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      Network meta-analysis incorporating randomized controlled trials and non-randomized comparative cohort studies for assessing the safety and effectiveness of medical treatments: challenges and opportunities

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          Abstract

          Network meta-analysis is increasingly used to allow comparison of multiple treatment alternatives simultaneously, some of which may not have been compared directly in primary research studies. The majority of network meta-analyses published to date have incorporated data from randomized controlled trials (RCTs) only; however, inclusion of non-randomized studies may sometimes be considered. Non-randomized studies can complement RCTs or address some of their limitations, such as short follow-up time, small sample size, highly selected population, high cost, and ethical restrictions. In this paper, we discuss the challenges and opportunities of incorporating both RCTs and non-randomized comparative cohort studies into network meta-analysis for assessing the safety and effectiveness of medical treatments. Non-randomized studies with inadequate control of biases such as confounding may threaten the validity of the entire network meta-analysis. Therefore, identification and inclusion of non-randomized studies must balance their strengths with their limitations. Inclusion of both RCTs and non-randomized studies in network meta-analysis will likely increase in the future due to the growing need to assess multiple treatments simultaneously, the availability of higher quality non-randomized data and more valid methods, and the increased use of progressive licensing and product listing agreements requiring collection of data over the life cycle of medical products. Inappropriate inclusion of non-randomized studies could perpetuate the biases that are unknown, unmeasured, or uncontrolled. However, thoughtful integration of randomized and non-randomized studies may offer opportunities to provide more timely, comprehensive, and generalizable evidence about the comparative safety and effectiveness of medical treatments.

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          Randomized, Controlled Trials, Observational Studies, and the Hierarchy of Research Designs

          New England Journal of Medicine, 342(25), 1887-1892
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            Evidence Synthesis for Decision Making 4

            Inconsistency can be thought of as a conflict between “direct” evidence on a comparison between treatments B and C and “indirect” evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed.
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              Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers

              Background In the last decade, network meta-analysis of randomized controlled trials has been introduced as an extension of pairwise meta-analysis. The advantage of network meta-analysis over standard pairwise meta-analysis is that it facilitates indirect comparisons of multiple interventions that have not been studied in a head-to-head fashion. Although assumptions underlying pairwise meta-analyses are well understood, those concerning network meta-analyses are perceived to be more complex and prone to misinterpretation. Discussion In this paper, we aim to provide a basic explanation when network meta-analysis is as valid as pairwise meta-analysis. We focus on the primary role of effect modifiers, which are study and patient characteristics associated with treatment effects. Because network meta-analysis includes different trials comparing different interventions, the distribution of effect modifiers cannot only vary across studies for a particular comparison (as with standard pairwise meta-analysis, causing heterogeneity), but also between comparisons (causing inconsistency). If there is an imbalance in the distribution of effect modifiers between different types of direct comparisons, the related indirect comparisons will be biased. If it can be assumed that this is not the case, network meta-analysis is as valid as pairwise meta-analysis. Summary The validity of network meta-analysis is based on the underlying assumption that there is no imbalance in the distribution of effect modifiers across the different types of direct treatment comparisons, regardless of the structure of the evidence network.
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                Author and article information

                Contributors
                (613) 852-2374 , ccame056@uottawa.ca
                bruce.fireman@kp.org
                bhutton@ohri.ca
                Tammyc@cadth.ca
                dcoyle@uottawa.ca
                gawells@ottawaheart.ca
                colin.dormuth@ti.ubc.ca
                robert.platt@mcgill.ca
                darren_toh@harvardpilgrim.org
                Journal
                Syst Rev
                Syst Rev
                Systematic Reviews
                BioMed Central (London )
                2046-4053
                5 November 2015
                5 November 2015
                2015
                : 4
                : 147
                Affiliations
                [ ]School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, 451 Smyth Road, Suite RGN 3105, Ottawa, ON K1H 8 M5 Canada
                [ ]Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215 USA
                [ ]Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612 USA
                [ ]Ottawa Hospital Research Institute, Center for Practice Changing Research Building, Ottawa Hospital—General Campus, PO Box 201B, Ottawa, ON K1H 8 L6 Canada
                [ ]Canadian Agency for Drugs and Technologies in Health, 865 Carling Ave., Suite 600, Ottawa, ON K1S 5S8 Canada
                [ ]Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC V6T 1Z3 Canada
                [ ]Department of Epidemiology and Biostatistics, McGill University, 4060 Ste Catherine W #300, Montréal, Québec H3Z 2Z3 Canada
                [ ]Evidence Synthesis Group, Cornerstone Research Group Inc., 3228 South Service Road, Burlington, ON L7N 3H8 Canada
                Article
                133
                10.1186/s13643-015-0133-0
                4634799
                26537988
                519d61f5-8cab-478f-8ca3-a92ea1b26890
                © Cameron et al. 2015

                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
                : 21 May 2015
                : 13 October 2015
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                © The Author(s) 2015

                Public health
                network meta-analysis,randomized controlled trials,observational studies,pharmacoepidemiology,comparative effectiveness research,distributed research networks

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