The use of the E-value for sensitivity analysis

Abstract

Observational research designs enable clinicians to investigate topics for which randomized-controlled trials may be difficult to conduct. However, the lack of randomization in observational studies increases the likelihood of confounders introducing bias to study results. Analytical methods such as propensity score matching and regression analysis are employed to reduce the effects of such confounding, mainly by determining characteristics of patient groups and adjusting for measured confounders. Sensitivity analyses are subsequently applied to elucidate the extent to which study results could still be affected by unmeasured confounding. The E-value is one such approach. By presenting a value that quantifies the strength of unmeasured confounding necessary to negate the observed results, the E-value is a useful heuristic concept for assessing the robustness of observational studies. This article provides an introductory overview of how the E-value can be evaluated and presented in clinical research studies.

Keywords: Confounding; E-value; Observational research; Sensitivity analysis.