification of a binary exposure. We show that in the absence of interaction, the misclassification of the exposure will bias the direct effect towards the null but can bias the indirect effect in either direction. We then develop an EM algorithm approach to correcting for the misclassification, and conduct simulation studies to assess the performance of the correction approach. Finally, we apply the approach to National Center for Health Statistics birth certificate data to study the effect of smoking status on the preterm birth mediated through pre-eclampsia.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Causal Mediation Analysis in the Presence of a Misclassified Binary Exposure ; volume:8 ; number:1 ; year:2019 ; extent:12
Epidemiologic methods ; 8, Heft 1 (2019) (gesamt 12)
- Creator
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Jiang, Zhichao
VanderWeele, Tyler
- DOI
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10.1515/em-2016-0006
- URN
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urn:nbn:de:101:1-2412141651120.806904842628
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:32 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Jiang, Zhichao
- VanderWeele, Tyler
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