Use of E-values for addressing confounding in observational studies-an empirical assessment of the literature
- PMID: 31930286
- DOI: 10.1093/ije/dyz261
Use of E-values for addressing confounding in observational studies-an empirical assessment of the literature
Abstract
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.
Keywords: E-value; confounding; literature review; observational study; sensitivity analysis.
© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
Comment in
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Commentary: Quantifying the unknown unknowns.Int J Epidemiol. 2020 Oct 1;49(5):1503-1505. doi: 10.1093/ije/dyaa092. Int J Epidemiol. 2020. PMID: 32594115 Free PMC article. No abstract available.
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Commentary: Cynical epidemiology.Int J Epidemiol. 2020 Oct 1;49(5):1507-1508. doi: 10.1093/ije/dyaa096. Int J Epidemiol. 2020. PMID: 32594156 No abstract available.
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Commentary: The value of E-values and why they are not enough.Int J Epidemiol. 2020 Oct 1;49(5):1505-1506. doi: 10.1093/ije/dyaa093. Int J Epidemiol. 2020. PMID: 32620951 No abstract available.
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Commentary: Developing best-practice guidelines for the reporting of E-values.Int J Epidemiol. 2020 Oct 1;49(5):1495-1497. doi: 10.1093/ije/dyaa094. Int J Epidemiol. 2020. PMID: 32743656 Free PMC article. No abstract available.
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Commentary: An argument against E-values for assessing the plausibility that an association could be explained away by residual confounding.Int J Epidemiol. 2020 Oct 1;49(5):1501-1503. doi: 10.1093/ije/dyaa095. Int J Epidemiol. 2020. PMID: 32808028 No abstract available.
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Commentary: Continuing the E-value's post-publication peer review.Int J Epidemiol. 2020 Oct 1;49(5):1497-1500. doi: 10.1093/ije/dyaa097. Int J Epidemiol. 2020. PMID: 33336256 Free PMC article. No abstract available.
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