ZHU Hongfei 1,2 , LI Mengting 1,2 , HOU Liangying 1,2,3 , WANG Qi 1,2 , TIAN Jinhui 3,4,6,7 , CHEN Yaolong 3,4,5,6,7 , YANG Kehu 3,4,6,7 , DENG Hongyong 8 , ZENG Linan 9,10,11 , ZHANG Lingli 9,10,11 , Romina Brignardello-Petersen 12 , GE Long 1,2,4,5,6,7
  • 1. Department of Social Science and Health Management, School of Public Health, Lanzhou University, Lanzhou 730000, P.R.China;
  • 2. Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou 730000, P.R.China;
  • 3. Evidence-Based Medicine Center of Lanzhou University, Lanzhou 730000, P.R.China;
  • 4. Key Laboratory of Evidence-Based Medicine and Clinical Transformation in Gansu Province, Lanzhou 730000, P.R.China;
  • 5. Lanzhou University Institute of Health Data Science, Lanzhou 73000, P.R.China;
  • 6. WHO Collaborating Center for Guideline Implementation and Knowledge Translation, Lanzhou 730000, P.R.China;
  • 7. Chinese GRADE Center, Lanzhou 73000, P.R.China;
  • 8. TCM Health Service Collaborative Innovation Center, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, P.R.China;
  • 9. Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu 610041, P.R.China;
  • 10. Evidence Based Pharmacy Center, West China Second University Hospital, Sichuan University, Chengdu 610041, P.R.China;
  • 11. Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu 610041, P.R.China;
  • 12. Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton L8S 4L8, Canada;
GE Long, Email: gelong2009@163.com
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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 one health outcome. However, the comparison of multiple interventions has increased the complexity of drawing conclusions of network meta-analysis, and the ignorance of the certainty of evidence has also led to the misleading conclusions. Recently, the GRADE (grading of recommendations assessment, development and evaluation) working group proposed two approaches on how to make 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, small 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 took an example to describe and explain the principles and four steps of partially contextualised framework, in order to provide guidance for the application of this GRADE approach in the interpretation of results and conclusions drawing from a network meta-analysis.