Use of E-values for addressing confounding in observational studies-an empirical assessment of the literature

Background: E-values are a recently introduced approach to evaluate confounding in observational studies. We aimed to empirically assess the current use of E-values in published literature. Methods: We conducted a systematic literature search for all publications, published up till the end of 2018, which cited at least one of two inceptive E-value papers and presented E-values for original data. For these case publications we identified control publications, matched by journal and issue, where the authors had not calculated E-values. Results: In total, 87 papers presented 516 E-values. Of the 87 papers, 14 concluded that residual confounding likely threatens at least some of the main conclusions. Seven of these 14 named potential uncontrolled confounders. 19 of 87 papers related E-value magnitudes to expected strengths of field-specific confounders. The median E-value was 1.88, 1.82, and 2.02 for the 43, 348, and 125 E-values where confounding was felt likely to affect the results, unlikely to affect the results, or not commented upon, respectively. The 69 case-control publication pairs dealt with effect sizes of similar magnitude. Of 69 control publications, 52 did not comment on unmeasured confounding and 44/69 case publications concluded that confounding was unlikely to affect study conclusions. Conclusions: Few papers using E-values conclude that confounding threatens their results, and their E-values overlap in magnitude with those of papers acknowledging susceptibility to confounding. Facile automation in calculating E-values may compound the already poor handling of confounding. E-values should not be a substitute for careful consideration of potential sources of unmeasured confounding. If used, they should be interpreted in the context of expected confounding in specific fields.