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Assessing key assumptions of network meta‐analysis: a review of methods
Author(s) -
Donegan Sarah,
Williamson Paula,
D'Alessandro Umberto,
Tudur Smith Catrin
Publication year - 2013
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1085
Subject(s) - meta analysis , computer science , consistency (knowledge bases) , homogeneity (statistics) , econometrics , covariate , statistics , data mining , machine learning , mathematics , medicine , artificial intelligence
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. Copyright © 2013 John Wiley & Sons, Ltd.