Differentiation and Handling of Homogeneity in Network Meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

 ZHANGChao ¹ , YANJin-zhu ¹ , SUNFeng ² , LIUQing ³ , GUOYi ³ ,  ZENGXian-tao ¹

1. Center for Evidence-based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China;
2. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China;
3. Department of Epidemiology, School of Public Health, Wuhan University, Wuhan 430071, China;

Corresponding author：

ZENGXian-tao, Email: zengxiantao1128@163.com

Keywords：

Network meta-analysis; Homogeneity; Heterogeneity; Evidence transitivity

DOI：

10.7507/1672-2531.20140146

Video：

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Compared with traditional head to head meta-analysis, network meta-analysis has more confounding factors and difficulties to handle. Due to the mutual transitivity of evidence in network meta-analysis, heterogeneity may be brought into indirect meta-analysis. Hence, effective differentiation and correct handling of heterogeneity are being current focus. In order to ensure the reliability of the results of network meta-analysis, the concept of homogeneity is proposed and a series of methods are developed for differentiation and handling of homogeneity. Based on the extension of Bucher methods, current methods for differentiation and handling of homogeneity has extended to ten quantitative measures (eg., node analysis method, hypothesis tests, and two-step method). However, because of the differences and the focus of fundamental methodological theories as well as the limitation of statistics power, no highly-effective method has been worked out. Therefore, the exploration of highly-effective, simple and high-resolved methods are still needed.

Citation： ZHANGChao, YANJin-zhu, SUNFeng, LIUQing, GUOYi, ZENGXian-tao. Differentiation and Handling of Homogeneity in Network Meta-analysis. Chinese Journal of Evidence-Based Medicine, 2014, 14(7): 884-888. doi: 10.7507/1672-2531.20140146 Copy

1.	罗杰, 冷卫东, 主编.系统评价/Meta分析理论与实践.北京:军事医学科学出版社, 2013.
2.	Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med, 2002, 21(16):2313-24.
3.	曾宪涛, 曹世义, 孙凤, 等. Meta分析系列之六:间接比较及网状Meta分析.中国循证心血管医学杂志, 2012, 4(5):399-402.
4.	Veroniki AA, Vasiliadis HS, Higgins JP, et al. Evaluation of inconsistency in networks of interventions. Int J Epidemiol, 2013, 42(1):332-345.
5.	Jansen JP, Cope S. Meta-regression models to address heterogeneity and inconsistency in network meta-analysis of survival outcomes. BMC Med Res Methodol, 2012, 12:152.
6.	Bucher HC, Guyatt GH, Griffith LE, et al. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol, 1997, 50(6):683-691.
7.	Dias S, Welton NJ, Sutton AJ, et al. Evidence synthesis for decision making 4:inconsistency in networks of evidence based on randomized controlled trials. Med Decis Making, 2013, 33(5):641-656.
8.	Dias S, Welton NJ, Caldwell DM, et al. Checking consistency in mixed treatment comparison meta-analysis. Stat Med, 2010, 29(7-8):932-944.
9.	Sutton A, Ades AE, Cooper N, et al. Use of indirect and mixed treatment comparisons for technology assessment. Pharmacoeconomics, 2008, 26(9):753-767.
10.	Madan J, Stevenson MD, Cooper KL, et al. Consistency between direct and indirect trial evidence:is direct evidence always more reliable? Value Health, 2011, 14(6):953-960.
11.	Song F, Loke Y, Walsh T, et al. Methodological problems in the use of indirect comparisons for evaluating healthcare interventions:survey of published systematic reviews. BMJ, 2009, 338:b1147.
12.	曾宪涛, 张超, 郭毅. R软件R2WinBUGS程序包在网状Meta分析中的应用.中国循证医学杂志, 2013, 13(9):1137-1144.
13.	张超, 董胜杰, 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.
14.	张超, 孙凤, 曾宪涛. R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.
15.	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.
16.	张超, 牛玉明, 曾宪涛. R软件nlme程序包在网状Meta分析中的应用.中国循证医学杂志, 2014, 14(3):355-360.
17.	张超, 耿培亮, 郭毅, 等.应用R语言netmeta程序包实现网状Meta分析.中国循证医学杂志, 2014, 14(5):625-630.
18.	曾宪涛, 主编.应用STATA做Meta分析.北京:军事医学科学出版社, 2014.
19.	曾宪涛, 徐畅, 张超, 等.网状Meta分析在Stata软件中的实现.中国循证医学杂志, 2013, 13(11):1387-1391.
20.	Eckermann S, Coory M, Willan AR. Indirect comparison:relative risk fallacies and odds solution. J Clin Epidemiol, 2009, 62(10):1031-1036.
21.	Naci H, Fleurence R. Using indirect evidence to determine the comparative effectiveness of prescription drugs:do benefits outweigh risks? Health Outcomes Res Med, 2011, 2(4):e241-e249.
22.	Achana FA, Cooper NJ, Dias S, et al. Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis. Stat Med, 2013, 32(5):752-771.
23.	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.
24.	曾宪涛, 田国祥, 张超, 等. Meta分析系列之十五:Meta分析的进展与思考.中国循证心血管医学杂志, 2013, 5(6):561-563, 587.
25.	Sturtz S, Bender R. Unsolved issues of mixed treatment comparison meta-analysis:network size and inconsistency. Res Synth Methods, 2012, 3(4):300-311.
26.	Song F, Clark A, Bachmann M, Maas J. Simulation evaluation of statistical properties of methods for indirect and mixed treatment comparisons. BMC Med Res Methodol, 2012, 12:138.
27.	田金徽, Song FJ, 李伦, 等.间接比较方法简介.中国循证医学杂志, 2014, 14(3):365-368.
28.	Vandermeer BW, Buscemi N, Liang Y, et al. Comparison of meta-analytic results of indirect, direct, and combined comparisons of drugs for chronic insomnia in adults:a case study. Med Care, 2007, 45(10 Supl 2):S166-S172.
29.	Mills E, Bansback N, Ghement I, et al. Multiple treatment comparison meta-analyses:a step forward into complexity. Clin Epidemiol, 2011, 3:193-202.
30.	Dias S, Welton NJ, Ades AE. Study designs to detect sponsorship and other biases in systematic reviews. J Clin Epidemiol, 2010, 63(6):587-588.
31.	Chung H, Lumley T. Graphical exploration of network meta-analysis data:the use of multidimensional scaling. Clin Trials, 2008, 5(4):301-307.
32.	Lu G, Welton N, Higgins J, et al. Linear inference for mixed treatment comparison meta-analysis:a two-stage approach. Res Synth Methods, 2011, 2(1):43-60.
33.	Rücker G. Network meta-analysis, electrical networks and graph theory. Res Synth Methods, 2012, 3(4):312-324.
34.	Piepho HP. Network-meta analysis made easy:Detection of inconsistency using factorial analysis-of-variance models. BMC Med Res Methodol, 2014, 14(1):61.
35.	Wilkinson GN, Rogers CE. Symbolic description of factorial models for analysis of variance. Appl Stat, 1973, 22(3):392-399.
36.	鄢金柱, 李胜, 翁鸿, 等.应用SAS宏命令实现二分类数据的Meta分析.中国循证心血管医学杂志, 2014, 6(2):21-124.
37.	曾宪涛, 邝心颖, 孙燕, 等.什么是循证医学?湖北医药学院学报, 2013, 32(1):1-5.

1. 罗杰, 冷卫东, 主编.系统评价/Meta分析理论与实践.北京:军事医学科学出版社, 2013.
2. Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med, 2002, 21(16):2313-24.
3. 曾宪涛, 曹世义, 孙凤, 等. Meta分析系列之六:间接比较及网状Meta分析.中国循证心血管医学杂志, 2012, 4(5):399-402.
4. Veroniki AA, Vasiliadis HS, Higgins JP, et al. Evaluation of inconsistency in networks of interventions. Int J Epidemiol, 2013, 42(1):332-345.
5. Jansen JP, Cope S. Meta-regression models to address heterogeneity and inconsistency in network meta-analysis of survival outcomes. BMC Med Res Methodol, 2012, 12:152.
6. Bucher HC, Guyatt GH, Griffith LE, et al. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol, 1997, 50(6):683-691.
7. Dias S, Welton NJ, Sutton AJ, et al. Evidence synthesis for decision making 4:inconsistency in networks of evidence based on randomized controlled trials. Med Decis Making, 2013, 33(5):641-656.
8. Dias S, Welton NJ, Caldwell DM, et al. Checking consistency in mixed treatment comparison meta-analysis. Stat Med, 2010, 29(7-8):932-944.
9. Sutton A, Ades AE, Cooper N, et al. Use of indirect and mixed treatment comparisons for technology assessment. Pharmacoeconomics, 2008, 26(9):753-767.
10. Madan J, Stevenson MD, Cooper KL, et al. Consistency between direct and indirect trial evidence:is direct evidence always more reliable? Value Health, 2011, 14(6):953-960.
11. Song F, Loke Y, Walsh T, et al. Methodological problems in the use of indirect comparisons for evaluating healthcare interventions:survey of published systematic reviews. BMJ, 2009, 338:b1147.
12. 曾宪涛, 张超, 郭毅. R软件R2WinBUGS程序包在网状Meta分析中的应用.中国循证医学杂志, 2013, 13(9):1137-1144.
13. 张超, 董胜杰, 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.
14. 张超, 孙凤, 曾宪涛. R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.
15. 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.
16. 张超, 牛玉明, 曾宪涛. R软件nlme程序包在网状Meta分析中的应用.中国循证医学杂志, 2014, 14(3):355-360.
17. 张超, 耿培亮, 郭毅, 等.应用R语言netmeta程序包实现网状Meta分析.中国循证医学杂志, 2014, 14(5):625-630.
18. 曾宪涛, 主编.应用STATA做Meta分析.北京:军事医学科学出版社, 2014.
19. 曾宪涛, 徐畅, 张超, 等.网状Meta分析在Stata软件中的实现.中国循证医学杂志, 2013, 13(11):1387-1391.
20. Eckermann S, Coory M, Willan AR. Indirect comparison:relative risk fallacies and odds solution. J Clin Epidemiol, 2009, 62(10):1031-1036.
21. Naci H, Fleurence R. Using indirect evidence to determine the comparative effectiveness of prescription drugs:do benefits outweigh risks? Health Outcomes Res Med, 2011, 2(4):e241-e249.
22. Achana FA, Cooper NJ, Dias S, et al. Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis. Stat Med, 2013, 32(5):752-771.
23. 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.
24. 曾宪涛, 田国祥, 张超, 等. Meta分析系列之十五:Meta分析的进展与思考.中国循证心血管医学杂志, 2013, 5(6):561-563, 587.
25. Sturtz S, Bender R. Unsolved issues of mixed treatment comparison meta-analysis:network size and inconsistency. Res Synth Methods, 2012, 3(4):300-311.
26. Song F, Clark A, Bachmann M, Maas J. Simulation evaluation of statistical properties of methods for indirect and mixed treatment comparisons. BMC Med Res Methodol, 2012, 12:138.
27. 田金徽, Song FJ, 李伦, 等.间接比较方法简介.中国循证医学杂志, 2014, 14(3):365-368.
28. Vandermeer BW, Buscemi N, Liang Y, et al. Comparison of meta-analytic results of indirect, direct, and combined comparisons of drugs for chronic insomnia in adults:a case study. Med Care, 2007, 45(10 Supl 2):S166-S172.
29. Mills E, Bansback N, Ghement I, et al. Multiple treatment comparison meta-analyses:a step forward into complexity. Clin Epidemiol, 2011, 3:193-202.
30. Dias S, Welton NJ, Ades AE. Study designs to detect sponsorship and other biases in systematic reviews. J Clin Epidemiol, 2010, 63(6):587-588.
31. Chung H, Lumley T. Graphical exploration of network meta-analysis data:the use of multidimensional scaling. Clin Trials, 2008, 5(4):301-307.
32. Lu G, Welton N, Higgins J, et al. Linear inference for mixed treatment comparison meta-analysis:a two-stage approach. Res Synth Methods, 2011, 2(1):43-60.
33. Rücker G. Network meta-analysis, electrical networks and graph theory. Res Synth Methods, 2012, 3(4):312-324.
34. Piepho HP. Network-meta analysis made easy:Detection of inconsistency using factorial analysis-of-variance models. BMC Med Res Methodol, 2014, 14(1):61.
35. Wilkinson GN, Rogers CE. Symbolic description of factorial models for analysis of variance. Appl Stat, 1973, 22(3):392-399.
36. 鄢金柱, 李胜, 翁鸿, 等.应用SAS宏命令实现二分类数据的Meta分析.中国循证心血管医学杂志, 2014, 6(2):21-124.
37. 曾宪涛, 邝心颖, 孙燕, 等.什么是循证医学?湖北医药学院学报, 2013, 32(1):1-5.

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