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GRADE approach to rate the certainty from a network meta-analysis: addressing incoherence
GRADE approach to rate the certainty from a network meta-analysis: addressing incoherence
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GRADE approach to rate the certainty from a network meta-analysis: addressing incoherence
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GRADE approach to rate the certainty from a network meta-analysis: addressing incoherence
GRADE approach to rate the certainty from a network meta-analysis: addressing incoherence

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GRADE approach to rate the certainty from a network meta-analysis: addressing incoherence
GRADE approach to rate the certainty from a network meta-analysis: addressing incoherence
Journal Article

GRADE approach to rate the certainty from a network meta-analysis: addressing incoherence

2019
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Overview
This article presents official guidance from the Grading of Recommendations Assessments, Development, and Evaluation (GRADE) working group on how to address incoherence when assessing the certainty in the evidence from network meta-analysis. Incoherence represents important differences between direct and indirect estimates that contribute to a network estimate. Bias due to limitations in study design or publication bias, indirectness, and intransitivity may be responsible for incoherence. Addressing incoherence requires a judgment regarding the importance of the impact on the network estimate. Reviewers need to be alert to the possibility of misguidedly arriving at excessively low ratings of certainty by rating down for both incoherence and other closely related GRADE domains. This article describes and illustrates each of these issues and provides explicit guidance on how to deal with them.