How to use this website
Please use the following citations when using this website:
- Mathur MB, Ding P, Riddell CA, VanderWeele TJ (2018). Website and R package for computing E-values. Epidemiology, 29(5), e45-e47. Link
- VanderWeele TJ & Ding P (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4), 268-274. Link
Computing an E-value
The tab Compute an E-value computes the E-value, defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome, conditional on the measured covariates, to fully explain away a specific exposure-outcome association. Stated otherwise, confounding associations that were jointly weaker than the E-value could not explain away the association. Note that for outcome types other than relative risks, assumptions are involved with the approximate conversions used (VanderWeele & Ding, 2017).
Alternatively, you can consider the confounding strength capable of moving the observed association to any other value (e.g. attenuating the observed association to a true causal effect that is no longer scientifically important, or alternatively increasing a near-null observed association to a value that is of scientific importance). For this purpose, simply type a non-null effect size into the box “True causal effect to which to shift estimate” when computing the E-value.
Computing a bias factor
Additionally, if you have substantive knowledge on the strength of the relationships between the unmeasured confounder(s) and the exposure and outcome, you can use these numbers to calculate the bias factor.
Submit any bug reports to:
mmathur [AT] stanford [DOT] edu or open an
issue on GitHub.