Sensitivity Analysis in Observational Research

Introducing the E-Value

Bibliographic Data

ID23375040
AuthorsTyler J Vanderweele (0000-0002-6112-0239, From Harvard T.H. Chan School of Public Health, Boston, Massachusetts, and University of California, Berkeley, Berkeley, California.), Peng Ding (0000-0002-2704-2353, From Harvard T.H. Chan School of Public Health, Boston, Massachusetts, and University of California, Berkeley, Berkeley, California.)
Year2017
Volume167
Issue4
Pages268-274
Publication date2017-08-15
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAnnals of Internal Medicine (JOURNAL)
Journal identifiersISSN: 0003-4819 • E-ISSN: 1539-3704
PublisherAmerican College of Physicians (PUBLISHER • US)
DOI10.7326/m16-2607
PMID28693043
OpenAlexW2735301415
LanguageEN
Citations received363
References cited33

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.

Observational study · Sensitivity (control systems) · Statistics · Value (mathematics) · Advanced Causal Inference Techniques · Health Systems, Economic Evaluations, Quality of Life · Internal Medicine · Medicine · Statistical Methods and Bayesian Inference