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Checking consistency in mixed treatment comparison meta‐analysis
Author(s) -
Dias S.,
Welton N. J.,
Caldwell D. M.,
Ades A. E.
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3767
Subject(s) - pooling , computer science , pairwise comparison , consistency (knowledge bases) , context (archaeology) , bayesian probability , node (physics) , data mining , econometrics , artificial intelligence , mathematics , paleontology , structural engineering , engineering , biology
Pooling of direct and indirect evidence from randomized trials, known as mixed treatment comparisons (MTC), is becoming increasingly common in the clinical literature. MTC allows coherent judgements on which of the several treatments is the most effective and produces estimates of the relative effects of each treatment compared with every other treatment in a network. We introduce two methods for checking consistency of direct and indirect evidence. The first method (back‐calculation) infers the contribution of indirect evidence from the direct evidence and the output of an MTC analysis and is useful when the only available data consist of pooled summaries of the pairwise contrasts. The second more general, but computationally intensive, method is based on ‘node‐splitting’ which separates evidence on a particular comparison (node) into ‘direct’ and ‘indirect’ and can be applied to networks where trial‐level data are available. Methods are illustrated with examples from the literature. We take a hierarchical Bayesian approach to MTC implemented using WinBUGS and R. We show that both methods are useful in identifying potential inconsistencies in different types of network and that they illustrate how the direct and indirect evidence combine to produce the posterior MTC estimates of relative treatment effects. This allows users to understand how MTC synthesis is pooling the data, and what is ‘driving’ the final estimates. We end with some considerations on the modelling assumptions being made, the problems with the extension of the back‐calculation method to trial‐level data and discuss our methods in the context of the existing literature. Copyright © 2010 John Wiley & Sons, Ltd.