Research Synthesis Methods special issue on network meta‐analysis: introduction from the editors

Sixteen years ago, a meta-analysis of studies comparing beta-blockers, sclerotherapy, and conventional care for preventing cirrhosis was the first to articulate that relative effects of different treatments can be jointly estimated in a single meta-analysis model to improve power (Higgins and Whitehead, 1996). This landmark paper introduced the basis for the methodology which, now extended and refined, is increasingly known as network meta-analysis. This term, interchangeably used with ‘multiple-treatments meta-analysis’ and ‘mixed-treatment comparison’ refers to the joint statistical analysis of a set of meta-analyses comparing different treatments. As in this early paper, Bayesian implementations of network meta-analysis have predominated and to date more than 100 applications and methods papers have been published attracting considerable attention and citations. It is increasingly recognized that network meta-analysis is the only technique that can provide a ranking of the alternative treatment options for the same condition. Consequently, national and international bodies such as the National Institute for Health and Clinical Excellence in the UK, the Agency for Healthcare Quality and Research in the US and The Cochrane Collaboration worldwide embrace the emerging framework to answer complex policyrelevant questions. Nevertheless, great skepticism accompanies this enthusiasm, and the clinical and statistical literature is filled with concerns regarding the lack of transparency in assumptions and methods of network meta-analysis. The first reviews of the methodology (e.g., Salanti et al., 2008) and guidelines (e.g., Jansen et al., 2011) are beginning to appear, and the production of new methods and the presentation of applications in several clinical fields are thriving. Amidst the rapidly evolving methods and increasing applications, this special issue of Research Synthesis Methods offers perspective on the current state of the art as well as important new developments in original and review articles from leaders in the field. The special issue starts with a review by Asmaa Abdelhamid and colleagues of the perceptions of Cochrane reviewers regarding indirect comparison and related methods. The next article by the issue editor, Georgia Salanti, is a tutorial providing a very detailed presentation of the assumptions underlying the methods and their major advantages and limitations. Julian Higgins and colleagues provide a novel model for testing and accounting for violations in consistency, the key assumption underlying network meta-analysis. Ian White and colleagues show how network meta-analysis can be fit in frequentist setting using STATA software (StataCorp, TX, USA), a development sure to have a major impact on application of the method. Two papers address more technical questions. Deborah Caldwell and colleagues address the association between the choice of effect size for dichotomous data and the consistency of evidence, whereas Angelo Franchini and colleagues study the importance of accounting for the correlations induced by multi-arm trials. The special issue closes with two important extensions of the network meta-analysis model. The article by Anna Chaimani and colleague shows how small study effects (the phenomenon that small studies appear to exaggerate relative treatment effects) can be accounted for in a dataset comparing many competing interventions. Finally, Jeroen Jansen extends the network meta-analysis model to synthesize information from individual patient data and aggregated data.