Mixed treatment comparison (MTC) (also called network meta-analysis) is an extension of traditional meta-analysis to allow the simultaneous pooling of data from clinical trials comparing more than two treatment options. Typically, MTCs are performed using general-purpose Markov chain Monte Carlo software such as WinBUGS, requiring a model and data to be specified using a specific syntax. It would be preferable if, for the most common cases, both could be derived from a well-structured data file that can be easily checked for errors. Automation is particularly valuable for simulation studies in which the large number of MTCs that have to be estimated may preclude manual model specification and analysis. Moreover, automated model generation raises issues that provide additional insight into the nature of MTC. We present a method for the automated generation of Bayesian homogeneous variance random effects consistency models, including the choice of basic parameters and trial baselines, priors, and starting values for the Markov chain(s). We validate our method against the results of five published MTCs. The method is implemented in freely available open source software. This means that performing an MTC no longer requires manually writing a statistical model. This reduces time and effort, and facilitates error checking of the dataset. Copyright © 2012 John Wiley & Sons, Ltd.

Clinical trials are the main source of information for the efficacy and safety evaluation of medical treatments. Although they are of pivotal importance in evidence-based medicine, there is a lack of usable information systems providing data-analysis and decision support capabilities for aggregate clinical trial results. This is partly caused by unavailability (i) of trial data in a structured format suitable for re-analysis, and (ii) of a complete data model for aggregate level results. In this paper, we develop a unifying data model that enables the development of evidence-based decision support in the absence of a complete data model. We describe the supported decision processes and show how these are implemented in the open source ADDIS software. ADDIS enables semi-automated construction of meta-analyses, network meta-analyses and benefit-risk decision models, and provides visualization of all results.

The designed method enables quantitative BR analysis of alternative treatments using all available evidence from a network of clinical trials. The preference-free analysis can be useful in presenting the results of an MTC considering multiple outcomes.

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