Result Interpretation of Network Meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

 YIYue-xiong ,  ZHANGWei , LIUXiao-yuan , ZHANGJuan , ZHUDing-jun , LVQiong-ying

First Department of Gynecology, Renmin Hospital of Wuhan University, Wuhan 430060, China;

Corresponding author：

ZHANGWei, Email: zw6676@163.com

Keywords：

Network meta-analysis; Graphic result; Interpretation

DOI：

10.7507/1672-2531.20150018

Video：

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Comparison among multiple interventions has been realized due to the development of network meta-analysis and so far many studies have reported its implementation process. However, its results are rarely interpreted in domestic studies at present. This article interprets the results of trace plots, density plots, Brooks-Gelman-Rubin diagnosis plots, rankogram, surface under the cumulative ranking, and network plots, to provide references and assistance for further research regarding network meta-analysis.

Citation： YIYue-xiong, ZHANGWei, LIUXiao-yuan, ZHANGJuan, ZHUDing-jun, LVQiong-ying. Result Interpretation of Network Meta-analysis. Chinese Journal of Evidence-Based Medicine, 2015, 15(1): 103-109. doi: 10.7507/1672-2531.20150018 Copy

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2.	曾宪涛, 张超, 杜亮. 应用ADDIS软件实现网状Meta分析. 中国循证医学杂志, 2013, 13(12):1508-1515.
3.	沈可, 王芬, 张超, 等. 网状Meta分析在JAGS软件中的实现. 数理医药学杂志, 2014, (4):446-450, 451.
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7.	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.
8.	Brooks SP, Roberts GO. Assessing convergence of Markov chain Monte Carlo algorithms. Stat Comput, 1998, 8(4):319-335.
9.	Toft N, Innocent GT, Gettinby G, et al. Assessing the convergence of Markov Chain Monte Carlo methods:an example from evaluation of diagnostic tests in absence of a gold standard. Prev Vet Med, 2007, 79(2):244-256.
10.	Roberts GO, Rosenthal JS. Examples of adaptive MCMC. J Comput Graph Stat, 2009, 18(2):349-367.
11.	Inc, SAS Institute. SAS/STAT^® 9.2 User's Guide, Second Edition[EB/OL]. 2009.
12.	Crosbie S, Corliss D. Are You Dense? Using Kernel Density Estimation (KDE) to Connect the Dots Amidst Uncertainty. 2012.
13.	Jones MC, Daly F. Density probability plots. Commun Stat-Simul C, 1995, 24(4):911-927.
14.	Turlach BA. Bandwidth selection in kernel density estimation:A review. Université catholique de Louvain, 1993.
15.	Jones MC, Marron JS, Sheather SJ. A brief survey of bandwidth selection for density estimation. J Am Stat Assoc, 1996, 91(433):401-407.
16.	Cox NJ. Speaking Stata:Density probability plots. Stata Journal, 2005, 5(2):259-273.
17.	Valkenhoef G, Lu G, Brock B, et al. Automating network meta-analysis. Research Synthesis Methods, 2012, 3(4):285-299.
18.	Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat, 1998, 7(4):434-455.
19.	Tunaru R. Hierarchical Bayesian models for multiple count data. Austrian Journal of statistics, 2002, 31(3):221-229.
20.	Woods BS, Hawkins N, Scott DA. Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials:a tutorial. BMC Med Res Methodol, 2010, 10:54.
21.	Chaimani A, Higgins JP, Mavridis D, et al. Graphical tools for network meta-analysis in STATA. PloS one, 2013, 8(10):e76654.
22.	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.
23.	Salanti G, Ades AE, Ioannidis J. 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.
24.	Cope S, Jansen JP. Quantitative summaries of treatment effect estimates obtained with network meta-analysis of survival curves to inform decision-making. BMC Med Res Methodol, 2013, 13:147.
25.	Salanti G, Dias S, Welton NJ, et al. Evaluating novel agent effects in multiple‐treatments meta-regression. Stat Med, 2010, 29(23):2369-2383.
26.	Fruchterman TM, Reingold EM. Graph drawing by force-directed placement. Software:Practice and experience, 1991, 21(11):1129-1164.
27.	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.
28.	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.

1. Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med, 2002, 21(16):2313-2324.
2. 曾宪涛, 张超, 杜亮. 应用ADDIS软件实现网状Meta分析. 中国循证医学杂志, 2013, 13(12):1508-1515.
3. 沈可, 王芬, 张超, 等. 网状Meta分析在JAGS软件中的实现. 数理医药学杂志, 2014, (4):446-450, 451.
4. 张天嵩, 熊茜. 网络Meta分析在R软件中的实现. 循证医学, 2012, 12(3):185-188.
5. R Development Core Team. R:a language and environment for statistical computing. Vienna:R Foundation for Statistical Computing. Available at:http://www.R-project.org.
6. van Valkenhoef G, Kuiper J. gemtc:Network Meta-Analysis Using Bayesian Methods. R package version 0.6-1. Available at:http://CRAN.R-project.org/package=gemtc.
7. 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.
8. Brooks SP, Roberts GO. Assessing convergence of Markov chain Monte Carlo algorithms. Stat Comput, 1998, 8(4):319-335.
9. Toft N, Innocent GT, Gettinby G, et al. Assessing the convergence of Markov Chain Monte Carlo methods:an example from evaluation of diagnostic tests in absence of a gold standard. Prev Vet Med, 2007, 79(2):244-256.
10. Roberts GO, Rosenthal JS. Examples of adaptive MCMC. J Comput Graph Stat, 2009, 18(2):349-367.
11. Inc, SAS Institute. SAS/STAT^® 9.2 User's Guide, Second Edition[EB/OL]. 2009.
12. Crosbie S, Corliss D. Are You Dense? Using Kernel Density Estimation (KDE) to Connect the Dots Amidst Uncertainty. 2012.
13. Jones MC, Daly F. Density probability plots. Commun Stat-Simul C, 1995, 24(4):911-927.
14. Turlach BA. Bandwidth selection in kernel density estimation:A review. Université catholique de Louvain, 1993.
15. Jones MC, Marron JS, Sheather SJ. A brief survey of bandwidth selection for density estimation. J Am Stat Assoc, 1996, 91(433):401-407.
16. Cox NJ. Speaking Stata:Density probability plots. Stata Journal, 2005, 5(2):259-273.
17. Valkenhoef G, Lu G, Brock B, et al. Automating network meta-analysis. Research Synthesis Methods, 2012, 3(4):285-299.
18. Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat, 1998, 7(4):434-455.
19. Tunaru R. Hierarchical Bayesian models for multiple count data. Austrian Journal of statistics, 2002, 31(3):221-229.
20. Woods BS, Hawkins N, Scott DA. Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials:a tutorial. BMC Med Res Methodol, 2010, 10:54.
21. Chaimani A, Higgins JP, Mavridis D, et al. Graphical tools for network meta-analysis in STATA. PloS one, 2013, 8(10):e76654.
22. 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.
23. Salanti G, Ades AE, Ioannidis J. 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.
24. Cope S, Jansen JP. Quantitative summaries of treatment effect estimates obtained with network meta-analysis of survival curves to inform decision-making. BMC Med Res Methodol, 2013, 13:147.
25. Salanti G, Dias S, Welton NJ, et al. Evaluating novel agent effects in multiple‐treatments meta-regression. Stat Med, 2010, 29(23):2369-2383.
26. Fruchterman TM, Reingold EM. Graph drawing by force-directed placement. Software:Practice and experience, 1991, 21(11):1129-1164.
27. 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.
28. 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.

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