title: Assessing key assumptions of network meta-analysis: a review of methods.
abstract: BACKGROUND
  Homogeneity and consistency assumptions underlie network meta-analysis (NMA). Methods exist to assess the assumptions but they are rarely and poorly applied. We review and illustrate methods to assess homogeneity and consistency.
  
  
  METHODS
  Eligible articles focussed on indirect comparison or NMA methodology. Articles were sought by hand-searching and scanning references (March 2013). Assumption assessment methods described in the articles were reviewed, and applied to compare anti-malarial drugs.
  
  
  RESULTS
  116 articles were included. Methods to assess homogeneity were: comparing characteristics across trials; comparing trial-specific treatment effects; using hypothesis tests or statistical measures; applying fixed-effect and random-effects pair-wise meta-analysis; and investigating treatment effect-modifiers. Methods to assess consistency were: comparing characteristics; investigating treatment effect-modifiers; comparing outcome measurements in the referent group; node-splitting; inconsistency modelling; hypothesis tests; back transformation; multidimensional scaling; a two-stage approach; and a graph-theoretical method. For the malaria example, heterogeneity existed for some comparisons that was unexplained by investigating treatment effect-modifiers. Inconsistency was detected using node-splitting and inconsistency modelling. It was unclear whether the covariates explained the inconsistency.
  
  
  CONCLUSIONS
  Presently, we advocate applying existing assessment methods collectively to gain the best understanding possible regarding whether assumptions are reasonable. In our example, consistency was questionable; therefore the NMA results may be unreliable.
authors.affiliation: Department of Biostatistics, Faculty of Health & Life Sciences, University of Liverpool, Shelley's Cottage, Brownlow Street, Liverpool, L69 3GS, UK.
authors.forename: Sarah
authors.initials: S
authors.lastname: Donegan
authors.affiliation: Department of Biostatistics, Faculty of Health & Life Sciences, University of Liverpool, Shelley's Cottage, Brownlow Street, Liverpool, L69 3GS, UK.
authors.forename: Paula
authors.initials: P
authors.lastname: Williamson
authors.affiliation: Department of Parasitology, Prince Leopold Institute of Tropical Medicine, National estraat 155, B-2000, Antwerp, Belgium.
authors.forename: Umberto
authors.initials: U
authors.lastname: D'Alessandro
authors.affiliation: Department of Biostatistics, Faculty of Health & Life Sciences, University of Liverpool, Shelley's Cottage, Brownlow Street, Liverpool, L69 3GS, UK.
authors.forename: Catrin
authors.initials: C
authors.lastname: Tudur Smith
mesh_terms: D000962:Antimalarials / Q000008:administration & dosage; D037621:Artemisinins / Q000008:administration & dosage; D056808:Biostatistics; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D006801:Humans; D008288:Malaria / Q000188:drug therapy; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D016896:Treatment Outcome
chemical_list: D000962:Antimalarials; D037621:Artemisinins; C031327:artemisinin
keywords: consistency, transitivity; mixed treatment comparison; multiple treatments meta-analysis; network meta-analysis