Method to draw conclusions from a network meta-analysis: a partially contextualised framework_Chinese Journal of Evidence-Based Medicine

Authors：

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 L8S4L8, Canada;

Corresponding author：

GE Long, Email: gelong2009@163.com

Keywords：

Network meta-analysis; GRADE approach; Drawing conclusion; Certainty of evidence; Partially contextualised framework

DOI：

10.7507/1672-2531.202105067

Video：

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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 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.

Citation： ZHU Hongfei, LI Mengting, 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 partially contextualised framework. Chinese Journal of Evidence-Based Medicine, 2021, 21(9): 1110-1116. doi: 10.7507/1672-2531.202105067 Copy

1.	Jansen JP, Crawford B, Bergman G, et al. Bayesian meta-analysis of multiple treatment comparisons: an introduction to mixed treatment comparisons. Value Health, 2008, 11(5): 956-964.
2.	李伦, 杨克虎. 网状Meta分析相关术语和定义的研究. 中国药物评价, 2014, (6): 321-326.
3.	Schünemann HJ, Higgins JP, Vist GE, et al. Completing 'summary of findings' tables and grading the certainty of the evidence. Hoboken: John Wiley & Sons, Ltd, 2019.
4.	王琪, 王建成, 潘蓓, 等. 网状Meta分析证据质量分级: GRADE方法学前沿与进展. 中国循证医学杂志, 2020, 20(8): 979-985.
5.	Trinquart L, Attiche N, Bafeta A, et al. Uncertainty in treatment rankings: reanalysis of network meta-analyses of randomized trials. Ann Intern Med, 2016, 164(10): 666-673.
6.	Mills EJ, Thorlund K, Ioannidis JP. Demystifying trial networks and network meta-analysis. BMJ, 2013, 346: f2914.
7.	Brignardello-Petersen R, Guyatt GH. β-Blockers in heart failure--are all created equal? Pol Arch Med Wewn, 2013, 123(5): 204-205.
8.	Brignardello-Petersen R, Rochwerg B, Guyatt GH. What is a network meta-analysis and how can we use it to inform clinical practice? Pol Arch Med Wewn, 2014, 124(12): 659-660.
9.	Brignardello-Petersen R, Bonner A, Alexander PE, et al. Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis. J Clin Epidemiol, 2018, 93: 36-44.
10.	Puhan MA, Schünemann HJ, Murad MH, et al. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ, 2014, 349: g5630.
11.	杨楠, 肖淑君, 周奇, 等. GRADE在网状Meta分析中应用的基本原理和方法介绍. 中国循证医学杂志, 2016, 16(5): 598-603.
12.	Brignardello-Petersen R, Murad MH, Walter SD, et al. GRADE approach to rate the certainty from a network meta-analysis: avoiding spurious judgments of imprecision in sparse networks. J Clin Epidemiol, 2019, 105: 60-67.
13.	Yepes-Nuñez JJ, Li SA, Guyatt G, et al. Development of the summary of findings table for network meta-analysis. J Clin Epidemiol, 2019, 115: 1-13.
14.	Schünemann HJ, Wiercioch W, Brozek J, et al. GRADE Evidence to Decision (EtD) frameworks for adoption, adaptation, and de novo development of trustworthy recommendations: GRADE-ADOLOPMENT. J Clin Epidemiol, 2017, 81: 101-110.
15.	Brignardello-Petersen R, Florez ID, Izcovich A, et al. GRADE approach to drawing conclusions from a network meta-analysis using a minimally contextualised framework. BMJ, 2020, 371: m3900.
16.	Brignardello-Petersen R, Izcovich A, Rochwerg B, et al. GRADE approach to drawing conclusions from a network meta-analysis using a partially contextualised framework. BMJ, 2020, 371: m3907.
17.	栗梦婷, 朱鸿飞, 后亮瑛, 等. 网状Meta结论形成的方法: 最小背景化框架. 中国循证医学杂志, 2021. DOI:10.7507/1672-2531.202105068.
18.	Balshem H, Helfand M, Schünemann HJ, et al. GRADE guidelines: 3. rating the quality of evidence. J Clin Epidemiol, 2011, 64(4): 401-406.
19.	Hultcrantz M, Rind D, Akl EA, et al. The GRADE Working Group clarifies the construct of certainty of evidence. J Clin Epidemiol, 2017, 87: 4-13.
20.	Zhang Y, Alonso-Coello P, Guyatt GH, et al. GRADE guidelines: 19. assessing the certainty of evidence in the importance of outcomes or values and preferences-risk of bias and indirectness. J Clin Epidemiol, 2019, 111: 94-104.
21.	Zhang Y, Coello PA, Guyatt GH, et al. GRADE guidelines: 20. assessing the certainty of evidence in the importance of outcomes or values and preferences-inconsistency, imprecision, and other domains. J Clin Epidemiol, 2019, 111: 83-93.
22.	Schünemann HJ, Lerda D, Dimitrova N, et al. Methods for development of the European commission initiative on breast cancer guidelines: recommendations in the era of guideline transparency. Ann Intern Med, 2019, 171(4): 273-280.
23.	Schünemann HJ. Interpreting GRADE's levels of certainty or quality of the evidence: GRADE for statisticians, considering review information size or less emphasis on imprecision? J Clin Epidemiol, 2016, 75: 6-15.
24.	Alonso-Coello P, Schünemann HJ, Moberg J, et al. GRADE Evidence to Decision (EtD) frameworks: a systematic and transparent approach to making well informed healthcare choices. 1: introduction. BMJ, 2016, 353: i2016.
25.	Alonso-Coello P, Oxman AD, Moberg J, et al. GRADE Evidence to Decision (EtD) frameworks: a systematic and transparent approach to making well informed healthcare choices. 2: clinical practice guidelines. BMJ, 2016, 353: i2089.
26.	Schünemann HJ, Mustafa R, Brozek J, et al. GRADE guidelines: 16. GRADE evidence to decision frameworks for tests in clinical practice and public health. J Clin Epidemiol, 2016, 76: 89-98.
27.	Santesso N, Glenton C, Dahm P, et al. GRADE guidelines 26: informative statements to communicate the findings of systematic reviews of interventions. J Clin Epidemiol, 2020, 119: 126-135.
28.	王琪, 王韵华, 赖鸿皓, 等. 网状Meta分析证据质量分级: CINeMA在线应用程序简介. 中国循证医学杂志, 2020, 20(9): 1111-1116.
29.	Florez ID, Veroniki AA, Al Khalifah R, et al. Comparative effectiveness and safety of interventions for acute diarrhea and gastroenteritis in children: A systematic review and network meta-analysis. PLoS One, 2018, 13(12): e0207701.

1. Jansen JP, Crawford B, Bergman G, et al. Bayesian meta-analysis of multiple treatment comparisons: an introduction to mixed treatment comparisons. Value Health, 2008, 11(5): 956-964.
2. 李伦, 杨克虎. 网状Meta分析相关术语和定义的研究. 中国药物评价, 2014, (6): 321-326.
3. Schünemann HJ, Higgins JP, Vist GE, et al. Completing 'summary of findings' tables and grading the certainty of the evidence. Hoboken: John Wiley & Sons, Ltd, 2019.
4. 王琪, 王建成, 潘蓓, 等. 网状Meta分析证据质量分级: GRADE方法学前沿与进展. 中国循证医学杂志, 2020, 20(8): 979-985.
5. Trinquart L, Attiche N, Bafeta A, et al. Uncertainty in treatment rankings: reanalysis of network meta-analyses of randomized trials. Ann Intern Med, 2016, 164(10): 666-673.
6. Mills EJ, Thorlund K, Ioannidis JP. Demystifying trial networks and network meta-analysis. BMJ, 2013, 346: f2914.
7. Brignardello-Petersen R, Guyatt GH. β-Blockers in heart failure--are all created equal? Pol Arch Med Wewn, 2013, 123(5): 204-205.
8. Brignardello-Petersen R, Rochwerg B, Guyatt GH. What is a network meta-analysis and how can we use it to inform clinical practice? Pol Arch Med Wewn, 2014, 124(12): 659-660.
9. Brignardello-Petersen R, Bonner A, Alexander PE, et al. Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis. J Clin Epidemiol, 2018, 93: 36-44.
10. Puhan MA, Schünemann HJ, Murad MH, et al. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ, 2014, 349: g5630.
11. 杨楠, 肖淑君, 周奇, 等. GRADE在网状Meta分析中应用的基本原理和方法介绍. 中国循证医学杂志, 2016, 16(5): 598-603.
12. Brignardello-Petersen R, Murad MH, Walter SD, et al. GRADE approach to rate the certainty from a network meta-analysis: avoiding spurious judgments of imprecision in sparse networks. J Clin Epidemiol, 2019, 105: 60-67.
13. Yepes-Nuñez JJ, Li SA, Guyatt G, et al. Development of the summary of findings table for network meta-analysis. J Clin Epidemiol, 2019, 115: 1-13.
14. Schünemann HJ, Wiercioch W, Brozek J, et al. GRADE Evidence to Decision (EtD) frameworks for adoption, adaptation, and de novo development of trustworthy recommendations: GRADE-ADOLOPMENT. J Clin Epidemiol, 2017, 81: 101-110.
15. Brignardello-Petersen R, Florez ID, Izcovich A, et al. GRADE approach to drawing conclusions from a network meta-analysis using a minimally contextualised framework. BMJ, 2020, 371: m3900.
16. Brignardello-Petersen R, Izcovich A, Rochwerg B, et al. GRADE approach to drawing conclusions from a network meta-analysis using a partially contextualised framework. BMJ, 2020, 371: m3907.
17. 栗梦婷, 朱鸿飞, 后亮瑛, 等. 网状Meta结论形成的方法: 最小背景化框架. 中国循证医学杂志, 2021. DOI:10.7507/1672-2531.202105068.
18. Balshem H, Helfand M, Schünemann HJ, et al. GRADE guidelines: 3. rating the quality of evidence. J Clin Epidemiol, 2011, 64(4): 401-406.
19. Hultcrantz M, Rind D, Akl EA, et al. The GRADE Working Group clarifies the construct of certainty of evidence. J Clin Epidemiol, 2017, 87: 4-13.
20. Zhang Y, Alonso-Coello P, Guyatt GH, et al. GRADE guidelines: 19. assessing the certainty of evidence in the importance of outcomes or values and preferences-risk of bias and indirectness. J Clin Epidemiol, 2019, 111: 94-104.
21. Zhang Y, Coello PA, Guyatt GH, et al. GRADE guidelines: 20. assessing the certainty of evidence in the importance of outcomes or values and preferences-inconsistency, imprecision, and other domains. J Clin Epidemiol, 2019, 111: 83-93.
22. Schünemann HJ, Lerda D, Dimitrova N, et al. Methods for development of the European commission initiative on breast cancer guidelines: recommendations in the era of guideline transparency. Ann Intern Med, 2019, 171(4): 273-280.
23. Schünemann HJ. Interpreting GRADE's levels of certainty or quality of the evidence: GRADE for statisticians, considering review information size or less emphasis on imprecision? J Clin Epidemiol, 2016, 75: 6-15.
24. Alonso-Coello P, Schünemann HJ, Moberg J, et al. GRADE Evidence to Decision (EtD) frameworks: a systematic and transparent approach to making well informed healthcare choices. 1: introduction. BMJ, 2016, 353: i2016.
25. Alonso-Coello P, Oxman AD, Moberg J, et al. GRADE Evidence to Decision (EtD) frameworks: a systematic and transparent approach to making well informed healthcare choices. 2: clinical practice guidelines. BMJ, 2016, 353: i2089.
26. Schünemann HJ, Mustafa R, Brozek J, et al. GRADE guidelines: 16. GRADE evidence to decision frameworks for tests in clinical practice and public health. J Clin Epidemiol, 2016, 76: 89-98.
27. Santesso N, Glenton C, Dahm P, et al. GRADE guidelines 26: informative statements to communicate the findings of systematic reviews of interventions. J Clin Epidemiol, 2020, 119: 126-135.
28. 王琪, 王韵华, 赖鸿皓, 等. 网状Meta分析证据质量分级: CINeMA在线应用程序简介. 中国循证医学杂志, 2020, 20(9): 1111-1116.
29. Florez ID, Veroniki AA, Al Khalifah R, et al. Comparative effectiveness and safety of interventions for acute diarrhea and gastroenteritis in children: A systematic review and network meta-analysis. PLoS One, 2018, 13(12): e0207701.

Chinese Journal of Evidence-Based Medicine

Method to draw conclusions from a network meta-analysis: a partially contextualised framework

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