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OVERVIEW
This course is for health economists, statisticians, and decision modellers interested in the extension of pair-wise meta-analysis to network meta-analysis (i.e. indirect treatment comparisons and mixed treatment comparisons - MTC), or anyone seeking an in-depth understanding of the statistical models for evidence synthesis, whether in the context of either clinical effectiveness or economic evaluation.

The course focuses on Bayesian methods for statistically combining evidence from networks of trials, integrating statistical estimation within a probabilistic modelling framework. The assumptions underlying both pair-wise meta-analysis and mixed treatment comparisons are critically examined. The course also covers methods for detecting and managing heterogeneity and inconsistency.

This is an informal, hands-on course, based on a mixture of lectures and practical work on published datasets using the Bayesian Markov chain Monte Carlo package WinBUGS. Course tutors are available throughout to answer questions and help with exercises.

Delegates who are new to WinBUGS should, when they have completed the course, be able to understand and code MTC analyses in WinBUGS, and to use WinBUGs for simple hierarchical models.

The course is a collaboration between the Department of Health Sciences, University of Leicester and the School of Social and Community Medicine, University of Bristol.

The methods taught on the course are designed to be fully compatible with the NICE Guide to the methods of technology appraisal 2013 and the NICE Decision Support Unit Technical Support Documents on Evidence Synthesis, and with the Report of the ISPOR Task Force on Indirect Comparisons

COURSE ORGANISERS: Keith Abrams, Tony Ades, Debbi Caldwell, Nicola Cooper, Sofia Dias, Alex Sutton, Nicky Welton

FULL DETAILS: http://www.bristol.ac.uk/social-community-medicine/projects/mpes/courses/treatment-comparisons/

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