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      Approaches to interpreting and choosing the best treatments in network meta-analyses

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          Abstract

          When randomized trials have addressed multiple interventions for the same health problem, network meta-analyses (NMAs) permit researchers to statistically pool data from individual studies including evidence from both direct and indirect comparisons. Grasping the significance of the results of NMAs may be very challenging. Authors may present the findings from such analyses in several numerical and graphical ways. In this paper, we discuss ranking strategies and visual depictions of rank, including the surface under the cumulative ranking (SUCRA) curve method. We present ranking approaches’ merits and limitations and provide an example of how to apply the results of a NMA to clinical practice.

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          Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial.

          Georgia Salanti, A E Ades, John Ioannidis (2011)
          To present some simple graphical and quantitative ways to assist interpretation and improve presentation of results from multiple-treatment meta-analysis (MTM). We reanalyze a published network of trials comparing various antiplatelet interventions regarding the incidence of serious vascular events using Bayesian approaches for random effects MTM, and we explore the advantages and drawbacks of various traditional and new forms of quantitative displays and graphical presentations of results. We present the results under various forms, conventionally based on the mean of the distribution of the effect sizes; based on predictions; based on ranking probabilities; and finally, based on probabilities to be within an acceptable range from a reference. We show how to obtain and present results on ranking of all treatments and how to appraise the overall ranks. Bayesian methodology offers a multitude of ways to present results from MTM models, as it enables a natural and easy estimation of all measures based on probabilities, ranks, or predictions. Copyright © 2011 Elsevier Inc. All rights reserved.
          Article 0 comments Cited 910 times – based on 0 reviews     Review now
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            Graphical Tools for Network Meta-Analysis in STATA

            Anna Chaimani, Julian P. T. Higgins, Dimitris Mavridis … (2014)
            Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.
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              A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis

              M. Puhan, H. J. Schunemann, M Murad … (2014)
              Article 0 comments Cited 315 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                L. Mbuagbaw: mbuagblc@mcmaster.ca
                B. Rochwerg: bram.rochwerg@medportal.ca
                R. Jaeschke: jaeschke@mcmaster.ca
                D. Heels-Andsell: ansdell@mcmaster.ca
                W. Alhazzani: alhazzaw@mcmaster.ca
                L. Thabane: thabanl@mcmaster.ca
                Gordon H. Guyatt: +905-525-9140 , guyatt@mcmaster.ca
                Journal
                Journal ID (nlm-ta): Syst Rev
                Journal ID (iso-abbrev): Syst Rev
                Title: Systematic Reviews
                Publisher: BioMed Central (London )
                ISSN (Electronic): 2046-4053
                Publication date (Electronic): 12 April 2017
                Publication date PMC-release: 12 April 2017
                Publication date Collection: 2017
                Volume: 6
                Electronic Location Identifier: 79
                Affiliations
                [1 ]GRID grid.25073.33, Department of Health Research Methods, Evidence and Impact, , McMaster University, ; Hamilton, ON Canada
                [2 ]GRID grid.25073.33, Biostatistics Unit, , Father Sean O’Sullivan Research Centre, St Joseph’s Healthcare, ; Hamilton, ON Canada
                [3 ]Centre for Development of Best Practices in Health (CDBPH), Yaoundé Central Hospital, Yaoundé, Cameroon
                [4 ]GRID grid.25073.33, Department of Medicine, , McMaster University, ; Hamilton, ON Canada
                [5 ]GRID grid.25073.33, Department of Paediatrics, , McMaster University, ; Hamilton, ON Canada
                [6 ]GRID grid.416721.7, , Centre for Evaluation of Medicine, St Joseph’s Healthcare—Hamilton, ; Hamilton, ON Canada
                [7 ]GRID grid.413615.4, , Population Health Research Institute, Hamilton Health Sciences, ; Hamilton, ON Canada
                [8 ]GRID grid.25073.33, CLARITY Research Group, Department of Clinical Epidemiology & Biostatistics, , McMaster University, ; Room 2C12, 1200 Main Street West, Hamilton, ON L8N 3Z5 Canada
                [9 ]GRID grid.25073.33, Department of Anaesthesia, , McMaster University, ; Hamilton, ON Canada
                Article
                Publisher ID: 473
                DOI: 10.1186/s13643-017-0473-z
                PMC ID: 5389085
                PubMed ID: 28403893
                SO-VID: af305ace-5994-4866-9ede-1efeeebbb595
                Copyright © © The Author(s). 2017
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 12 February 2017
                Date accepted : 3 April 2017
                Categories
                Subject: Commentary
                Custom metadata
                issue-copyright-statement © The Author(s) 2017

                ScienceOpen disciplines: Public health
                Keywords: ranking,sucra,network meta-analysis,advantages,limitations
                Data availability:
                ScienceOpen disciplines: Public health
                Keywords: ranking, sucra, network meta-analysis, advantages, limitations

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