Health care practitioners face daily questions and make choices regarding the effectiveness and quality of several health technologies (e.g. alternative interventions). On this regard they usually consider meta-analysis; the statistical synthesis of results from relevant experiments. The main drawback of the current state of the art is that meta-analysis focuses on comparing only two alternatives. However, clinicians and scientists need to know the relative ranking of a set of alternative options and not only whether option A is better than B. There is an urgent need to establish and disseminate a robust framework for selecting among many treatment options, possibly after taking into account environmental and genetic interactions. The goal of the proposed project is to provide this by establishing and disseminating a revolutionary evidence synthesis toolkit. Its main methodological vehicle is a flexible statistical framework using Bayesian techniques for multiple-treatments meta-analysis. This will enable the relative ranking of all alternative health care options, will allow comprehensive use of all available data, will improve precision and confidence in the conclusions and will answer methodological questions related to bias. Once established in clinical epidemiology, the tool will be extended to genetic epidemiology to account for multiple genetic markers, environmental factors and effects of treatments. Based on ongoing collaborations with teams undertaking applied health care research I plan to evaluate the new tool empirically in real-life health care problems such as ranking the pharmacological treatments for osteoarthritis, indicating the best treatments for multiple sclerosis and ranking the vaccines for influenza.