4.8 Article

How to report E-values for meta-analyses: Recommended improvements and additions to the new GRADE approach

Journal

ENVIRONMENT INTERNATIONAL
Volume 160, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.envint.2021.107032

Keywords

Meta-analysis; Bias; Confounding; Observational studies; Sensitivity analysis

Funding

  1. National Institutes of Health (NIH) [R01 LM013866R01, R01 CA222147]
  2. NIH [UL1TR003142]
  3. Biostatistics Shared Resource (BSR) of the NIH [P30CA124435]
  4. Quantitative Sciences Unit through the Stanford Diabetes Research Center [P30DK116074]

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This paper proposes improvements to the GRADE guidelines for assessing sensitivity to uncontrolled confounding in meta-analyses of nonrandomized studies. The specific proposal suggests comparing the E-value with the strength of association of a reference confounder, considering the possibility of confounding bias that is heterogeneous across studies.
In a recent concept paper (Verbeek et al., 2021), the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group provides a preliminary proposal to improve its existing guidelines for assessing sensitivity to uncontrolled confounding in meta-analyses of nonrandomized studies. The new proposal centers on reporting the E-value for the meta-analytic mean and on comparing this E-value to a measured reference confounder to determine whether residual uncontrolled confounding in the meta-analyzed studies could or could not plausibly explain away the meta-analytic mean. Although we agree that E-value analogs for meta-analyses could be an informative addition to future GRADE guidelines, we suggest improvements to the Verbeek et al. (2021)'s specific proposal regarding: (1) their interpretation of comparisons between the E-value and the strengths of associations of a reference confounder; (2) their characterization of evidence strength in meta-analyses in terms of only the meta-analytic mean; and (3) the possibility of confounding bias that is heterogeneous across studies.

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