Abstract
Network meta-analysis is used to synthesize evidence from a network of treatments. The models used in a network meta-analysis are more complex than those used for pairwise meta-analysis. Two types of models are available to undertake a network meta-analysis: contrast-based and arm-based models. Contrast-based models have been used in most published network meta-analyses. Arm-based models offer greater flexibility and handle treatments symmetrically, but risk compromising randomization. In this chapter, we (1) present the contrast-based and arm-based statistical models; (2) describe the theoretical differences between the models (noting when the estimates from the models are expected to diverge); (3) summarize the evidence comparing the two models from simulation studies and empirical investigations; and (4) provide a worked example applying the two models to a network using the R software package.
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References
Efthimiou O, Debray TP, van Valkenhoef G, Trelle S, Panayidou K, Moons KG, Reitsma JB, Shang A, Salanti G, GetReal Methods Review G (2016) GetReal in network meta-analysis: a review of the methodology. Res Synth Methods 7(3):236–263. https://doi.org/10.1002/jrsm.1195
Hong H, Chu H, Zhang J, Carlin BP (2016) A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons. Res Synth Methods 7(1):6–22. https://doi.org/10.1002/jrsm.1153
Zhang J, Carlin BP, Neaton JD, Soon GG, Nie L, Kane R, Virnig BA, Chu H (2014) Network meta-analysis of randomized clinical trials: reporting the proper summaries. Clin Trials 11(2):246–262. https://doi.org/10.1177/1740774513498322
Dias S, Ades AE (2016) Absolute or relative effects? Arm-based synthesis of trial data. Res Synth Methods 7(1):23–28. https://doi.org/10.1002/jrsm.1184
Lu G, Ades AE (2004) Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med 23(20):3105–3124. https://doi.org/10.1002/sim.1875
White IR, Turner RM, Karahalios A, Salanti G (2019) A comparison of arm-based and contrast-based models for network meta-analysis. Stat Med 38(27):5197–5213. https://doi.org/10.1002/sim.8360
Salanti G, Higgins JP, Ades AE, Ioannidis JP (2008) Evaluation of networks of randomized trials. Stat Methods Med Res 17(3):279–301. https://doi.org/10.1177/0962280207080643
Lu G, Ades A (2009) Modeling between-trial variance structure in mixed treatment comparisons. Biostatistics 10(4):792–805. https://doi.org/10.1093/biostatistics/kxp032
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
Turner RM, Davey J, Clarke MJ, Thompson SG, Higgins JP (2012) Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane database of systematic reviews. Int J Epidemiol 41(3):818–827. https://doi.org/10.1093/ije/dys041
Piepho HP, Williams ER, Madden LV (2012) The use of two-way linear mixed models in multitreatment meta-analysis. Biometrics 68(4):1269–1277. https://doi.org/10.1111/j.1541-0420.2012.01786.x
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(2):80–97. https://doi.org/10.1002/jrsm.1037
Lin L, Chu H, Hodges JS (2016) Sensitivity to excluding treatments in network meta-analysis. Epidemiology 27(4):562–569. https://doi.org/10.1097/EDE.0000000000000482
Karahalios AE, Salanti G, Turner SL, Herbison GP, White IR, Veroniki AA, Nikolakopoulou A, McKenzie JE (2017) An investigation of the impact of using different methods for network meta-analysis: a protocol for an empirical evaluation. Syst Rev 6(1):119. https://doi.org/10.1186/s13643-017-0511-x
Elliott WJ, Meyer PM (2007) Incident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis. Lancet 369(9557):201–207. https://doi.org/10.1016/S0140-6736(07)60108-1
White IR (2015) Network meta-analysis. Stata J 15:1–34. https://www.stata-journal.com/article.html?article=st0410
van Valkenhoef G, Kuiper J (2016) gemtc: Network Meta-Analysis Using Bayesian Methods; 2016, https://CRAN.R-project.org/package=gemtc, r package version 0.8–2
Lin L, Zhang J, Hodges JS, Chu H (2017) Performing arm-based network meta-analysis in R with the pcnetmeta package. J Stat Softw 80:5. https://doi.org/10.18637/jss.v080.i05
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
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Karahalios, A., McKenzie, J.E., White, I.R. (2022). Contrast-Based and Arm-Based Models for 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_13
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DOI: https://doi.org/10.1007/978-1-0716-1566-9_13
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