eBayesMet - on the main activities and results regarding to e-learning

Kraków, 29.10.2011

Mateusz Nikodem, CASPolska Association

eBayesMet (11.2009 – 10.2011)

Partners:•CASPolska Association - leader

•Queen Mary University of London

•Academic Medical Center, Amsterdam

•EMMERCE EEIG

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

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?

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?

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?

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:

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:

The substantial content

Methods for direct comparisonYou can learn how to handle with dataof different characteristics

including problematic issues like small sample size and rare events

Including observational studiesYou 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 comparisonsYou can develop your skills in conducting indirect and mixed comparison

(in basic structures of data)

Lessons

Introduction

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.

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

Bayesian meta-analysis can be easy!

Further development

• Network /Multiple Treatment Analysis analysis

• Diagnostic Test Accuracy

• Assessing the Clinical Significance

Thank you