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Abstract

Clinicians are often faced with results of randomised controlled trials comparing different interventions for a condition. However, selecting from a range of interventions is difficult when no head-to-head trials are comparing their safety and efficacy. Network meta-analysis (NMA) extends the principles of conventional meta-analysis to allow assessment of multiple treatments in a single analysis. Hence it is also called as multiple treatment meta-analysis or mixed treatment comparisons. Importantly NMA can provide evidence on ‘relative ranking’ of multiple interventions. The ability to synthesise indirect evidence and evaluate multiple interventions with a common comparator in one analysis separates NMA from conventional pairwise meta-analyses. NMA is vital for evidence-based decision-making because it allows assessment of direct as well as indirect evidence. Given its complexity compared with conventional meta-analyses, the involvement of both subject experts and experienced biostatistician is necessary when planning an NMA. This is particularly important because crucial judgements and assumptions are involved. This chapter briefly covers the principles of NMA.

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References

  • Chaimani A, Caldwell DM, Li T, Higgins JPT, Salanti G. Chapter 11: Undertaking network meta-analyses. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. Cochrane handbook for systematic reviews of interventions version 6.0 (updated July 2019). Cochrane; 2019. www.training.cochrane.org/handbook.

  • Cipriani A, Higgins JP, Geddes JR, Salanti G. Conceptual and technical challenges in network meta-analysis. Ann Intern Med. 2013;159:130–7.

    Article  Google Scholar 

  • Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ. Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: Application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Stat Med. 2009;28:1861–81.

    Article  Google Scholar 

  • Dias S, Caldwell DM. Network meta-analysis explained. Arch Dis Child Fetal Neonatal Ed January 2019; 104, 1: F8-F12.

    Google Scholar 

  • Dias S, Ades AE, Welton NJ, Jansen JP, Sutton AJ. Network meta‐analysis for decision making. First published: 12 January 2018 Print ISBN: 9781118647509|Online ISBN: 9781118951651|https://doi.org/10.1002/9781118951651 © 2018 John Wiley & Sons Ltd.

  • Dias S, Welton NJ, Caldwell DM, Ades AE. Checking consistency in mixed treatment comparison meta-analysis. Stat Med. 2010;29:932–44.

    Article  CAS  Google Scholar 

  • Dias S, Welton NJ, Sutton AJ, Caldwell DM, Lu G, Ades AE. Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomised controlled trials. Med Decis Making. 2013;33:641–56.

    Article  Google Scholar 

  • Dobler CC, Wilson ME, Murad MH. A pulmonologist’s guide to understanding network meta-analysis. Eur Respir J. 2018;52:1800525. https://doi.org/10.1183/13993003.00525-2018.

    Article  PubMed  Google Scholar 

  • Donegan S, Williamson P, D’Alessandro U, Tudur Smith C. Assessing key assumptions of network meta-analysis: A review of methods. Res Syn Methods. 2013;4:291–323.

    Article  Google Scholar 

  • Faltinsen EG, Storebø OJ, Jakobsen JC, Boesen K, Lange T, Gluud C. Network meta-analysis: the highest level of medical evidence? BMJ Evid Based Med. 2018;23(2):56–9.

    Article  Google Scholar 

  • Higgins JPT, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: Concepts and models for multi-arm studies. Res Syn Methods. 2012;3:98–110.

    Article  CAS  Google Scholar 

  • Hoaglin DC, Hawkins N, Jansen JP, et al. Conducting indirect-treatment-comparison and network-meta-analysis studies: Report of the ISPOR task force on indirect treatment comparisons good research practices: Part 2. Value Health. 2011;14:429–37.

    Article  Google Scholar 

  • 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;162:777–84.

    Article  Google Scholar 

  • Jansen JP, Fleurence R, Devine B, et al. Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: Report of the ISPOR task force on indirect treatment comparisons good research practices: part 1. Value Health. 2011;14:417–28.

    Article  Google Scholar 

  • Kiefer C, Sturtz S, Bender R. Indirect comparisons and network meta-Analyses. Dtsch Arztebl Int. 2015;112(47):803–8. https://doi.org/10.3238/arztebl.2015.0803.

    Article  PubMed  PubMed Central  Google Scholar 

  • Krahn U, Binder H, König J. Visualizing inconsistency in network meta-analysis by independent path decomposition. BMC Med Res Methodol. 2014;14:131. https://doi.org/10.1186/1471-2288-14-131.

    Article  PubMed  PubMed Central  Google Scholar 

  • Li T, Puhan MA, Vedula SS, Singh S, Dickersin K, The Ad Hoc Network Meta-analysis Methods Meeting Working Group. Network meta-analysis-highly attractive but more methodological research is needed. BMC Med. 2011; 9: 79. https://doi.org/10.1186/1741-7015-9-79.

  • Lu G, Ades A. Modelling between-trial variance structure in mixed treatment comparisons. Biostatistics. 2009;10:792–805.

    Article  Google Scholar 

  • Mills EJ, Ioannidis JP, Thorlund K, Schünemann HJ, Puhan MA, Guyatt GH. How to use an article reporting a multiple treatment comparison meta-analysis. JAMA. 2012;308:1246–53.

    Article  CAS  Google Scholar 

  • Mills EJ, Thorlund K, Ioannidis JP. Demystifying trial networks and network meta-analysis. BMJ. 2013;346:

    Article  Google Scholar 

  • Puhan MA, Schünemann HJ, Murad MH, et al for the GRADE Working Group. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ 2014; 349:g5630. https://doi.org/10.1136/bmj.g5630 (Published 24 September 2014).

  • Quan H, Zhang B, Chuang-Stein C, Jones B & On behalf of the EFSPI integrated data analysis efficacy working group. integrated data analysis for assessing treatment effect through combining information from all sources. Stat Biopharm Res. 2017; 9:1, 52–64.

    Google Scholar 

  • Rouse B, Chaimani A, Li T. Network meta-analysis: an introduction for clinicians. Intern Emerg Med. 2017;12(1):103–11.

    Article  Google Scholar 

  • Salanti G. 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 Syn Methods. 2012;3:80–97.

    Article  Google Scholar 

  • Salanti G, Del Giovane C, Chaimani A, Caldwell DM, Higgins JP. Evaluating the quality of evidence from a network meta-analysis. PLoS One. 2014;9(7):e99682. Published 2014 Jul 3. https://doi.org/10.1371/journal.pone.0099682.

  • Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol. 2011;64(2):163–71.

    Article  Google Scholar 

  • Senn S, Gavini F, Magrez D, Scheen A. Issues in performing a network meta-analysis. Stat Methods Med Res. 2013;22(2):169–89.

    Article  Google Scholar 

  • Spiegelhalter DJ. Incorporating Bayesian ideas into health-care evaluation. Stat Sci. 2004;19:156–74.

    Article  Google Scholar 

  • Spiegelhalter DJ, Abrams KR, Myles J. Bayesian approaches to clinical trials and health-care evaluation. New York: Wiley; 2004.

    Google Scholar 

  • Sturtz S, Bender R. Unsolved issues of mixed treatment comparison meta-analysis: Network size and inconsistency. Res Syn Methods. 2012;3:300–11.

    Article  Google Scholar 

  • ter Veer E, van Oijen MGH, van Laarhoven HWM. The use of (network) meta-analysis in clinical oncology. Front Oncol. 27 August 2019| https://doi.org/10.3389/fonc.2019.00822.

  • Tonin FS, Rotta I, Mendes AM, Pontarolo R. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons. Pharm Prac 2017 Jan-Mar;15(1):943: 1–11. https://doi.org/10.18549/PharmPract.2017.01.943.

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Correspondence to Sanjay Patole .

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Patole, S. (2021). Network Meta-Analysis. In: Patole, S. (eds) Principles and Practice of Systematic Reviews and Meta-Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-71921-0_16

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