Abstract
Statistical software for meta-analysis (MA) and network meta-analysis (NMA) have become indispensable for researchers. The aim of this chapter is to introduce key features of MA and NMA software to compare the effectiveness of interventions. Commonly used or routinely maintained statistical software are reviewed, including commercial and open-sourced programs such as Stata, R and Excel plug-ins. It does not provide a comprehensive overview of all features available in the software covered. Rather, it focuses on the essential features required to carry out an MA or NMA . This chapter begins with a review of key considerations when implementing an MA or NMA , then presents a summary of the software. Key features of each software option are discussed.
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References
Bax L, Yu LM, Ikeda N, Moons KG (2007) A systematic comparison of software dedicated to meta-analysis of causal studies. BMC Med Res Methodol 7:40. https://doi.org/10.1186/1471-2288-7-40
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
Balduzzi S, Rücker G, Schwarzer G (2019) How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health 22(4):153–160
Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J Statistical Software 36(3):48. https://doi.org/10.18637/jss.v036.i03
Rücker G, Krahn U, König J, Efthimiou O, Schwarzer G (2020) netmeta: Network Meta-Analysis using Frequentist Methods. . R package version 1.2-1 edn
van Valkenhoef G, Kuiper J (2016) gemtc: Network Meta-Analysis Using Bayesian Methods. . R package version 0.8.2 edn. CRAN
Béliveau A, Boyne DJ, Slater J, Brenner D, Arora P (2019) BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network meta-analyses. BMC Med Res Methodol 19(1):196. https://doi.org/10.1186/s12874-019-0829-2
Lin L, Zhang J, Hodges JS, Chu H (2017) Performing arm-based network meta-analysis in R with the pcnetmeta package. J Statistical Software 80(5):1–25. https://doi.org/10.18637/jss.v080.i05
Palmer TM, Sterne JAC (2016) Meta-analysis in Stata: an updated collection from the Stata journal. Stata Press, College Station, Texas
White IR (2015) Network meta-analysis. Stata J 15(4):951–985
Chaimani A, Salanti G (2015) Visualizing assumptions and results in network meta-analysis: the network graphs package. Stata J 15(4):905–950. https://doi.org/10.1177/1536867X1501500402
National Institute for Health and Care Excellence (2019) Technical Support Documents. http://nicedsu.org.uk/technical-support-documents/
Jackson D, White IR (2018) When should meta-analysis avoid making hidden normality assumptions? Biom J 60(6):1040–1058. https://doi.org/10.1002/bimj.201800071
Efthimiou O (2018) Practical guide to the meta-analysis of rare events. Evid Based Ment Health 21(2):72–76
Bradburn MJ, Deeks JJ, Berlin JA, Localio AR (2007) Much ado about nothing: a comparison of the performance of meta-analysis methods with rare events. Stat Med 26:53–77
Sweeting MJ, Sutton AJ, Lambert PC (2004) What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Stat Med 23(9):1351–1375. https://doi.org/10.1002/sim.1761
Yusuf S, Peto R, Lewis J, Collins R, Sleight P (1985) Beta blockade during and after myocardial infarction: an overview of the randomized trials. Prog Cardiovasc Dis 27(5):335–371. https://doi.org/10.1016/s0033-0620(85)80003-7
Rücker G, Schwarzer G, Carpenter J, Olkin I (2009) Why add anything to nothing? The arcsine difference as a measure of treatment effect in meta-analysis with zero cells. Stat Med 28(5):721–738. https://doi.org/10.1002/sim.3511
Mantel N, Haenszel W (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22(4):719–748
Senn S, Weir J, Hua TA, Berlin C, Branson M, Glimm E (2011) Creating a suite of macros for meta-analysis in SAS®: a case study in collaboration. Stat Probab Lett 81(7):842–851. https://doi.org/10.1016/j.spl.2011.02.010
McCullagh P, Nelder JA (1989) Generalised linear models, 2nd edn. Chapman and Hall, London
Simmonds MC, Higgins JP (2016) A general framework for the use of logistic regression models in meta-analysis. Stat Methods Med Res 25(6):2858–2877. https://doi.org/10.1177/0962280214534409
Sutton AJ, Abrams KR (2001) Bayesian methods in meta-analysis and evidence synthesis. Stat Methods Med Res 10(4):277–303. https://doi.org/10.1177/096228020101000404
Dias S, Ades AE, Welton NJ, Jansen JP, Sutton AJ (2018) Network meta-analysis for decision making. Statistics in Practice. Wiley, Hoboken, New Jersey. https://doi.org/10.1002/9781118951651
Kuss O (2015) Statistical methods for meta-analyses including information from studies without any events—add nothing to nothing and succeed nevertheless. Stat Med 34(7):1097–1116. https://doi.org/10.1002/sim.6383
Efthimiou O, Rucker G, Schwarzer G, Higgins JPT, Egger M, Salanti G (2019) Network meta-analysis of rare events using the Mantel-Haenszel method. Stat Med 38(16):2992–3012. https://doi.org/10.1002/sim.8158
Dias S, Welton NJ, Sutton AJ, Ades AE (2011) NICE DSU technical support document 2: a generalised linear modelling framework for pair-wise and network meta-analysis of randomised controlled trials. Technical Support Document
Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, Kuss O, Higgins JPT, Langan D, Salanti G (2016) Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods 7(1):55–79. https://doi.org/10.1002/jrsm.1164
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(2):111–125. https://doi.org/10.1002/jrsm.1045
Seide SE, Jensen K, Kieser M (2020) A comparison of Bayesian and frequentist methods in random-effects network meta-analysis of binary data. Res Synth Methods 11(3):363–378. https://doi.org/10.1002/jrsm.1397
Wagenmakers E-J, Lee M, Lodewyckx T, Iverson GJ (2008) Bayesian versus frequentist inference. In: Hoijtink H, Klugkist I, Boelen PA (eds) Bayesian evaluation of informative hypotheses. Springer New York, New York, NY, pp 181–207. https://doi.org/10.1007/978-0-387-09612-4_9
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
Turner RM, Jackson D, Wei Y, Thompson SG, Higgins JP (2015) Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis. Stat Med 34(6):984–998. https://doi.org/10.1002/sim.6381
Borenstein M, Higgins JPT, Hedges LV, Rothstein HR (2017) Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Res Synth Methods 8(1):5–18. https://doi.org/10.1002/jrsm.1230
Dias S, Welton NJ, Sutton AJ, Caldwell DM, Lu G, Ades AE (2011) NICE DSU technical support document 4: inconsistency in networks of evidence based on randomised controlled trials. Technical Support Document
Dias S, Welton NJ, Sutton AJ, Caldwell DM, Lu G, Ades AE (2013) Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Mak 33:641–656. https://doi.org/10.1177/0272989X12455847
Veroniki AA, Mavridis D, Higgins JPT, 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):106. https://doi.org/10.1186/1471-2288-14-106
Higgins JPT, 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
Krahn U, Binder H, Konig J (2013) A graphical tool for locating inconsistency in network meta-analyses. BMC Med Res Methodol 13:35. https://doi.org/10.1186/1471-2288-13-35
Dias S, Welton NJ, Caldwell DM, Ades AE (2010) Checking consistency in mixed treatment comparison meta-analysis. Stat Med 29:932–944
Efthimiou O, Debray TP, van Valkenhoef G, Trelle S, Panayidou K, Moons KG, Reitsma JB, Shang A, Salanti G (2016) GetReal in network meta-analysis: a review of the methodology. Res Synth Methods 7(3):236–263
Brooks SP, Gelman A (1998) General methods for monitoring convergence of iterative simulations. J Comput Graph Stat 7(4):434–455
Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–472
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
Nikolakopoulou A, Higgins JPT, Papakonstantinou T, Chaimani A, Del Giovane C, Egger M, Salanti G (2020) CINeMA: an approach for assessing confidence in the results of a network meta-analysis. PLoS Med 17(4):e1003082. https://doi.org/10.1371/journal.pmed.1003082
Dias S, Welton NJ, Sutton AJ, Ades AE (2011) NICE DSU technical support document 1: introduction to evidence synthesis for decision making. Technical Support Document
Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 62(10):e1–e34. https://doi.org/10.1016/j.jclinepi.2009.06.006
Page MJHJ, Sterne JAC (2016) Chapter 13: assessing risk of bias due to missing results in a synthesis. In: Higgins JPTTJ, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (eds) Cochrane handbook for systematic reviews of interventions version 6.0 (updated July 2019). Cochrane
Salanti G, Giovane CD, Chaimani A, Caldwell D, Higgins J (2014) Evaluating the quality of evidence from a network meta-analysis. PLoS one 9(7):ee99682
Rucker G, Schwarzer G (2015) Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol 15:58. https://doi.org/10.1186/s12874-015-0060-8
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
Schwarzer G, Carpenter JR, Rücker G (2015) Meta-analysis with R. Springer International Publishing, Switzerland
Röver C (2020) Bayesian random-effects meta-analysis using the bayesmeta R package. J Stat Softw 93(6). https://doi.org/10.18637/jss.v093.i06
Gelman A, Meng X, Stern H (1996) Posterior predictive assessment of model fitness via realized discrepancies. Stat Sin 6:733–807
StataCorp (2019) Stata meta-analysis reference manual. vol Release 16. College Statioin, TX
The Nordic Cochrane Centre (2014) Review Manager (RevMan). Version 5.3. edn. The Cochrane Collaboration, Copenhagen
Barendregt JJ, Doi SA (2019) MetaXL User Guide. Version 5.3 edn., Sunrise Beach, Queensland, Australia
Stijnen T, Hamza TH, Ozdemir P (2010) Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data. Stat Med 29(29):3046–3067. https://doi.org/10.1002/sim.4040
Jackson D, White IR, Riley RD (2012) Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Stat Med 31(29):3805–3820. https://doi.org/10.1002/sim.5453
Rucker G, Peropoulou M, Schwarzer G (2019) Network meta-analysis of mulicomponent interventions. Biom J 62(3):808–821
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
Beliveau A, Boyne DJ, Slater J, Brenner D, Arora P (2019) BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network meta-analyses. BMC Med Res Methodol 19(1):196. https://doi.org/10.1186/s12874-019-0829-2
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
Jones B, Roger J, Lane PW, Lawton A, Fletcher C, Cappelleri JC, Tate H, Moneuse P, on behalf of Psi Health Technology Special Interest Group ESs-t (2011) Statistical approaches for conducting network meta-analysis in drug development. Pharm Stat 10(6):523–531. https://doi.org/10.1002/pst.533
SAS Institute Inc. (2018) The GLIMMIX Procedure. In: SAS/STAT® 15.1 User’s Guide. SAS Institute Inc., Cary, NC
Tu Y-K (2014) Use of generalized linear mixed models for network meta-analysis. Med Decis Mak 34(7):911–918. https://doi.org/10.1177/0272989X14545789
Piepho H-P, Madden LV, Roger J, Payne R, Williams ER (2018) Estimating the variance for heterogeneity in arm-based network meta-analysis. Pharm Stat 17(3):264–277. https://doi.org/10.1002/pst.1857
Chen F, Brown G (2016) Stokes M Fitting Your Favorite Mixed Models with PROC MCMC. In: Inc. SI (ed) SAS Global Forum 2016, Las Vegas, NV, 2016. SAS Institute Inc.
The Wine Project (2020) Wine. 5.0 edn
Owen RK, Bradbury N, Xin Y, Cooper N, Sutton A (2019) MetaInsight: an interactive web-based tool for analyzing, interrogating, and visualizing network meta-analyses using R-shiny and netmeta. Res Synth Methods 10(4):569–581. https://doi.org/10.1002/jrsm.1373
Brown S, Hutton B, Clifford T, Coyle D, Grima D, Wells G, Cameron C (2014) A Microsoft-excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL. Syst Rev 3:110. https://doi.org/10.1186/2046-4053-3-110
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. https://doi.org/10.1177/0272989X12458724
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Daly, C., Soobiah, C. (2022). Software to Conduct a Meta-Analysis and 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_14
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