Sensitivity Analysis in Observational Research: Introducing the E-Value

Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to...

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Bibliographic Details
Published in	Annals of internal medicine Vol. 167; no. 4; p. 268
Main Authors	VanderWeele, Tyler J, Ding, Peng
Format	Journal Article
Language	English
Published	United States 15.08.2017
Subjects	Confounding Factors (Epidemiology) Epidemiologic Research Design Humans Observational Studies as Topic - statistics & numerical data Sensitivity and Specificity
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Abstract	Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
AbstractList	Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
Author	Ding, Peng VanderWeele, Tyler J
Author_xml	– sequence: 1 givenname: Tyler J surname: VanderWeele fullname: VanderWeele, Tyler J organization: From Harvard T.H. Chan School of Public Health, Boston, Massachusetts, and University of California, Berkeley, Berkeley, California – sequence: 2 givenname: Peng surname: Ding fullname: Ding, Peng organization: From Harvard T.H. Chan School of Public Health, Boston, Massachusetts, and University of California, Berkeley, Berkeley, California
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28693043$$D View this record in MEDLINE/PubMed
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Snippet	Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new...
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SubjectTerms	Confounding Factors (Epidemiology) Epidemiologic Research Design Humans Observational Studies as Topic - statistics & numerical data Sensitivity and Specificity
Title	Sensitivity Analysis in Observational Research: Introducing the E-Value
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