66
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers

      discussion

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Related collections

          Most cited references7

          • Record: found
          • Abstract: found
          • Article: not found

          Why sources of heterogeneity in meta-analysis should be investigated.

          Although meta-analysis is now well established as a method of reviewing evidence, an uncritical use of the technique can be very misleading. One common problem is the failure to investigate appropriately the sources of heterogeneity, in particular the clinical differences between the studies included. This paper distinguishes between the concepts of clinical and statistical heterogeneity and exemplifies the importance of investigating heterogeneity by using published meta-analyses of epidemiological studies of serum cholesterol concentration and clinical trials of its reduction. Although not without some dangers of speculative conclusions, prompted by overzealous inspection of the data to hand, a sensible investigation of sources of heterogeneity should increase both the scientific and the clinical relevance of the results of meta-analyses.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Use of indirect and mixed treatment comparisons for technology assessment.

            Indirect and mixed treatment comparison (MTC) approaches to synthesis are logical extensions of more established meta-analysis methods. They have great potential for estimating the comparative effectiveness of multiple treatments using an evidence base of trials that individually do not compare all treatment options. Connected networks of evidence can be synthesized simultaneously to provide estimates of the comparative effectiveness of all included treatments and a ranking of their effectiveness with associated probability statements. The potential of the use of indirect and MTC methods in technology assessment is considerable, and would allow for a more consistent assessment than simpler alternative approaches. Although such models can be viewed as a logical and coherent extension of standard pair-wise meta-analysis, their increased complexity raises some unique issues with far-reaching implications concerning how we use data in technology assessment, while simultaneously raising searching questions about standard pair-wise meta-analysis. This article reviews pair-wise meta-analysis and indirect and MTC approaches to synthesis, clearly outlining the assumptions involved in each approach. It also raises the issues that the National Institute for Health and Clinical Excellence (NICE) needed to consider in updating their 2004 Guide to the Methods of Technology Appraisal, if such methods are to be used in their technology appraisals.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: Application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation.

              Mixed treatment comparison models extend meta-analysis methods to enable comparisons to be made between all relevant comparators in the clinical area of interest. In such modelling it is imperative that potential sources of variability are explored to explain both heterogeneity (variation in treatment effects between trials within pairwise contrasts) and inconsistency (variation in treatment effects between pairwise contrasts) to ensure the validity of the analysis.The objective of this paper is to extend the mixed treatment comparison framework to allow for the incorporation of study-level covariates in an attempt to explain between-study heterogeneity and reduce inconsistency. Three possible model specifications assuming different assumptions are described and applied to a 17-treatment network for stroke prevention treatments in individuals with non-rheumatic atrial fibrillation.The paper demonstrates the feasibility of incorporating covariates within a mixed treatment comparison framework and using model fit statistics to choose between alternative model specifications. Although such an approach may adjust for inconsistencies in networks, as for standard meta-regression, the analysis will suffer from low power if the number of trials is small compared with the number of treatment comparators. Copyright (c) 2009 John Wiley & Sons, Ltd.
                Bookmark

                Author and article information

                Contributors
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central
                1741-7015
                2013
                4 July 2013
                : 11
                : 159
                Affiliations
                [1 ]Mapi, 180 Canal Street, Suite 503, Boston, MA 02114, USA
                [2 ]Tufts University School of Medicine, 145 Harrison Avenue, Boston, MA 02111, USA
                [3 ]LSE Health & Social Care, London School of Economics & Political Science, Cowdray House, 20 Houghton Street, London WC2A 2AE, UK
                Article
                1741-7015-11-159
                10.1186/1741-7015-11-159
                3707819
                23826681
                40b677bf-8e67-4a40-b818-c42816fac489
                Copyright © 2013 Jansen and Naci; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 February 2013
                : 30 May 2013
                Categories
                Debate

                Medicine
                bias,comparative effectiveness,confounding,effect modification,indirect comparison,meta-analysis,mixed treatment comparison,network meta-analysis,randomized controlled trial,systematic review

                Comments

                Comment on this article