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      Network meta-analysis: application and practice using Stata

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          Abstract

          This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions—similarity, transitivity, and consistency—should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system.

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          Most cited references25

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          The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials.

          When little or no data directly comparing two treatments are available, investigators often rely on indirect comparisons from studies testing the treatments against a control or placebo. One approach to indirect comparison is to pool findings from the active treatment arms of the original controlled trials. This approach offers no advantage over a comparison of observational study data and is prone to bias. We present an alternative model that evaluates the differences between treatment and placebo in two sets of clinical trials, and preserves the randomization of the originally assigned patient groups. We apply the method to data on sulphamethoxazole-trimethoprim or dapsone/pyrimethamine as prophylaxis against Pneumocystis carinii in HIV infected patients. The indirect comparison showed substantial increased benefit from the former (odds ratio 0.37, 95% CI 0.21 to 0.65), while direct comparisons from randomized trials suggests a much smaller difference (risk ratio 0.64, 95% CI 0.45 to 0.90; p-value for difference of effect = 0.11). Direct comparisons of treatments should be sought. When direct comparisons are unavailable, indirect comparison meta-analysis should evaluate the magnitude of treatment effects across studies, recognizing the limited strength of inference.
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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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              Evaluation of inconsistency in networks of interventions.

              The assumption of consistency, defined as agreement between direct and indirect sources of evidence, underlies the increasingly popular method of network meta-analysis. No evidence exists so far regarding the extent of inconsistency in full networks of interventions or the factors that control its statistical detection. In this paper we assess the prevalence of inconsistency from data of 40 published networks of interventions involving 303 loops of evidence. Inconsistency is evaluated in each loop by contrasting direct and indirect estimates and by employing an omnibus test of consistency for the entire network. We explore whether different effect measures for dichotomous outcomes are associated with differences in inconsistency, and evaluate whether different ways to estimate heterogeneity affect the magnitude and detection of inconsistency. Inconsistency was detected in from 2% to 9% of the tested loops, depending on the effect measure and heterogeneity estimation method. Loops that included comparisons informed by a single study were more likely to show inconsistency. About one-eighth of the networks were found to be inconsistent. The proportions of inconsistent loops do not materially change when different effect measures are used. Important heterogeneity or the overestimation of heterogeneity was associated with a small decrease in the prevalence of statistical inconsistency. The study suggests that changing the effect measure might improve statistical consistency, and that an analysis of sensitivity to the assumptions and an estimator of heterogeneity might be needed before reaching a conclusion about the absence of statistical inconsistency, particularly in networks with few studies.
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                Author and article information

                Journal
                Epidemiol Health
                Epidemiol Health
                EPIH
                Epidemiology and Health
                Korean Society of Epidemiology
                2092-7193
                2017
                27 October 2017
                : 39
                : e2017047
                Affiliations
                [1 ]Institute for Clinical Molecular Biology Research, Soonchunhyang University Hospital, Seoul, Korea
                [2 ]Department of Orthopaedic Surgery, Seoul Paik Hospital, ,Inje University College of Medicine, Seoul, Korea
                [3 ]Department of Education, Jeonju University, Jeonju, Korea
                [4 ]Department of Preventive Medicine, Jeju National University School of Medicine, Jeju, Korea
                Author notes
                Correspondence: Jong-Myon Bae  Department of Preventive Medicine, Jeju National University School of Medicine, 102 Jejudaehak-ro, Jeju 63243, Korea  E-mail: jmbae@ 123456cheju.ac.kr
                Author information
                http://orcid.org/0000-0003-4143-7383
                http://orcid.org/0000-0001-8518-6331
                http://orcid.org/0000-0001-7535-3511
                http://orcid.org/0000-0003-3080-7852
                Article
                epih-39-e2017047
                10.4178/epih.e2017047
                5733388
                29092392
                a4f7d479-6b2c-43cb-8339-0d161d02d516
                ©2017, Korean Society of Epidemiology

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 6 September 2017
                : 27 October 2017
                Categories
                Methods

                Public health
                network meta-analysis,treatment outcome,mixed treatment comparison,biostatistic
                Public health
                network meta-analysis, treatment outcome, mixed treatment comparison, biostatistic

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