Implementation of Bayesian network meta-analysis with BUGSnet package in R software_Chinese Journal of Evidence-Based Medicine

Authors：

XIANG Hong , YUE Lei , ZHAO Hong , LI Hua , XIE Tianhong , YANG Ting , WANG Jie , ZHANG Yuhao ,  XIE Zhongping

Institute of Medical Biology, Chinese Academy of Medical Science and Peking Union Medical College, Yunnan Key Laboratory of Vaccine Research and Development on Severe Infections Disease, Kunming 650118, P. R. China;

Corresponding author：

XIE Zhongping, Email: xzp218@126.com

Keywords：

Network meta-analysis; R software; BUGSnet package; Bayesian inference

DOI：

10.7507/1672-2531.202111140

Video：

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Abstract Full text Figures/Tables Video References Cited by

BUGSnet is a powerful R project package for Bayesian network meta-analysis. The package is based on JAGS and enables high-quality Bayesian network meta-analysis according to recognized reporting guidelines (PRISMA, ISPOR-AMPC-NCA and NICE-DSU). In this paper, we introduced the procedure of the BUGSnet package for Bayesian network meta-analysis through an example of network meta-analysis of steroid adjuvant treatment of pemphigus with continuous or dichotomous data.

Citation： XIANG Hong, YUE Lei, ZHAO Hong, LI Hua, XIE Tianhong, YANG Ting, WANG Jie, ZHANG Yuhao, XIE Zhongping. Implementation of Bayesian network meta-analysis with BUGSnet package in R software. Chinese Journal of Evidence-Based Medicine, 2022, 22(5): 600-608. doi: 10.7507/1672-2531.202111140 Copy

1.	孙凤, 杨智荣, 张天嵩, 等. 网络Meta分析研究进展系列之一: 概述. 中国循证心血管医学杂志, 2020, 12(6): 644-650.
2.	Watt J, Tricco AC, Straus S, et al. Research techniques made simple: network meta-analysis. J Invest Dermatol, 2019, 139(1): 4-12.
3.	Tian J, Gao Y, Zhang J, et al. Progress and challenges of network meta-analysis. J Evid Based Med, 2021, 14(3): 218-231.
4.	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.
5.	Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med, 2004, 23(20): 3105-3124.
6.	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): 1-18.
7.	张天嵩, 孙凤, 董圣杰, 等. 网络Meta分析研究进展系列(二): 网络Meta分析统计模型及模型拟合软件选择. 中国循证心血管医学杂志, 2020, 12(7): 769-774, 793.
8.	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.
9.	郑亮, 兰琴, 周晓慧, 等. STATA软件network模块在两分类数据网络meta分析中的应用. 同济大学学报(医学版), 2018, 39(3): 119-122.
10.	Béliveau A, Boyne DJ, Slater J, et al. BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network meta-analyses. BMC Med Res Methodol, 2019, 19(1): 1-13.
11.	Lee MS, Yeh YC, Tu YK, et al. Network meta-analysis-based comparison of first-line steroid-sparing adjuvants in the treatment of pemphigus vulgaris and pemphigus foliaceus. J Am Acad Dermatol, 2021, 85(1): 176-186.
12.	Rouse B, Chaimani A, Li T. Network meta-analysis: an introduction for clinicians. Intern Emerg Med, 2017, 12(1): 103-111.
13.	Pottie K, Welch V, Morton R, et al. GRADE equity guidelines 4: considering health equity in GRADE guideline development: evidence to decision process. J Clin Epidemiol, 2017, 90: 84-91.
14.	Djulbegovic B, Guyatt GH. Progress in evidence-based medicine: a quarter century on. Lancet, 2017, 390(10092): 415-423.
15.	Murad MH. Clinical practice guidelines: a primer on development and dissemination. Mayo Clin Proc, 2017, 92(3): 423-433.
16.	曾宪涛, 张超, 邝心颖, 等. 基于使用的网状Meta分析软件的比较研究与选择. 中国循证医学杂志, 2014, 14(10): 1270-1275.
17.	Xu C, Niu Y, Wu J, et al. Software and package applicating for network meta-analysis: a usage-based comparative study. J Evid Based Med, 2018, 11(3): 176-183.
18.	Culleton B. Evidence-based decision-making 4: Development and limitations of clinical practice guidelines. Methods Mol Biol, 2015, 1281: 443-453.
19.	Jansen JP, Trikalinos T, Cappelleri JC, et al. Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility to inform health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report. Value Health, 2014, 17(2): 157-173.
20.	Shamseer L, Moher D, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ, 2015, 350: 1-25.

1. 孙凤, 杨智荣, 张天嵩, 等. 网络Meta分析研究进展系列之一: 概述. 中国循证心血管医学杂志, 2020, 12(6): 644-650.
2. Watt J, Tricco AC, Straus S, et al. Research techniques made simple: network meta-analysis. J Invest Dermatol, 2019, 139(1): 4-12.
3. Tian J, Gao Y, Zhang J, et al. Progress and challenges of network meta-analysis. J Evid Based Med, 2021, 14(3): 218-231.
4. 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.
5. Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med, 2004, 23(20): 3105-3124.
6. 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): 1-18.
7. 张天嵩, 孙凤, 董圣杰, 等. 网络Meta分析研究进展系列(二): 网络Meta分析统计模型及模型拟合软件选择. 中国循证心血管医学杂志, 2020, 12(7): 769-774, 793.
8. 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.
9. 郑亮, 兰琴, 周晓慧, 等. STATA软件network模块在两分类数据网络meta分析中的应用. 同济大学学报(医学版), 2018, 39(3): 119-122.
10. Béliveau A, Boyne DJ, Slater J, et al. BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network meta-analyses. BMC Med Res Methodol, 2019, 19(1): 1-13.
11. Lee MS, Yeh YC, Tu YK, et al. Network meta-analysis-based comparison of first-line steroid-sparing adjuvants in the treatment of pemphigus vulgaris and pemphigus foliaceus. J Am Acad Dermatol, 2021, 85(1): 176-186.
12. Rouse B, Chaimani A, Li T. Network meta-analysis: an introduction for clinicians. Intern Emerg Med, 2017, 12(1): 103-111.
13. Pottie K, Welch V, Morton R, et al. GRADE equity guidelines 4: considering health equity in GRADE guideline development: evidence to decision process. J Clin Epidemiol, 2017, 90: 84-91.
14. Djulbegovic B, Guyatt GH. Progress in evidence-based medicine: a quarter century on. Lancet, 2017, 390(10092): 415-423.
15. Murad MH. Clinical practice guidelines: a primer on development and dissemination. Mayo Clin Proc, 2017, 92(3): 423-433.
16. 曾宪涛, 张超, 邝心颖, 等. 基于使用的网状Meta分析软件的比较研究与选择. 中国循证医学杂志, 2014, 14(10): 1270-1275.
17. Xu C, Niu Y, Wu J, et al. Software and package applicating for network meta-analysis: a usage-based comparative study. J Evid Based Med, 2018, 11(3): 176-183.
18. Culleton B. Evidence-based decision-making 4: Development and limitations of clinical practice guidelines. Methods Mol Biol, 2015, 1281: 443-453.
19. Jansen JP, Trikalinos T, Cappelleri JC, et al. Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility to inform health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report. Value Health, 2014, 17(2): 157-173.
20. Shamseer L, Moher D, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ, 2015, 350: 1-25.

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