The E-value quantifies the minimum strength of confounding
association needed to fully explain away an observed treatment
effect:
$$E = RR + \sqrt{RR \cdot (RR - 1)}$$
Usage
morie_e_value(rr, rr_lower = NULL)
Arguments
- rr
Risk ratio estimate (> 0). Supply > 1; if < 1, pass its
reciprocal.
- rr_lower
Lower bound of the 95\
E-value for CI).
Value
Named list: e_value (for the point estimate),
e_value_ci (for the CI bound).
Details
For a risk ratio \(RR < 1\), use \(1/RR\) before applying the
formula.
Computed in closed form; the formula above is the one
EValue::evalue() uses for RR estimands, so the two agree.
References
VanderWeele TJ, Ding P (2017). Sensitivity analysis in
observational research: introducing the E-value. Annals of
Internal Medicine, 167(4):268-274.
Examples
morie_e_value(rr = 3.9, rr_lower = 2.4)
#> $e_value
#> [1] 7.263034
#>
#> $e_value_ci
#> [1] 4.23303
#>
