Progress and challenges of network meta-analysis.
bibliometric analysis
diagnostic test accuracy
individual participant data
methodological advances
network meta-analysis
Journal
Journal of evidence-based medicine
ISSN: 1756-5391
Titre abrégé: J Evid Based Med
Pays: England
ID NLM: 101497477
Informations de publication
Date de publication:
Sep 2021
Sep 2021
Historique:
revised:
03
08
2021
received:
28
04
2021
accepted:
03
08
2021
pubmed:
1
9
2021
medline:
29
10
2021
entrez:
31
8
2021
Statut:
ppublish
Résumé
In the past years, network meta-analysis (NMA) has been widely used among clinicians, guideline makers, and health technology assessment agencies and has played an important role in clinical decision-making and guideline development. To inform further development of NMAs, we conducted a bibliometric analysis to assess the current status of published NMA methodological studies, summarized the methodological progress of seven types of NMAs, and discussed the current challenges of NMAs.
Types de publication
Journal Article
Meta-Analysis
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
218-231Informations de copyright
© 2021 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.
Références
Chaimani A, Higgins JP, Mavridis D, Spyridonos P, Salanti G. Graphical tools for network meta-analysis in STATA. Plos One. 2013;8(10):e76654.
Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med. 2002;21(16):2313-2324.
Li L, Catala-Lopez F, Alonso-Arroyo A, et al. The Global research collaboration of network meta-analysis: a social network analysis. Plos One. 2016;11(9):e0163239.
Cameron C, Fireman B, Hutton B, et al. Network meta-analysis incorporating randomised controlled trials and non-randomised comparative cohort studies for assessing the safety and effectiveness of medical treatments: challenges and opportunities. Syst Rev. 2015;4:147.
Mills EJ, Thorlund K, Ioannidis JPA. Demystifying trial networks and network meta-analysis. BMJ (Clini Res Ed). 2013;346:f2914.
Caldwell DM, Ades AE, Higgins JP. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ (Clini Res Ed). 2005;331(7521):897-900.
Heinecke A, Tallarita M, De Iorio M. Bayesian splines versus fractional polynomials in network meta-analysis. BMC Med Res Methodol [Electr Res]. 2020;20(1):261.
Wang R, Danhof NA, Tjon-Kon-Fat RI, et al. Interventions for unexplained infertility: a systematic review and network meta-analysis. Cochr Datab Syst Rev. 2019;9(9):Cd012692.
Scheiman M, Kulp MT, Cotter SA, Lawrenson JG, Wang L, Li T. Interventions for convergence insufficiency: a network meta-analysis. Cochr Datab Syst Rev. 2020;12:Cd006768.
Shi J, Gao Y, Ming L, et al. A bibliometric analysis of global research output on network meta-analysis. BMC Med Informat Dec Mak [Electr Resour]. 2021;21(1):144.
Noma H, Tanaka S, Matsui S, Cipriani A, Furukawa TA. Quantifying indirect evidence in network meta-analysis. Stat Med. 2017;36(6):917-927.
Lee AW. Review of mixed treatment comparisons in published systematic reviews shows marked increase since 2009. J Clin Epidemiol. 2014;67(2):138-143.
Jansen JP, Schmid CH, Salanti G. Directed acyclic graphs can help understand bias in indirect and mixed treatment comparisons. J Clin Epidemiol. 2012;65(7):798-807.
Ge L, Tian JH, Li XX, et al. Epidemiology characteristics, methodological assessment and reporting of statistical analysis of network meta-analyses in the field of cancer. Sci Rep. 2016;6:37208.
Bafeta A, Trinquart L, Seror R, Ravaud P. Reporting of results from network meta-analyses: methodological systematic review. BMJ (Clini Res Ed). 2014;348:g1741.
Trinquart L, Attiche N, Bafeta A, Porcher R, Ravaud P. Uncertainty in treatment rankings: reanalysis of network meta-analyses of randomised trials. Ann Int Med. 2016;164(10):666-673.
Gao Y, Ge L, Ma X, Shen X, Liu M, Tian J. Improvement needed in the network geometry and inconsistency of Cochrane network meta-analyses: a cross-sectional survey. J Clin Epidemiol. 2019;113:214-227.
Dias S, Welton NJ, Caldwell DM, Ades AE. Checking consistency in mixed treatment comparison meta-analysis. Stat Med. 2010;29(7-8):932-944.
Elliott WJ, Meyer PM. Incident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis. Lancet (Lon, Engl). 2007;369(9557):201-207.
Sutton A, Ades AE, Cooper N, Abrams K. Use of indirect and mixed treatment comparisons for technology assessment. Pharmacoeconomics. 2008;26(9):753-767.
Lawson DO, Puljak L, Pieper D, et al. Reporting of methodological studies in health research: a protocol for the development of the MethodologIcal STudy reportIng Checklist (MISTIC). BMJ Open. 2020;10(12):e040478.
Liang YD, Li Y, Zhao J, Wang XY, Zhu HZ, Chen XH. Study of acupuncture for low back pain in recent 20 years: a bibliometric analysis via CiteSpace. J Pain Res. 2017;10:951-964.
Gao Y, Ge L, Shi S, et al. Global trends and future prospects of e-waste research: a bibliometric analysis. Environ Sci Pollut Res Int. 2019;26(17):17809-17820.
Chen CM, CiteSpace II. Detecting and visualising emerging trends and transient patterns in scientific literature. J Am Soc Info Sci Technol. 2006;57(3):359-377.
Gao Y, Shi S, Ma W, et al. Bibliometric analysis of global research on PD-1 and PD-L1 in the field of cancer. Int Immunopharmacol. 2019;72:374-384.
Shi S, Gao Y, Liu M, et al. Top 100 most-cited articles on exosomes in the field of cancer: a bibliometric analysis and evidence mapping. Clin Exp Med. 2021;1(2):181-194.
Fan J, Gao Y, Zhao N, et al. Bibliometric analysis on COVID-19: a comparison of research between english and chinese studies. Front Public Health. 2020;8:477.
Shi S, Gao Y, Sun Y, et al. The top-100 cited articles on biomarkers in the depression field: a bibliometric analysis. Psychol, Health Med. 2021;26(5):533-542.
Xie P. Study of international anticancer research trends via co-word and document co-citation visualisation analysis. Scientometrics. 2015;105(1):611-622.
Liu M, Gao Y, Yuan Y, et al. Global hotspots and future prospects of chimeric antigen receptor T-cell therapy in cancer research: a bibliometric analysis. Fut Oncol (Lond, Engl). 2020;16(10):597-612.
Gao Y, Yang K, Liu M, et al. Research collaboration and outcome measures of interventional clinical trial protocols for COVID-19 in China. Front Public Health. 2020;8:554247.
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 Int Med. 2015;162(11):777-784.
Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004;23(20):3105-3124.
Bucher HC, Guyatt GH, Griffith LE, Walter SD. The results of direct and indirect treatment comparisons in meta-analysis of randomised controlled trials. J Clin Epidemiol. 1997;50(6):683-691.
Chaimani A, Higgins JPT, Mavridis D, Spyridonos P, Salanti G. Graphical tools for network meta-analysis in STATA. Plos One. 2013;8(10):e76654.
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 Synth Meth. 2012;3(2):80-97.
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 Synth Meth. 2012;3(2):98-110.
Glenny AM, Altman DG, Song F, et al. Indirect comparisons of competing interventions. Health Technol Assess (Winches, Engl). 2005;9(26):1-134. iii-iv.
Puhan MA, Schunemann HJ, Murad MH, et al. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ (Clini Res Ed). 2014;349:g5630.
White IR, Barrett JK, Jackson D, Higgins JPT. Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Res Synth Meth. 2012;3(2):111-125.
Cipriani A, Higgins JPT, Geddes JR, Salanti G. Conceptual and technical challenges in network meta-analysis. Ann Int Med. 2013;159(2):130-137.
Salanti G, Del Giovane C, Chaimani A, Caldwell DM, Higgins JPT. Evaluating the quality of evidence from a network meta-analysis. Plos One. 2014;9(7):e99682.
Song FJ, Loke YK, Walsh T, Glenny AM, Eastwood AJ, Altman DG. Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews. BMJ (Clini Res Ed). 2009;338:b1147.
Jansen JP, Naci H. Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers. BMC Med. 2013;11:159.
van Valkenhoef G, Lu GB, de Brock B, Hillege H, Ades AE, Welton NJ. Automating network meta-analysis. Res Synth Meth. 2012;3(4):285-299.
Mills EJ, Ioannidis JPA, Thorlund K, Schunemann HJ, Puhan MA, Guyatt GH. How to use an article reporting a multiple treatment comparison meta-analysis. JAMA. 2012;308(12):1246-1253.
Rucker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol. 2015;15:58.
Ge L, Pan B, Song F, et al. Comparing the diagnostic accuracy of five common tumour biomarkers and CA19-9 for pancreatic cancer: a protocol for a network meta-analysis of diagnostic test accuracy. BMJ Open. 2017;7(12):e018175.
O'Sullivan JW. Network meta-analysis for diagnostic tests. BMJ Evid-Based Med. 2019;24(5):192-193.
Gao Y, Sun F, Wu S, et al. Advances in methodology of network meta-analysis (5): network meta-analysis of diagnostic test accuracy. Chin J Evid-Based Cardiovasc Med. 2020;12(10):1161-1165.
Trikalinos TA, Hoaglin DC, Small KM, Terrin N, Schmid CH. Methods for the joint meta-analysis of multiple tests. Res Synth Meth. 2014;5(4):294-312.
Menten J, Lesaffre E. A general framework for comparative Bayesian meta-analysis of diagnostic studies. BMC Med Res Methodol [Electr Res]. 2015;15:70.
Dimou NL, Adam M, Bagos PG. A multivariate method for meta-analysis and comparison of diagnostic tests. Stat Med. 2016;35(20):3509-3523.
Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58(10):982-990.
Arends LR, Hamza TH, van Houwelingen JC, Heijenbrok-Kal MH, Hunink MG, Stijnen T. Bivariate random effects meta-analysis of ROC curves. Med Decision Mak. 2008;28(5):621-638.
Nyaga VN, Aerts M, Arbyn M. ANOVA model for network meta-analysis of diagnostic test accuracy data. Stat Meth Med Res. 2018;27(6):1766-1784.
Cheng W. Network Meta-Analysis of Diagnostic Accuracy Studies. Providence, RI: Brown University; 2016.
Hoyer A, Kuss O. Meta-analysis for the comparison of two diagnostic tests to a common gold standard: a generalised linear mixed model approach. Stat Meth Med Res. 2018;27(5):1410-1421.
Owen RK, Cooper NJ, Quinn TJ, Lees R, Sutton AJ. Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making. J Clin Epidemiol. 2018;99:64-74.
Ma X, Lian Q, Chu H, Ibrahim JG, Chen Y. A Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests. Biostatistics (Oxf, Engl). 2018;19(1):87-102.
Lian Q, Hodges JS, Chu H. A bayesian hierarchical summary receiver operating characteristic model for network meta-analysis of diagnostic tests. J Am Stat Assoc. 2019;114(527):949-961.
Rutter CM, Gatsonis CA. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med. 2001;20(19):2865-2884.
Takwoingi Y, Leeflang MM, Deeks JJ. Empirical evidence of the importance of comparative studies of diagnostic test accuracy. Ann Int Med. 2013;158(7):544-554.
Tian JH, Song FJ, Gao Y, Zhang JH. An introduction of the origin and development of the individual patient data network meta analysis. Chin J Drug Eval. 2020;37(03):161-164.
Gao Y, Luo XY, Song FJ, Zhang JH, Tian JH. The descriptive analysis of network meta-analyses of individual participant data. Chin J Drug Eval. 2020;37(03):165-171.
Donegan S, Welton NJ, Tudur Smith C, D'Alessandro U, Dias S. Network meta-analysis including treatment by covariate interactions: consistency can vary across covariate values. Res Synth Meth. 2017;8(4):485-495.
Donegan S, Williamson P, D'Alessandro U, Garner P, Smith CT. Combining individual patient data and aggregate data in mixed treatment comparison meta-analysis: individual patient data may be beneficial if only for a subset of trials. Stat Med. 2013;32(6):914-930.
Veroniki AA, Straus SE, Soobiah C, Elliott MJ, Tricco AC. A scoping review of indirect comparison methods and applications using individual patient data. BMC Med Res Methodol. 2016;16:47.
Hong H, Fu HD, Carlin BP. Power and commensurate priors for synthesising aggregate and individual patient level data in network meta-analysis. J R Stat Soc Ser C-Appl Stat. 2018;67(4):1047-1069.
Donegan S, Williamson P, D'Alessandro U, Smith CT. Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: individual patient-level covariates versus aggregate trial-level covariates. Stat Med. 2012;31(29):3840-3857.
Gao Y, Liu M, Shi S, et al. Prespecification of subgroup analyses and examination of treatment-subgroup interactions in cancer individual participant data meta-analyses are suboptimal. J Clin Epidemiol. 2021;138:156-167.
Saramago P, Sutton AJ, Cooper NJ, Manca A. Mixed treatment comparisons using aggregate and individual participant level data. Stat Med. 2012;31(28):3516-3536.
Jansen JP. Network meta-analysis of individual and aggregate level data. Res Synth Meth. 2012;3(2):177-190.
Saramago P, Chuang LH, Soares MO. Network meta-analysis of (individual patient) time to event data alongside (aggregate) count data. BMC Med Res Methodol. 2014;14:105.
Hong H, Fu H, Price KL, Carlin BP. Incorporation of individual-patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment. Stat Med. 2015;34(20):2794-2819.
Thom HH, Capkun G, Cerulli A, Nixon RM, Howard LS. Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension. BMC Med Res Methodol. 2015;15:34.
Saramago P, Woods B, Weatherly H, et al. Methods for network meta-analysis of continuous outcomes using individual patient data: a case study in acupuncture for chronic pain. BMC Med Res Methodol. 2016;16(1):131.
Freeman SC, Carpenter JR. Bayesian one-step IPD network meta-analysis of time-to-event data using Royston-Parmar models. Res Synth Meth. 2017;8(4):451-464.
Freeman SC, Fisher D, Tierney JF, Carpenter JR. A framework for identifying treatment-covariate interactions in individual participant data network meta-analysis. Res Synth Meth. 2018;9(3):393-407.
Gao Y, Shi S, Li M, et al. Statistical analyses and quality of individual participant data network meta-analyses were suboptimal: a cross-sectional study. BMC Med [Electr Resour]. 2020;18(1):120.
Ouwens MJ, Philips Z, Jansen JP. Network meta-analysis of parametric survival curves. Res Synth Meth. 2010;1(3-4):258-271.
Cope S, Ouwens MJ, Jansen JP, Schmid P. Progression-free survival with fulvestrant 500 mg and alternative endocrine therapies as second-line treatment for advanced breast cancer: a network meta-analysis with parametric survival models. Value Health. 2013;16(2):403-417.
Vickers AD. Survival network meta-analysis: hazard ratios versus reconstructed survival data. Value Health. 2016;19(3):A90-A90.
Petit C, Blanchard P, Pignon JP, Lueza B. Individual patient data network meta-analysis using either restricted mean survival time difference or hazard ratios: is there a difference? A case study on locoregionally advanced nasopharyngeal carcinomas. Syst Rev. 2019;8(1):96.
Welton NJ, Caldwell DM, Adamopoulos E, Vedhara K. Mixed treatment comparison meta-analysis of complex interventions: psychological interventions in coronary heart disease. Am J Epidemiol. 2009;169(9):1158-1165.
Furukawa TA, Karyotaki E, Suganuma A, et al. Dismantling, personalising and optimising internet cognitive-behavioural therapy for depression: a study protocol for individual participant data component network meta-analysis. BMJ Open. 2019;8(11):e026137.
Rücker G, Petropoulou M, Schwarzer G. Network meta-analysis of multicomponent interventions. Biomet J. 2020;62(3):808-821.
Rücker G, Schmitz S, Schwarzer G. Component network meta-analysis compared to a matching method in a disconnected network: a case study. Biomet J. 2021;63(2):447-461.
Del Giovane C, Vacchi L, Mavridis D, Filippini G, Salanti G. Network meta-analysis models to account for variability in treatment definitions: application to dose effects. Stat Med. 2013;32(1):25-39.
Owen RK, Tincello DG, Keith RA. Network meta-analysis: development of a three-level hierarchical modeling approach incorporating dose-related constraints. Value Health. 2015;18(1):116-126.
Mawdsley D, Bennetts M, Dias S, Boucher M, Welton NJ. Model-based network meta-analysis: a framework for evidence synthesis of clinical trial data. CPT: Pharmacometr Syst Pharmacol. 2016;5(8):393-401.
Normand S-L, Spertus J, Horvitz-Lennon M. Network meta-analysis of causal dose-response relationships using individual participant trial data. Biol Psychiatr. 2017;81(10):S34-S35.
Hamza T, Cipriani A, Furukawa TA, Egger M, Orsini N, Salanti G. A Bayesian dose-response meta-analysis model: a simulations study and application. Stat Meth Med Res. 2021;30(5):1358-1372.
Pedder H, Dias S, Bennetts M, Boucher M, Welton NJ. Joining the dots: linking disconnected networks of evidence using dose-response model-based network meta-analysis. Med Decision Mak. 2021;41(2):194-208.
Khalili D, Hadaegh F, Soori H, Steyerberg EW, Bozorgmanesh M, Azizi F. Clinical usefulness of the Framingham cardiovascular risk profile beyond its statistical performance: the tehran lipid and glucose study. Am J Epidemiol. 2012;176(3):177-186.
Alonzo TA. Clinical prediction models: a practical approach to development, validation, and updating: by Ewout W. Steyerberg. Am J Epidemiol. 2009;170(4):528-528.
Debray TP, Moons KG, Ahmed I, Koffijberg H, Riley RD. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med. 2013;32(18):3158-3180.
Debray TP, Riley RD, Rovers MM, Reitsma JB, Moons KG. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use. PLoS Med. 2015;12(10):e1001886.
Ahmed I, Debray TP, Moons KG, Riley RD. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med Res Methodol [Electr Res]. 2014;14:3.
Haile SR, Guerra B, Soriano JB, Puhan MA. Multiple Score Comparison: a network meta-analysis approach to comparison and external validation of prognostic scores. BMC Med Res Methodol [Electr Res]. 2017;17(1):172.
Elliott JH, Turner T, Clavisi O, et al. Living systematic reviews: an emerging opportunity to narrow the evidence-practice gap. PLoS Med. 2014;11(2):6.
Elliott JH, Synnot A, Turner T, et al. Living systematic review: 1. Introduction-the why, what, when, and how. J Clin Epidemiol. 2017;91:23-30.
Thomas J, Noel-Storr A, Marshall F, et al. Living systematic reviews: 2. Combining human and machine effort. J Clin Epidemiol. 2017;91:31-37.
Simmonds M, Salanti G, McKenzie J, Elliott J. Living systematic review N. Living systematic reviews: 3. Statistical methods for updating meta-analyses. J Clin Epidemiol. 2017;91:38-46.
Akl EA, Meerpohl JJ, Elliott J, Kahale LA, Schunemann HJ. Living systematic review N. Living systematic reviews: 4. Living guideline recommendations. J Clin Epidemiol. 2017;91:47-53.
Gao Y, Yang K, Cai Y, et al. Updating systematic reviews can improve the precision of outcomes: a comparative study. J Clin Epidemiol. 2020;125:108-119.
Nikolakopoulou A, Mavridis D, Furukawa TA, et al. Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study. BMJ (Clini Res Ed). 2018;360:10.
Crequit P, Martin-Montoya T, Attiche N, Trinquart L, Vivot A, Ravaud P. Living network meta-analysis was feasible when considering the pace of evidence generation. J Clin Epidemiol. 2019;108:10-16.
Lerner I, Crequit P, Ravaud P, Atal I. Automatic screening using word embeddings achieved high sensitivity and workload reduction for updating living network meta-analyses. J Clin Epidemiol. 2019;108:86-94.
Sbidian E, Chaimani A, Afach S, et al. Systemic pharmacological treatments for chronic plaque psoriasis: a network meta-analysis. Cochr Datab Syst Rev (Online). 2019;1:604.
Siemieniuk RA, Bartoszko JJ, Ge L, et al. Drug treatments for covid-19: living systematic review and network meta-analysis. BMJ (Clini Res Ed). 2020;370:m2980.
Lamontagne F, Agoritsas T, Siemieniuk R, et al. A living WHO guideline on drugs to prevent covid-19. BMJ (Clini Res Ed). 2021;372:n526.
Li L, Tian J, Tian H, et al. Network meta-analyses could be improved by searching more sources and by involving a librarian. J Clin Epidemiol. 2014;67(9):1001-1007.
Wu IXY, Xiao F, Wang H, et al. Trials number, funding support, and intervention type associated with IPDMA data retrieval: a cross-sectional study. J Clin Epidemiol. 2021;130:59-68.
Tsujimoto Y, Fujii T, Onishi A, et al. No consistent evidence of data availability bias existed in recent individual participant data meta-analyses: a meta-epidemiological study. J Clin Epidemiol. 2020;118:107-114.e105.
Nevitt SJ, Marson AG, Davie B, Reynolds S, Williams L, Smith CT. Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic review. BMJ (Clini Res Ed). 2017;357:j1390.
Huang Y, Mao C, Yuan J, et al. Distribution and epidemiological characteristics of published individual patient data meta-analyses. Plos One. 2014;9(6):e100151.
Ahmed I, Sutton AJ, Riley RD. Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey. BMJ (Clini Res Ed). 2012;344:d7762.
Veroniki AA, Straus SE, Rücker G, Tricco AC. Is providing uncertainty intervals in treatment ranking helpful in a network meta-analysis?. J Clin Epidemiol. 2018;100:122-129.
Rücker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol [Electr Res]. 2015;15:58.
Chiocchia V, Nikolakopoulou A, Papakonstantinou T, Egger M, Salanti G. Agreement between ranking metrics in network meta-analysis: an empirical study. BMJ Open. 2020;10(8):e037744.
Chaimani A, Porcher R, Sbidian É, Mavridis D. A Markov chain approach for ranking treatments in network meta-analysis. Stat Med. 2021;40(2):451-464.
Chaimani A, Caldwell DM, Li T, Higgins JPT, Salanti G. Additional considerations are required when preparing a protocol for a systematic review with multiple interventions. J Clin Epidemiol. 2017;83:65-74.
Petropoulou M, Nikolakopoulou A, Veroniki AA, et al. Bibliographic study showed improving statistical methodology of network meta-analyses published between 1999 and 2015. J Clin Epidemiol. 2017;82:20-28.
Higgins JP, 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 Synth Meth. 2012;3(2):98-110.
van Valkenhoef G, Dias S, Ades AE, Welton NJ. Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis. Res Synth Meth. 2016;7(1):80-93.
Puhan MA, Schünemann HJ, Murad MH, et al. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ (Clini Res Ed). 2014;349:g5630.
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.
Nikolakopoulou A, Higgins JPT, Papakonstantinou T, et al. CINeMA: an approach for assessing confidence in the results of a network meta-analysis. PLoS Med. 2020;17(4):e1003082.
Brignardello-Petersen R, Mustafa RA, Siemieniuk RAC, et al. GRADE approach to rate the certainty from a network meta-analysis: addressing incoherence. J Clin Epidemiol. 2019;108:77-85.
Brignardello-Petersen R, Murad MH, Walter SD, et al. GRADE approach to rate the certainty from a network meta-analysis: avoiding spurious judgments of imprecision in sparse networks. J Clin Epidemiol. 2019;105:60-67.
Brignardello-Petersen R, Florez ID, Izcovich A, et al. GRADE approach to drawing conclusions from a network meta-analysis using a minimally contextualised framework. BMJ (Clini Res Ed). 2020;371:m3900.
Brignardello-Petersen R, Izcovich A, Rochwerg B, et al. GRADE approach to drawing conclusions from a network meta-analysis using a partially contextualised framework. BMJ (Clini Res Ed). 2020;371:m3907.
Dias S, Sutton AJ, Ades AE, Welton NJ. Evidence synthesis for decision making 2: a generalised linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Mak. 2013;33(5):607-617.