Sensitivity and Homogeneity Analysis in Network Meta-analysis Using R Software_Chinese Journal of Evidence-Based Medicine

Authors：

 YANGBo-wen ^1,2 , CHENXin ^1,2 , SUNHao ^1,2 ,  SHIJing-pu ^1,2

1. Department of Clinical Epidemiology, First Alienated Hospital, China Medical University, Shenyang 110001, China;
2. Evidence-Based Medicine Center of Liaoning Province, Shenyang 110001, China;

Corresponding?author：

SHIJing-pu, Email: sjp56@yahoo.com

Keywords：

Network meta-analysis; R software; WinBUGS; Sensitivity; Homogeneity

DOI：

10.7507/1672-2531.20150017

Video：

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Objective To introduce sensitivity and homogeneity tests in network meta-analysis and its implementation in R software. Methods Using an example, we performed sensitivity analysis by comparing the random effect model with the fixed effect model. Homogeneity analysis was performed using metaphor package and combinat package in R software. Results The results of the two models were similar, and the data was steady. The results of homogeneity analysis showed that the confidential intervals in all loops were crossed over with blank value; and direct and indirect estimates of the effects in network meta-analysis were not significantly different, with good homogeneity. Conclusion Network meta-analysis is a kind of indirect comparison analysis method, and its sensitivity is especially important. The introduction of homogeneity makes network meta-analysis more accurate. Using R software for sensitivity and homogeneity analysis in network meta-analysis is a feasible method.

Citation： YANGBo-wen, CHENXin, SUNHao, SHIJing-pu. Sensitivity and Homogeneity Analysis in Network Meta-analysis Using R Software. Chinese Journal of Evidence-Based Medicine, 2015, 15(1): 99-102. doi: 10.7507/1672-2531.20150017 Copy

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3.	張天嵩, 熊茜. 網絡Meta分析在 R 軟件中的實現. 循證醫學, 2012, 12(3):185-188.
4.	曾憲濤, 張超, 郭毅. R軟件R2WinBUGS程序包在網狀Meta分析中的應用. 中國循證醫學雜志 2013, 13(9):1137-1144.
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6.	Dias S, Welton N, Caldwell D, et al. Checking consistency in mixed treatment comparison meta‐analysis. Statistics in Medicine, 2010, 29(7-8):932-944.
7.	Veroniki AA, Vasiliadis HS, Higgins JP, et al. Evaluation of inconsistency in networks of interventions. Int J Epidemiol, 2013, 42(1):332-345.
8.	Jansen JP, Cope S. Meta-regression models to address heterogeneity and inconsistency in network meta-analysis of survival outcomes. BMC Med Res Methodol, 2012, 8(12):152.
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10.	張超鄢, 孫鳳, 劉晴, 等. 網狀Meta分析一致性的鑒別與處理方法. 中國循證醫學雜志, 2014, 14(7):884-888.

1. Lumley T. Network meta-analysis for indirect treatment comparisons. Statistics in Medicine, 2002, 21(6):2313-2324.
2. 曾憲濤, 曹世義, 孫鳳, 等. Meta分析系列之六-間接比較及網狀 Meta 分析. 中國循證心血管醫學雜志, 2012, 4(5):399-402.
3. 張天嵩, 熊茜. 網絡Meta分析在 R 軟件中的實現. 循證醫學, 2012, 12(3):185-188.
4. 曾憲濤, 張超, 郭毅. R軟件R2WinBUGS程序包在網狀Meta分析中的應用. 中國循證醫學雜志 2013, 13(9):1137-1144.
5. 董圣杰, 冷衛東, 田家祥, 等. Meta分析系列之五:貝葉斯Meta分析與 WinBUGS 軟件. 中國循證心血管醫學雜志, 2013, 4(5):395-398.
6. Dias S, Welton N, Caldwell D, et al. Checking consistency in mixed treatment comparison meta‐analysis. Statistics in Medicine, 2010, 29(7-8):932-944.
7. Veroniki AA, Vasiliadis HS, Higgins JP, et al. Evaluation of inconsistency in networks of interventions. Int J Epidemiol, 2013, 42(1):332-345.
8. Jansen JP, Cope S. Meta-regression models to address heterogeneity and inconsistency in network meta-analysis of survival outcomes. BMC Med Res Methodol, 2012, 8(12):152.
9. Higgins J, Jackson D, Barrett J, et al. Consistency and inconsistency in network meta‐analysis:concepts and models for multi‐arm studies. Research Synthesis Methods, 2012, 3(2):98-110.
10. 張超鄢, 孫鳳, 劉晴, 等. 網狀Meta分析一致性的鑒別與處理方法. 中國循證醫學雜志, 2014, 14(7):884-888.

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