Imperfectness of today’s methods for conducting meta-analyses in systematic reviews and health technology assessments (HTAs) that are more and more frequently used as a basis for reimbursement decisions, could result in even denial of public financing for a drug or other treatment that is actually worth financing. The credibility of results of meta-analyses could be increased by applying methods appropriate for specific data available. Often it is the Bayesian statistics that would provide best estimates for the results. However due to complicated application and difficulties with interpretation of a priori knowledge it is rarely used. There is a similar situation in regard to indirect comparisons, where methods relatively widely used nowadays (e.g. Bücher method) are estimated to lack credibility by some experts or even their authors. Our main aim is to implement innovative Bayesian approach to analyses used in reimbursement decisions to improve their potential for rationality. Our detailed aims are the following:- to increase competency of health care analysts (both academics and pharmaceutical industry); - to increase competency of reimbursement decision-makers, doctors and those public administration staff who is involved in preparing evidence for reimbursement decisions;- to increase cross-border mobility through enhanced labour market attractiveness of people who acquire the unique competency; Objectives:- to create teaching materials (e-learning tools) concerning methods for conducting meta-analyses during drug and other health technology assessments with special focus on Bayesian methods;- to implement a universal tool to process meta-analyses in various ways depending on type of input data;- to organize dissemination activities (workshops and e-learning courses) focusing on addressing unmet statistical literacy needs related to Bayesian statistics in analyses of health technologies. The eBayesMet consortium consists of partners who are well experienced in EU and international projects: CASPolska (Poland), University of Birmingham (UK), AMC Amsterdam (Netherlands) and EMMERCE EEIG (Sweden). The tangible outcomes will be a project website with eBayesMet e-learning platform, teaching materials, a universal tool to appropriately process meta-analyses and a discussion forum for project partners, advisors and interested members of the public. All this will be internally and independently externally evaluated and optimized during the project life cycle. We expect the intangible outcomes in a form of increased interest and competencies in use of Bayesian methods and mobility of analytic workforce. The envisaged impact will be increased use of Bayesian methods in health care analyses, hence improved potential for more rational decisions on public financing of health technologies.