Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Tyler
VanderWeele*, Professor of Epidemiology at Harvard University. He will be
presenting work entitled *Sensitivity analysis in observational research:
introducing the E-value*. Please find the abstract below and on the
Applied Stats website here
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>.
As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided. See you all there!
-- Dana Higgins
*Title:* *Sensitivity analysis in observational research: introducing the
E-value*
*Abstract:* Sensitivity analysis is useful in assessing how robust an
association is to potential unmeasured or uncontrolled confounding. This
webinar 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 speaker and his collaborators 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.
Reference: VanderWeele, T.J. and Ding, P. (2017). Sensitivity analysis in
observational research: introducing the E-value. Annals of Internal
Medicine, 167:268-274.
Online E-value Calculator:
https://mmathur.shinyapps.io/evalue/