Checking consistency in mixed treatment comparison meta-analysis

• Published in: Statistics in medicine 2010-03, Vol.29 (7-8), p.932-944
• Main Authors: Dias, S., Welton, N. J., Caldwell, D. M., Ades, A. E.
• Format: Article
• Language: English
• Subjects: Angioplasty - statistics & numerical data; Bayes Theorem; Bayesian; Bayesian analysis; Biostatistics; Comparative analysis; Effects; Estimates; Fibrinolytic Agents - therapeutic use; Humans; Markov Chains; MCMC; Medical treatment; Meta-analysis; Meta-Analysis as Topic; mixed treatment comparisons; Monte Carlo Method; Myocardial Infarction - drug therapy; Myocardial Infarction - surgery; Randomized Controlled Trials as Topic - statistics & numerical data; Review Literature as Topic; Smoking Cessation - statistics & numerical data; treatment network
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.3767

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.