Title Bayesian Methods to Enhance Reimbursement Decisions - global e-learning tool with advanced computing software
Project Number 2009-1-PL1-LEO05-05083
Project Type Transfer of Innovation
There is lack of Bayesian statistics e-learning courses which could be helpful in everyday practice of systematic reviewers and HTA specialists. Moreover, those rare courses from other areas generally present knowledge in a non-user friendly format. By the teaching programme and dissemination activities we hope our project fills the knowledge gap in this field.
eBayesMet project will be appropriate in many different ways: it can be taught in hospital or in a classroom or in a self-learning format through the Internet. In the long term, this project will benefit the general public with more precise results of meta-analyses and more transparent funding decisions in health care system, better-informed patients and a generally better informed society.
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;
- 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.
The project aims were to improve interests and level of comprehensive knowledge of meta-analyses and indirect comparisons with further consequences. This was achieved by realizing the following particular objectives:
- Arranging scientific inter-discipline discussion. There is a special website created with discussion panel, where project ideas, problems and solutions are available. Moreover, discussion could take place also on conferences and meetings.
- Creating advanced e-learning platform for training and developing skills. This includes materials (in pdf and HTML format) consisting of lectures, solved examples of applying methods, exercises to practise and test.
- Optimizing software based on OpenBUGS (making it more friendly and adding new utilities). It is advanced enough to be a basic tool to conduct meta-analyses needed in pharmacoeconomic and clinical analyses (HTAs). eBayesMet project thanks to the innovative way of disseminating knowledge and skills (e-learning platform with use of different multimedia methods - sound, text and visualizations) improves access to continuing vocational education by developing distance learning materials, increasing training capacity for those groups of people who traditionally have limited access to formal training, with subsequent increase of attractiveness of our beneficiaries on the European labour market.
Utilization and distribution of results
Open and distance learning
Access for disadvantaged
Professional, Scientific and Technical Activities
Public Administration and Defence; Compulsory Social Security
Human Health and Social Work Activities
Information and Communication
program or curricula
open and distance learning
material for open learning
One of the project products is a user-friendly universal software for conducting meta-analyses and indirect comparisons. As one of the main methods used for meta-analyses is the Bayesian one. We planned to create a user-friendly environment and a library containing procedures used for processing meta-analyses and indirect comparisons. In short term the results of the project will
Facilitate an access for all interested beneficiaries to specific
statistical knowledge gathered in two curricula placed on user
friendly e-learning platform.
In addition, increase in number of people interested in extension of their
knowledge in the field of statistics used in medicine, especially
Bayesian statistics is expected.
In the long term the results of the project will lead to:
an increase in the knowledge and skills in the field of medical
statistics and Bayesian statistics,
an increase in credibility of conducted meta-analyses,
an increase in ability of decision making process with making use of
statistical analyses results,
an Increase in the number of analysts and doctors, who will take
e-learning courses regularly,
an increase in the number of participants, who will fulfill the
an increase in the number of experts who will participate in
discussion on project website forum.