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Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.

Miguel A Hernán·Sonia Hernández-Díaz·Martha M Werler·Allen A Mitchell
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Abstract

Common strategies to decide whether a variable is a confounder that should be adjusted for in the analysis rely mostly on statistical criteria. The authors present findings from the Slone Epidemiology Unit Birth Defects Study, 1992-1997, a case-control study on folic acid supplementation and risk of neural tube defects. When statistical strategies for confounding evaluation are used, the adjusted odds ratio is 0.80 (95% confidence interval: 0.62, 1.21). However, the consideration of a priori causal knowledge suggests that the crude odds ratio of 0.65 (95% confidence interval: 0.46, 0.94) should be used because the adjusted odds ratio is invalid. Causal diagrams are used to encode qualitative a priori subject matter knowledge.

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BiasCausalityConfounding Factors, EpidemiologicEpidemiologic MethodsFemaleFolic AcidHumansInfant, NewbornMental RecallNeural Tube DefectsOntarioPregnancyUnited StatesJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

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Miguel A Hernán, Sonia Hernández-Díaz, Martha M Werler, Allen A Mitchell. 2002-01-15. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.. https://doi.org/10.1093/aje%2F155.2.176

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