Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis

J Clin Epidemiol. 2018 Jan:93:36-44. doi: 10.1016/j.jclinepi.2017.10.005. Epub 2017 Oct 17.


This article describes conceptual advances of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group guidance to evaluate the certainty of evidence (confidence in evidence, quality of evidence) from network meta-analysis (NMA). Application of the original GRADE guidance, published in 2014, in a number of NMAs has resulted in advances that strengthen its conceptual basis and make the process more efficient. This guidance will be useful for systematic review authors who aim to assess the certainty of all pairwise comparisons from an NMA and who are familiar with the basic concepts of NMA and the traditional GRADE approach for pairwise meta-analysis. Two principles of the original GRADE NMA guidance are that we need to rate the certainty of the evidence for each pairwise comparison within a network separately and that in doing so we need to consider both the direct and indirect evidence. We present, discuss, and illustrate four conceptual advances: (1) consideration of imprecision is not necessary when rating the direct and indirect estimates to inform the rating of NMA estimates, (2) there is no need to rate the indirect evidence when the certainty of the direct evidence is high and the contribution of the direct evidence to the network estimate is at least as great as that of the indirect evidence, (3) we should not trust a statistical test of global incoherence of the network to assess incoherence at the pairwise comparison level, and (4) in the presence of incoherence between direct and indirect evidence, the certainty of the evidence of each estimate can help decide which estimate to believe.

Keywords: Certainty of evidence; Confidence in estimates of effect; GRADE; Indirect comparisons; Network meta-analysis; Quality of evidence.

MeSH terms

  • GRADE Approach / trends*
  • Humans
  • Models, Theoretical
  • Network Meta-Analysis*
  • Systematic Reviews as Topic