  The strength of evidence provided by epidemiological and observational
studies is inherently limited by the potential for unmeasured confounding.
Researchers should present a quantified sensitivity to unmeasured confounding
analysis that is contextualized by the study's observed covariates. VanderWeele
and Ding's E-value provides an easily calculated metric for the magnitude of
the hypothetical unmeasured confounding required to render the study's result
inconclusive. We propose the Observed Covariate E-value to contextualize the
sensitivity analysis' hypothetical E-value within the actual impact of observed
covariates, individually or within groups. We introduce a sensitivity analysis
figure that presents the Observed Covariate E-values, on the E-value scale,
next to their corresponding observed bias effects, on the original scale of the
study results. This observed bias plot allows easy comparison of the
hypothetical E-values, Observed Covariate E-values, and observed bias effects.
We illustrate the methods with a specific example and provide a supplemental
appendix with modifiable code that teaches how to implement the method and
create a publication quality figure.
