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 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. In observational studies, the E-value provides an important supplement to the p-value. 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. Questions of interpretation and relations with prior sensitivity analysis techniques and Rosenbaum’s design sensitivity will be discussed.
Date: 29 November 2019, 15:30 (Friday, 7th week, Michaelmas 2019)
Venue: 24-29 St Giles', 24-29 St Giles' OX1 3LB
Venue Details: Large Lecture Theatre (LG.01)
Speaker: Professor Tyler Vanderweele (Departments of Epidemiology and Biostatistics at the Harvard T.H. Chan School of Public Health)
Organising department: Department of Statistics
Organisers: Dr Robin Evans (Department of Statistics, University of Oxford), Beverley Lane (Department of Statistics, University of Oxford)
Organiser contact email address: lane@stats.ox.ac.uk
Host: Dr Robin Evans (Department of Statistics, University of Oxford)
Part of: Distinguished Speaker Seminar
Booking required?: Not required
Audience: Members of the University only
Editor: Beverley Lane