LI Mengting 1,2 , ZHU Hongfei 1,2 , HOU Liangying 1,2,3 , WANG Qi 1,2 , TIAN Jinhui 3,4,5,6,7 , CHEN Yaolong 3,4,5,6,7 , YANG Kehu 3,4,5,6,7 , 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 L8S4L8, Canada;
GE Long, Email: gelong2009@163.com
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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.

Citation: LI Mengting, ZHU Hongfei, HOU Liangying, WANG Qi, TIAN Jinhui, CHEN Yaolong, YANG Kehu, DENG Hongyong, ZENG Linan, ZHANG Lingli, Romina Brignardello-Petersen, GE Long. Method to draw conclusions from a network meta-analysis: a minimally contextualised framework. Chinese Journal of Evidence-Based Medicine, 2021, 21(9): 1102-1109. doi: 10.7507/1672-2531.202105068 Copy

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