Abstract
There are often multiple potential interventions to treat a disease; therefore, we need a method for simultaneously comparing and ranking all of these available interventions. In contrast to pairwise meta-analysis, which allows for the comparison of one intervention to another based on head-to-head data from randomized trials, network meta-analysis (NMA) facilitates simultaneous comparison of the efficacy or safety of multiple interventions that may not have been directly compared in a randomized trial. NMAs help researchers study important and previously unanswerable questions, which have contributed to a rapid rise in the number of NMA publications in the biomedical literature. However, the conduct and interpretation of NMAs are more complex than pairwise meta-analyses: there are additional NMA model assumptions (i.e., network connectivity, homogeneity, transitivity, and consistency) and outputs (e.g., network plots and surface under the cumulative ranking curves [SUCRAs]). In this chapter, we will: (1) explore similarities and differences between pairwise and network meta-analysis; (2) explain the differences between direct, indirect, and mixed treatment comparisons; (3) describe how treatment effects are derived from NMA models; (4) discuss key criteria predicating completion of NMA; (5) interpret NMA outputs; (6) discuss areas of ongoing methodological research in NMA; (7) outline an approach to conducting a systematic review and NMA; (8) describe common problems that researchers encounter when conducting NMAs and potential solutions; and (9) outline an approach to critically appraising a systematic review and NMA.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dias S, Sutton AJ, Ades AE, Welton NJ (2013) Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Mak 33:607–617
Zarin W, Veroniki AA, Nincic V, Varaei A, Reynen E, Motiwala SS, Antony J, Sullivan SM, Rios P, Daly C, Ewusie J, Petropoulou M, Nikolakopoulou A, Chaimani A, Salanti G, Straus SE, Tricco AC (2017) Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review. BMC Med 15(3)
Jansen JP, Trikalinos T, Cappelleri JC, Daw J, Andes S, Eldessouki R, Salanti G (2014) 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 17:157–173
Veroniki AA, Straus SE, Fyraridis A, Tricco AC (2016) The rank-heat plot is a novel way to present the results from a network meta-analysis including multiple outcomes. J Clin Epidemiol 76:193–199. https://doi.org/10.1016/j.jclinepi.2016.02.016
Watt JA, Goodarzi Z, Veroniki AA, Nincic V, Khan PA, Ghassemi M, Thompson Y, Tricco AC, Straus SE (2019) Comparative efficacy of interventions for aggressive and agitated behaviors in dementia: a systematic review and network meta-analysis. Ann Intern Med 171(9):633–642. https://doi.org/10.7326/M19-0993
Puhan MA, Schunemann HJ, Murad MH, Li T, Brignardello-Petersen R, Singh JA, Kessels AG, Guyatt GH, Group GW (2014) A GRADE working group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ 349:g5630. https://doi.org/10.1136/bmj.g5630
Salanti G, Del Giovane C, Chaimani A, Caldwell DM, Higgins JP (2014) Evaluating the quality of evidence from a network meta-analysis. PLoS One 9(7):e99682. https://doi.org/10.1371/journal.pone.0099682
Del Giovane C, Cortese S, Ciprian A (2019) Combining pharmacological and nonpharmacological interventions in network meta-analysis in psychiatry. JAMA Psychiat 76(8). https://doi.org/10.1001/jamapsychiatry.2019.0304
Salanti G, Marinho V, Higgins JP (2009) A case study of multiple-treatments meta-analysis demonstrates that covariates should be considered. J Clin Epidemiol 62(8):857–864. https://doi.org/10.1016/j.jclinepi.2008.10.001
Veroniki AA, Vasiliadis HS, Higgins JPT, Salanti G (2013) Evaluation of inconsistency in networks of interventions. Int J Epidemiol 42:332–345
Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR (2012) Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods 3(2):98–110. https://doi.org/10.1002/jrsm.1044
Veroniki AA, Mavridis D, Higgins JP, Salanti G (2014) Characteristics of a loop of evidence that affect detection and estimation of inconsistency: a simulation study. BMC Med Res Methodol 14:1–12
Dias S, Welton NJ, Sutton AJ, Caldwell DM, Lu G, Ades A (2013) Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Mak 33(5):641–656
Chaimani A, Higgins JP, Mavridis D, Spyridonos P, Salanti G (2013) Graphical tools for network meta-analysis in STATA. PLoS One 8(10):e76654. https://doi.org/10.1371/journal.pone.0076654
Kadane JB (1995) Prime time for Bayes. Control Clin Trials 16:313–318
Cochrane Handbook for Systematic Reviews of Interventions (2011). In: Higgins JPT, Green S (eds)
Brignardello-Petersen R, Bonner A, Alexander PE, Siemieniuk RA, Furukawa TA, Rochwerg B, Hazlewood GS, Alhazzani W, Mustafa RA, Murad MH, Puhan MA, Schunemann HJ, Guyatt GH, Group GW (2018) Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis. J Clin Epidemiol 93:36–44. https://doi.org/10.1016/j.jclinepi.2017.10.005
Del Giovane C, Vacchi L, Mavridis D, Filippini G, Salanti G (2013) Network meta-analysis models to account for variability in treatment definitions: application to dose effects. Stat Med 32(1):25–39. https://doi.org/10.1002/sim.5512
Welton NJ, Caldwell DM, Adamopoulos E, Vedhara K (2009) Mixed treatment comparison meta-analysis of complex interventions: psychological interventions in coronary heart disease. Am J Epidemiol 169(9):1158–1165. https://doi.org/10.1093/aje/kwp014
Efthimiou O, Mavridis D, Debray TP, Samara M, Belger M, Siontis GC, Leucht S, Salanti G, GetReal Work P (2017) Combining randomized and non-randomized evidence in network meta-analysis. Stat Med 36(8):1210–1226. https://doi.org/10.1002/sim.7223
Veroniki AA, Straus S, Soobiah C, Elliot MJ, Tricco AC (2016) A scoping review of indirect comparison methods and applications using individual patient data. BMC Med Res Methodol 16:47
Veroniki AA, Ashoor HM, Le SPC, Rios P, Stewart LA, Clarke M, Mavridis D, Straus SE, Tricco AC (2019) Retrieval of individual patient data depended on study characteristics: a randomized controlled trial. J Clin Epidemiol 113:176–188. https://doi.org/10.1016/j.jclinepi.2019.05.031
Neupane B, Richer D, Bonner AJ, Kibret T, Beyene J (2014) Network meta-analysis using R: a review of currently available automated packages. PLoS One 9(12):e115065. https://doi.org/10.1371/journal.pone.0115065
Salanti G, Ades AE, Ioannidis JPA (2011) Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol 64:163–171
Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, Ioannidis JP, Straus S, Thorlund K, Jansen JP, Mulrow C, Catala-Lopez F, Gotzsche PC, Dickersin K, Boutron I, Altman DG, Moher D (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(11):777–784. https://doi.org/10.7326/M14-2385
Rhodes KM, Turner RM, Higgins JP (2015) Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J Clin Epidemiol 68(1):52–60. https://doi.org/10.1016/j.jclinepi.2014.08.012
Lambert PC, Sutton AJ, Burton PR, Abrams KR, Jones DR (2005) How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med 24(15):2401–2428. https://doi.org/10.1002/sim.2112
Mills EJ, Ioannidis JPA, Thorlund K, Schunemann HJ, Puhan MA, Guyatt GH (2012) How to use an article reporting a multiple treatment comparison meta-analysis. JAMA 308(12):1246–1253
Dias S, Ades AE, Welton NJ, Jansen JP, Sutton AJ (2018) Network meta-analysis for decision-making. Statistics in practice. Wiley, New York
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Watt, J., Del Giovane, C. (2022). Network Meta-Analysis. In: Evangelou, E., Veroniki, A.A. (eds) Meta-Research. Methods in Molecular Biology, vol 2345. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1566-9_12
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1566-9_12
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1565-2
Online ISBN: 978-1-0716-1566-9
eBook Packages: Springer Protocols