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This is the Information Age. We can expect that for a particular research question that is empirically testable, we should have a collection of evidence which indicates the best way to proceed. Unfortunately, this is not the case in several areas of empirical research and decision making. Instead, when researchers and policy makers ask a specific question, such as “What is the effectiveness of a new treatment?”, the structure of the evidence available to answer this question may be complex and fragmented (e.g. published experiments may have different grades of quality, observational data, subjective judgments, etc.). Meta-analysis is a branch of statistical techniques that helps researchers to combine evidence from a multiplicity of indirect sources. A main hurdle in meta-analysis is that we not only combine results from a diversity of sources but we also combine their multiplicity of biases. Therefore, commonly applied meta-analysis methods, e.g. random-effects models, could be misleading. In this chapter we present a new method for meta-analysis that we have called: the “Hierarchical Meta-Regression” (HMR). The HMR is an integrated approach for evidence synthesis when a multiplicity of bias, coming from indirect and disparate evidence, has to be incorporated in a meta-analysis.

Two Examples of Bayesian Evidence Synthesis with the Hierarchical Meta-Regression Approach