Movatterモバイル変換

[0]ホーム
URL:

Skip to main content
Springer Nature Link
Log in

Network meta-analysis: an introduction for clinicians

  • CE - COCHRANE'S CORNER
  • Published:
Internal and Emergency Medicine Aims and scope Submit manuscript

Abstract

Network meta-analysis is a technique for comparing multiple treatments simultaneously in a single analysis by combining direct and indirect evidence within a network of randomized controlled trials. Network meta-analysis may assist assessing the comparative effectiveness of different treatments regularly used in clinical practice and, therefore, has become attractive among clinicians. However, if proper caution is not taken in conducting and interpreting network meta-analysis, inferences might be biased. The aim of this paper is to illustrate the process of network meta-analysis with the aid of a working example on first-line medical treatment for primary open-angle glaucoma. We discuss the key assumption of network meta-analysis, as well as the unique considerations for developing appropriate research questions, conducting the literature search, abstracting data, performing qualitative and quantitative synthesis, presenting results, drawing conclusions, and reporting the findings in a network meta-analysis.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Chapter© 2018

References

  1. Guyatt GH, Sackett DL, Sinclair JC et al (1995) Users’ guides to the medical literature. IX. A method for grading health care recommendations. Evidence-based Medicine Working Group. JAMA 274:1800–1804. doi:10.1001/jama.281.21.2029

    Article CAS PubMed  Google Scholar 

  2. Brighton B, Bhandari M, Tornetta P, Felson DT (2003) Hierarchy of evidence: from case reports to randomized controlled trials. Clin Orthop Relat Res. doi:10.1097/01.blo.0000079323.41006.12

    PubMed  Google Scholar 

  3. Evans D (2003) Hierarchy of evidence: a framework for ranking evidence evaluating healthcare interventions. J Clin Nurs 12:77–84. doi:10.1046/j.1365-2702.2003.00662.x

    Article PubMed  Google Scholar 

  4. Cipriani A, Higgins JPT, Geddes JR, Salanti G (2013) Conceptual and technical challenges in network meta-analysis. Ann Intern Med 159:130–137. doi:10.7326/0003-4819-159-2-201307160-00008

    Article PubMed  Google Scholar 

  5. Biondi-Zoccai G, Abbate A, Benedetto U et al (2015) Network meta-analysis for evidence synthesis: what is it and why is it posed to dominate cardiovascular decision making? Int J Cardiol 182:309–314. doi:10.1016/j.ijcard.2015.01.023

    Article PubMed  Google Scholar 

  6. Salanti G (2012) 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 3:80–97. doi:10.1002/jrsm.1037

    Article PubMed  Google Scholar 

  7. Li T, Puhan MA, Vedula SS et al (2011) Network meta-analysis-highly attractive but more methodological research is needed. BMC Med 9:79. doi:10.1186/1741-7015-9-79

    Article PubMed PubMed Central  Google Scholar 

  8. Li T, Lindsley K, Rouse B et al (2016) Comparative effectiveness of first-line medications for primary open-angle glaucoma: a systematic review and network meta-analysis. Ophthalmology 123:129–140. doi:10.1016/j.ophtha.2015.09.005

    Article PubMed  Google Scholar 

  9. Michelessi M, Lindsley K, Yu T, Li T (2014) Combination medical treatment for primary open angle glaucoma and ocular hypertension: a network meta-analysis. Cochrane Database Syst Rev. doi:10.1002/14651858.CD011366

    PubMed Central  Google Scholar 

  10. Yu Q, Zhu Z, Liu Y et al (2015) Efficacy and safety of HER2-targeted agents for breast cancer with HER2-overexpression: a network meta-analysis. PLoS One 10:e0127404. doi:10.1371/journal.pone.0127404

    Article PubMed PubMed Central  Google Scholar 

  11. Russell R, Chung M, Balk EM et al (2009) Issues and challenges in conducting systematic reviews to support development of nutrient reference values: workshop summary. Agency for Healthcare Research and Quality (US), Rockville

    Google Scholar 

  12. Caldwell DM (2014) An overview of conducting systematic reviews with network meta-analysis. Syst Rev 3:109. doi:10.1186/2046-4053-3-109

    Article PubMed PubMed Central  Google Scholar 

  13. Mavridis D, Giannatsi M, Cipriani A, Salanti G (2015) A primer on network meta-analysis with emphasis on mental health. Evid Based Ment Heal 18:40–46. doi:10.1136/eb-2015-102088

    Article  Google Scholar 

  14. Sturtz S, Bender R (2012) Unsolved issues of mixed treatment comparison meta-analysis: network size and inconsistency. Res Synth Methods 3:300–311. doi:10.1002/jrsm.1057

    Article  Google Scholar 

  15. Li L, Tian J, Tian H et al (2014) Network meta-analyses could be improved by searching more sources and by involving a librarian. J Clin Epidemiol 67:1001–1007. doi:10.1016/j.jclinepi.2014.04.003

    Article PubMed  Google Scholar 

  16. Mills EJ, Thorlund K, Ioannidis JP (2013) Demystifying trial networks and network meta-analysis. BMJ Br Med J 2914:10–15. doi:10.1136/bmj.f2914

    Google Scholar 

  17. Deeks JJ, Higgins JP, Altman DG (2011) Chapter 9. Analysing data and undertaking meta-analyses, Cochrane Handb Syst Rev Interv

    Google Scholar 

  18. Higgins JPT, Jackson D, Barrett JK et al (2012) Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods 3:98–110. doi:10.1002/jrsm.1044

    Article CAS PubMed PubMed Central  Google Scholar 

  19. White IR (2015) Network meta-analysis. Stat J 15:951–985

    Google Scholar 

  20. Salanti G, Higgins JP, Ades A, Ioannidis JP (2008) Evaluation of networks of randomized trials. Stat Methods Med Res 17:279–301. doi:10.1177/0962280207080643

    Article PubMed  Google Scholar 

  21. Lu G, Ades AE (2004) Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med 23:3105–3124. doi:10.1002/sim.1875

    Article CAS PubMed  Google Scholar 

  22. Chaimani A, Higgins JPT, Mavridis D et al (2013) Graphical tools for network meta-analysis in STATA. PLoS One 8:e76654. doi:10.1371/journal.pone.0076654

    Article CAS PubMed PubMed Central  Google Scholar 

  23. van Valkenhoef G, Lu G, de Brock B et al (2012) Automating network meta-analysis. Res Synth Methods 3:285–299. doi:10.1002/jrsm.1054

    Article PubMed  Google Scholar 

  24. Dias S, Welton NJ, Sutton AJ et al (2013) Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Making 33:641–656. doi:10.1177/0272989X12455847

    Article PubMed PubMed Central  Google Scholar 

  25. White IR, Barrett JK, Jackson D, Higgins JPT (2012) Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Res Synth Methods 3:111–125. doi:10.1002/jrsm.1045

    Article PubMed PubMed Central  Google Scholar 

  26. Song F, Xiong T, Parekh-Bhurke S et al (2011) Inconsistency between direct and indirect comparisons of competing interventions: meta-epidemiological study. BMJ Br Med J 343:d4909. doi:10.1136/bmj.d4909

    Article  Google Scholar 

  27. Cipriani A, Barbui C, Salanti G et al (2011) Comparative efficacy and acceptability of antimanic drugs in acute mania: a multiple-treatments meta-analysis. Lancet 378:1306–1315. doi:10.1016/S0140-6736(11)60873-8

    Article CAS PubMed  Google Scholar 

  28. Salanti G, Ades A, Ioannidis JP (2011) Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol 64:163–171. doi:10.1016/j.jclinepi.2010.03.016

    Article PubMed  Google Scholar 

  29. Medeiros FA (2014) Biomarkers and surrogate endpoints in glaucoma clinical trials. Br J Ophthalmol. doi:10.1136/bjophthalmol-2014-305550

    PubMed PubMed Central  Google Scholar 

  30. Rouse B, Cipriani A, Shi Q et al (2016) Network meta-analysis for clinical practice guidelines—a case study on first-line medical therapies for primary open-angle glaucoma. Ann Intern Med 164:674–682. doi:10.7326/M15-2367

    Article PubMed  Google Scholar 

  31. Guyatt GH, Oxman AD, Vist GE et al (2008) GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 336:924–926. doi:10.1136/bmj.39489.470347.AD

    Article PubMed PubMed Central  Google Scholar 

  32. Guyatt GH, Oxman AD, Montori V et al (2011) GRADE guidelines: 5. Rating the quality of evidence—publication bias. J Clin Epidemiol 64:1277–1282. doi:10.1016/j.jclinepi.2011.01.011

    Article PubMed  Google Scholar 

  33. Salanti G, Del Giovane C, Chaimani A et al (2014) Evaluating the quality of evidence from a network meta-analysis. PLoS One. doi:10.1371/journal.pone.0099682

    Google Scholar 

  34. Puhan MA, Schunemann HJ, Murad MH et al (2014) A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ Br Med J 349:g5630. doi:10.1136/bmj.g5630

    Article  Google Scholar 

  35. Leucht S, Chaimani A, Cipriani AS et al (2016) Network meta-analyses should be the highest level of evidence in treatment guidelines. Eur Arch Psychiatry Clin Neurosci. doi:10.1007/s00406-016-0715-4

    Google Scholar 

  36. Moher D, Liberati A, Tetzlaff J et al (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (Reprinted from Annals of Internal Medicine). PLoS Med 6:e1000097. doi:10.1371/journal.pmed.1000097

    Article PubMed PubMed Central  Google Scholar 

  37. Hutton B, Salanti G, Caldwell DM et al (2015) The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med 162:777. doi:10.7326/M14-2385

    Article PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Clinical Trials and Evidence Synthesis, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Benjamin Rouse & Tianjing Li

  2. Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, University Campus Ioannina, Ioannina, Greece

    Anna Chaimani

Authors
  1. Benjamin Rouse

    You can also search for this author inPubMed Google Scholar

  2. Anna Chaimani

    You can also search for this author inPubMed Google Scholar

  3. Tianjing Li

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toTianjing Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Statement of human and animal rights

The present work did not involve human participants and/or animals.

Informed consent

None.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rouse, B., Chaimani, A. & Li, T. Network meta-analysis: an introduction for clinicians.Intern Emerg Med12, 103–111 (2017). https://doi.org/10.1007/s11739-016-1583-7

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp