1. eBayesMet - on the main activities and results regarding to e-learning Kraków, 29.10.2011 Mateusz Nikodem, CASPolska Association

2. eBayesMet (11.2009 – 10.2011) Partners: CASPolska Association - leader Queen Mary University of London Academic Medical Center, Amsterdam EMMERCE EEIG

3. The main idea of the Project: Establishing the variety of statistical methods for meta-analysis (both direct and indirect comparison) Assessing the precision and credibility of particular methods dependently on characteristics of input data Creating a guide facilitating the choise of an optimal method Creating educational materials which can be helpful in cunducting meta-analyses Disseminating results of the project, e.g. presenting main conclusions on relevant conferences

4. Target groups : Main target groups: reviewers with background, i.e.:  Medical data analysts,  Pharmacoeconomics specialists,  University researchers,  Clinicians. Problems to handle with: What does it exactly mean: „with background” ? background in HTA reviews? background in statistics? background in Bayesian models? How to assure that the user, in fact has proper background?

5. Target groups : Problems to handle with: What does it exactly mean: „with background” ? background in HTA reviews? background in statistics? background in Bayesian models? How to assure that a user, in fact has a proper background? should he/she pass some intro test? what if he/she fail? maybe it is enough to declare that he/she is familiar with some knowledge?

6. Target groups : Problems to handle with: What is the characterization of the target group? What are their needs? What are their capabilities? time? language? terminology? software/equipment?

7. Needs Summarize the state of knowledge on statistical methods for meta- analyses, especially differences between different Bayesian modes Clear classification of statistical methods: which of them are applicable for which kind of input data. Some help in implementation of Bayesian models. Main needs:

8. Aims To make the reviewers aware of multiplicity of statistical methods for meta-analyses and summarize the status of knowledge in this area To show how to choose the most adequate method to particular input data - and how to avoid substantial mistakes To facilitate conducing meta-analyses using several methods including advanced Bayesian models Main aims:

9. The substantial content Methods for direct comparison You can learn how to handle with dataof different characteristics including problematic issues like small sample size and rare events Including observational studies You can learn how to include knowledge from observational studies to meta-analysis of randomized trials and how to set the level of conviction of the extra data Methods combining direct and indirect comparisons You can develop your skills in conducting indirect and mixed comparison (in basic structures of data) Lessons Introduction

10. What about Bayesian methods? The Bayesian models are not presented of itself in the main path of the course, but the lessons are focused on their applications. They are especially used in the case of including observational studies and combining direct and indirect evidence. Before the application of Bayesian methods was decribed, all adequate models was implemented in WinBUGS and tested.

11. Bayesian meta-analysis can be easy! A special multi-functional spreadsheet was created. It can be helpful in: menagement of data from studies, conducting meta-analyses with several classical (frequentialist) approaches conducting meta-analyses with several Bayesian models it automatically generate the adequate WinBUGS code for performing Bayesian meta-analysis by transforming the input data It transform the results obtained in WinBUGS to more user-friendly form

12. Bayesian meta-analysis can be easy!

13. Further development Network /Multiple Treatment Analysis analysis Diagnostic Test Accuracy Assessing the Clinical Significance

14. Thank you