Software to Conduct a Meta-Analysis and Network Meta-Analysis.
BUGS
Meta-analysis
MetaInsight
MetaXL
NetMetaXL
Network meta-analysis
R
Review Manager
SAS
Stata
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
22
9
2021
pubmed:
23
9
2021
medline:
8
1
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 34550594
doi: 10.1007/978-1-0716-1566-9_14
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
223-244Informations de copyright
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.
Références
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
doi: 10.1186/1471-2288-7-40
pubmed: 17845719
pmcid: 2048970
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
doi: 10.1371/journal.pone.0115065
pubmed: 25541687
pmcid: 4277278
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
doi: 10.1136/ebmental-2019-300117
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
doi: 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
doi: 10.1186/s12874-019-0829-2
pubmed: 31640567
pmcid: 6805536
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
doi: 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
doi: 10.1177/1536867X1501500403
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
doi: 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
doi: 10.1002/bimj.201800071
pubmed: 30062789
pmcid: 6282623
Efthimiou O (2018) Practical guide to the meta-analysis of rare events. Evid Based Ment Health 21(2):72–76
doi: 10.1136/eb-2018-102911
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
doi: 10.1002/sim.2528
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
doi: 10.1002/sim.1761
pubmed: 15116347
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
doi: 10.1016/s0033-0620(85)80003-7
pubmed: 2858114
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
doi: 10.1002/sim.3511
pubmed: 19072749
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
pubmed: 13655060
pmcid: 13655060
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
doi: 10.1016/j.spl.2011.02.010
McCullagh P, Nelder JA (1989) Generalised linear models, 2nd edn. Chapman and Hall, London
doi: 10.1007/978-1-4899-3242-6
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
doi: 10.1177/0962280214534409
pubmed: 24823642
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
doi: 10.1177/096228020101000404
pubmed: 11491414
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
doi: 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
doi: 10.1002/sim.6383
pubmed: 25446971
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
doi: 10.1002/sim.8158
pubmed: 30997687
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
doi: 10.1002/jrsm.1164
pubmed: 26332144
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
doi: 10.1002/jrsm.1045
pubmed: 4433771
pmcid: 4433771
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
doi: 10.1002/jrsm.1397
pubmed: 31955519
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
doi: 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
doi: 10.1016/j.jclinepi.2014.08.012
pubmed: 25304503
pmcid: 4270451
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
doi: 10.1002/sim.6381
pubmed: 25475839
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
doi: 10.1002/jrsm.1230
pubmed: 28058794
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
doi: 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
doi: 10.1186/1471-2288-14-106
pubmed: 25239546
pmcid: 4190337
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
doi: 10.1002/jrsm.1044
pubmed: 4433772
pmcid: 4433772
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
doi: 10.1186/1471-2288-13-35
pubmed: 23496991
pmcid: 3644268
Dias S, Welton NJ, Caldwell DM, Ades AE (2010) Checking consistency in mixed treatment comparison meta-analysis. Stat Med 29:932–944
doi: 10.1002/sim.3767
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
doi: 10.1002/jrsm.1195
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
doi: 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
doi: 10.1371/journal.pmed.1003082
pubmed: 32243458
pmcid: 32243458
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
doi: 10.1016/j.jclinepi.2009.06.006
pubmed: 19631507
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
doi: 10.1371/journal.pone.0099682
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
doi: 10.1186/s12874-015-0060-8
pubmed: 26227148
pmcid: 4521472
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
doi: 10.1371/journal.pone.0076654
pubmed: 3789683
pmcid: 3789683
Schwarzer G, Carpenter JR, Rücker G (2015) Meta-analysis with R. Springer International Publishing, Switzerland
doi: 10.1007/978-3-319-21416-0
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
doi: 10.1002/sim.4040
pubmed: 20827667
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
doi: 10.1002/sim.5453
pubmed: 22763950
pmcid: 3546377
Rucker G, Peropoulou M, Schwarzer G (2019) Network meta-analysis of mulicomponent interventions. Biom J 62(3):808–821
doi: 10.1002/bimj.201800167
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
doi: 10.1093/aje/kwp014
pubmed: 19258485
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
doi: 10.1186/s12874-019-0829-2
pubmed: 31640567
pmcid: 6805536
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
doi: 10.1002/sim.8360
pubmed: 31583750
pmcid: 6899819
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
doi: 10.1002/pst.533
pubmed: 22213533
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
doi: 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
doi: 10.1002/pst.1857
pubmed: 29676023
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
doi: 10.1002/jrsm.1373
pubmed: 31349391
pmcid: 6973101
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
doi: 10.1186/2046-4053-3-110
pubmed: 25267416
pmcid: 4195340
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
doi: 10.1177/0272989X12458724