Application of bnma package of R software in Bayesian network meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

LIANG Xingyu , CHEN Xi , ZHANG Guilin , LI Xuemei ,  ZHANG Hong

School of Acupuncture-Moxibustion and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, P. R. China;

Corresponding author：

ZHANG Hong, Email: hzhang0123@foxmail.com

Keywords：

R software; bnma package; Network meta-analysis; Bayesian statistics

DOI：

10.7507/1672-2531.202309048

Video：

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Abstract Full text Figures/Tables Video References Cited by

The "bnma" package is a Bayesian network meta-analysis software developed based on the R programming language. The network meta-analysis was performed utilizing JAGS software, which yielded relevant results and visual graphs. Moreover, this software package provides support for various data structures and types, while also providing the advantages of flexible utilization, user-friendly operation, and delivering rich and accurate outcomes. In this paper, using a network meta-analysis example of different therapies for androgenetic alopecia, the operational process of conducting network meta-analysis using the "bnma" package is briefly introduced.

1.	曾宪涛, 张超, 李胜, 等. WinBUGS软件实现Meta分析的Doodle模型图的构建. 中国循证医学杂志, 2014, 14(1): 101-109.
2.	张超, 孙凤, 曾宪涛. R软件调用JAGS软件实现网状Meta分析. 中国循证医学杂志, 2014, 14(2): 241-248.
3.	罗杰, 张超, 曾宪涛. R软件调用OpenBUGS软件实现网状Meta分析. 中国循证医学杂志, 2014, 14(4): 492-496.
4.	田金徽, 张俊华, 李伦, 等. 中国学者发表网状Meta分析现状分析. 中国药物评价, 2017, 34(1): 1-4.
5.	张超, 董圣杰, 曾宪涛. R软件gemtc程序包在网状Meta分析中的应用. 中国循证医学杂志, 2013, 13(10): 1258-1264.
6.	李胜, 张超, 杜亮, 等. 应用R软件pcnetmeta程序包实现网状Meta分析. 中国循证医学杂志, 2015, 15(2): 230-235.
7.	Green PL, Worden K. Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty. Philos Trans A Math Phys Eng Sci, 2015, 373(2051): 20140405.
8.	Dias S, Sutton AJ, Ades AE, et al. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making, 2013, 33(5): 607-617.
9.	Hutton B, Salanti G, Caldwell DM, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med, 2015, 162(11): 777-784.
10.	Feldman PR, Gentile P, Piwko C, et al. Hair regrowth treatment efficacy and resistance in androgenetic alopecia: a systematic review and continuous Bayesian network meta-analysis. Front Med (Lausanne), 2023, 9: 998623.
11.	Hawkins N, Scott DA, Woods B. 'Arm-based' parameterization for network meta-analysis. Res Synth Methods, 2016, 7(3): 306-313.
12.	李晓松. 医学统计学(第2版). 北京: 高等教育出版社, 2008.
13.	Turner RM, Davey J, Clarke MJ, et al. Predicting the extent of heterogeneity in meta-analysis, using empirical data from the cochrane database of systematic reviews. Int J Epidemiol, 2012, 41(3): 818-827.
14.	Brooks S, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat, 1998, 7: 434-455.
15.	van Valkenhoef G, Dias S, Ades AE, et al. Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis. Res Synth Methods, 2016, 7(1): 80-93.
16.	Dias S, Welton NJ, Caldwell DM, et al. Checking consistency in mixed treatment comparison meta-analysis. Stat Med, 2010, 29(7-8): 932-944.
17.	张超, 徐畅, 曾宪涛. 网状Meta分析中网状关系图的绘制. 中国循证医学杂志, 2013, 13(11): 1382-1386.
18.	易跃雄, 张蔚, 刘小媛, 等. 网状Meta分析图形结果解读. 中国循证医学杂志, 2015, 15(1): 103-109.
19.	Madden LV, Piepho HP, Paul PA. Statistical models and methods for network meta-analysis. Phytopathology, 2016, 106(8): 792-806.
20.	Dias S, Ades AE, Welton NJ, et al. Network meta-analysis for decision making: model fit, model comparison and outlier detection (1st edition). Hoboken: John Wiley & Sons, 2018.
21.	Woldeamanuel YW, Oliveira ABD. What is the efficacy of aerobic exercise versus strength training in the treatment of migraine. A systematic review and network meta-analysis of clinical trials. J Headache Pain, 2022, 23(1): 134.
22.	Sall J. Leverage plots for general linear hypotheses. Am Stat, 1990, 44(4): 308-315.
23.	Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med, 2002, 21(16): 2313-2324.
24.	Cipriani A, Furukawa TA, Salanti G, et al. Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis. Lancet, 2009, 373(9665): 746-758.
25.	刘明, 葛龙, 高亚, 等. 网络Meta分析研究进展系列(十六): 网络Meta分析结果的可视化. 中国循证心血管医学杂志, 2021, 13(9): 1031-1035,1041.
26.	Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ, 2001, 322(7300): 1479-1480.
27.	Hawkins N, Scott DA, Woods BS, et al. No study left behind: a network meta-analysis in non-small-cell lung cancer demonstrating the importance of considering all relevant data. Value Health, 2009, 12(6): 996-1003.
28.	Chaimani A, Higgins JP, Mavridis D, et al. Graphical tools for network meta-analysis in STATA. PLoS One, 2013, 8(10): e76654.
29.	Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol, 2011, 64(2): 163-171.
30.	Salanti G, Dias S, Welton NJ, et al. Evaluating novel agent effects in multiple-treatments meta-regression. Stat Med, 2010, 29(23): 2369-2383.
31.	Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods, 2012, 3(2): 80-97.
32.	董圣杰, 冷卫东, 田家祥, 等. Meta分析系列之五: 贝叶斯Meta分析与WinBUGS软件. 中国循证心血管医学杂志, 2012, 4(5): 395-398.

1. 曾宪涛, 张超, 李胜, 等. WinBUGS软件实现Meta分析的Doodle模型图的构建. 中国循证医学杂志, 2014, 14(1): 101-109.
2. 张超, 孙凤, 曾宪涛. R软件调用JAGS软件实现网状Meta分析. 中国循证医学杂志, 2014, 14(2): 241-248.
3. 罗杰, 张超, 曾宪涛. R软件调用OpenBUGS软件实现网状Meta分析. 中国循证医学杂志, 2014, 14(4): 492-496.
4. 田金徽, 张俊华, 李伦, 等. 中国学者发表网状Meta分析现状分析. 中国药物评价, 2017, 34(1): 1-4.
5. 张超, 董圣杰, 曾宪涛. R软件gemtc程序包在网状Meta分析中的应用. 中国循证医学杂志, 2013, 13(10): 1258-1264.
6. 李胜, 张超, 杜亮, 等. 应用R软件pcnetmeta程序包实现网状Meta分析. 中国循证医学杂志, 2015, 15(2): 230-235.
7. Green PL, Worden K. Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty. Philos Trans A Math Phys Eng Sci, 2015, 373(2051): 20140405.
8. Dias S, Sutton AJ, Ades AE, et al. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making, 2013, 33(5): 607-617.
9. Hutton B, Salanti G, Caldwell DM, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med, 2015, 162(11): 777-784.
10. Feldman PR, Gentile P, Piwko C, et al. Hair regrowth treatment efficacy and resistance in androgenetic alopecia: a systematic review and continuous Bayesian network meta-analysis. Front Med (Lausanne), 2023, 9: 998623.
11. Hawkins N, Scott DA, Woods B. 'Arm-based' parameterization for network meta-analysis. Res Synth Methods, 2016, 7(3): 306-313.
12. 李晓松. 医学统计学(第2版). 北京: 高等教育出版社, 2008.
13. Turner RM, Davey J, Clarke MJ, et al. Predicting the extent of heterogeneity in meta-analysis, using empirical data from the cochrane database of systematic reviews. Int J Epidemiol, 2012, 41(3): 818-827.
14. Brooks S, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat, 1998, 7: 434-455.
15. van Valkenhoef G, Dias S, Ades AE, et al. Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis. Res Synth Methods, 2016, 7(1): 80-93.
16. Dias S, Welton NJ, Caldwell DM, et al. Checking consistency in mixed treatment comparison meta-analysis. Stat Med, 2010, 29(7-8): 932-944.
17. 张超, 徐畅, 曾宪涛. 网状Meta分析中网状关系图的绘制. 中国循证医学杂志, 2013, 13(11): 1382-1386.
18. 易跃雄, 张蔚, 刘小媛, 等. 网状Meta分析图形结果解读. 中国循证医学杂志, 2015, 15(1): 103-109.
19. Madden LV, Piepho HP, Paul PA. Statistical models and methods for network meta-analysis. Phytopathology, 2016, 106(8): 792-806.
20. Dias S, Ades AE, Welton NJ, et al. Network meta-analysis for decision making: model fit, model comparison and outlier detection (1st edition). Hoboken: John Wiley & Sons, 2018.
21. Woldeamanuel YW, Oliveira ABD. What is the efficacy of aerobic exercise versus strength training in the treatment of migraine. A systematic review and network meta-analysis of clinical trials. J Headache Pain, 2022, 23(1): 134.
22. Sall J. Leverage plots for general linear hypotheses. Am Stat, 1990, 44(4): 308-315.
23. Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med, 2002, 21(16): 2313-2324.
24. Cipriani A, Furukawa TA, Salanti G, et al. Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis. Lancet, 2009, 373(9665): 746-758.
25. 刘明, 葛龙, 高亚, 等. 网络Meta分析研究进展系列(十六): 网络Meta分析结果的可视化. 中国循证心血管医学杂志, 2021, 13(9): 1031-1035,1041.
26. Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ, 2001, 322(7300): 1479-1480.
27. Hawkins N, Scott DA, Woods BS, et al. No study left behind: a network meta-analysis in non-small-cell lung cancer demonstrating the importance of considering all relevant data. Value Health, 2009, 12(6): 996-1003.
28. Chaimani A, Higgins JP, Mavridis D, et al. Graphical tools for network meta-analysis in STATA. PLoS One, 2013, 8(10): e76654.
29. Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol, 2011, 64(2): 163-171.
30. Salanti G, Dias S, Welton NJ, et al. Evaluating novel agent effects in multiple-treatments meta-regression. Stat Med, 2010, 29(23): 2369-2383.
31. Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods, 2012, 3(2): 80-97.
32. 董圣杰, 冷卫东, 田家祥, 等. Meta分析系列之五: 贝叶斯Meta分析与WinBUGS软件. 中国循证心血管医学杂志, 2012, 4(5): 395-398.

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