The primary advantage of network meta-analysis is the capability to quantify and compare different interventions for the same diseases and rank their benefits or harms according to a certain health outcome. The inclusion of a variety of interventions has increased the complexity of the conclusions drawing from a network meta-analysis, and based on the ranking results alone may lead to misleading conclusions. At present, there are no accepted standards for the conclusion drawing from a network meta-analysis. In November 2020, based on the evidence certainty results of network meta-analysis, the GRADE (Grades of Recommendations Assessment, Development and Evaluation) working group proposed two approaches to draw conclusions from a network meta-analysis: the partially contextualised framework and the minimally contextualised framework. This paper aimed to introduce principles and procedures of the minimal contextualised framework through a specific example to provide guidance for the network meta-analysis authors in China to present and interpret the results using minimally contextualised framework.
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.
