At present, the network meta-analysis has been rapidly developed and widely used, and it has the characteristic of quantifying and comparing the relative advantages of two or more different interventions for a single health outcome. However, comparison of multiple interventions has increased the complexity of drawing conclusions from network meta-analysis, and ignorance of the certainty of evidence has also led to misleading conclusions. Recently, the GRADE (grading of recommendations assessment, development and evaluation) working group proposed two approaches for obtaining conclusions from a network meta-analysis of interventions, namely, the partially contextualised framework and the minimally contextualised framework. When using partially contextualised framework, authors should establish ranges of magnitudes of effect that represent a trivial to no effect, minimal but important effect, moderate effect, and large effect. The guiding principles of this framework are that interventions should be grouped in categories based on the magnitude of the effect and its benefit or harm; and that when classifying, consider the point estimates, the rankings, and the certainty of the evidence comprehensively to draw conclusions. This article employs a case to describe and explain the principles and four steps of partially contextualised framework to provide guidance for the application of this GRADE approach in the interpretation of results and conclusions drawing from a network meta-analysis.
High-quality randomized controlled trials are the best source of evidence to explain the relationship between health interventions and outcomes. However, in cases where they are insufficient, indirect, or inappropriate, researchers may need to include non-randomized studies of interventions to strengthen the evidence body and improve the certainty (quality) of evidence. The latest research from the GRADE working group provides a way for researchers to integrate randomized and non-randomized evidence. The present paper introduced the relevant methods to provide guidance for systematic reviewers, health technology assessors, and guideline developers.
Previous methods of grading evidence for systematic reviews of diagnostic test accuracy have generally focused on assessing the certainty (quality) of evidence at the level of diagnostic indicators. When the question is not limited to follow the diagnostic test accuracy results themselves, the grading results may be inaccurate due to the lack of consideration of the downstream effects of the test accuracy in specific settings. To address these challenges, the GRADE working group conducted a series of studies focused on updating methods to explore or simulate important downstream effects of diagnostic test accuracy outcomes within a contextual framework. This paper aimed to introduce advances in the contextual framework of the GRADE approach to rate the certainty of evidence from systematic reviews of single diagnostic test accuracy.