E-Value Sensitivity Analysis

The E-value, introduced by Tyler VanderWeele and Peng Ding in 2017, is a simple, assumption-free way to quantify how robust an observational association is to unmeasured confounding. It answers a single, sharply posed question: how strong would an unmeasured confounder have to be — in its association with both the exposure and the outcome — to fully explain away the observed effect? The larger the E-value, the more powerful a hidden confounder would need to be, and so the more robust the finding. The method rests on the bounding factor derived by Ding and VanderWeele in their 2016 'Sensitivity analysis without assumptions,' which holds regardless of the distribution or number of unmeasured confounders. Because it requires only the point estimate and confidence limit on the risk-ratio scale and no untestable bias parameters, the E-value has become a routine reporting standard in observational epidemiology, including social epidemiology where unmeasured confounding is pervasive.