Health Technology Assessment

Module code: MD7449

Health Technology Assessment (HTA) attempts to answer the following questions about new health technologies including drugs, medical equipment, diagnostic techniques, public health programmes:

• Does the technology work?
• For whom (does it work)?
• At what cost?
• How does it compare with the alternatives (in particular current clinical practice)?

This module provides an introduction to the methods used in HTA, in particular the synthesis of evidence beyond a pairwise meta-analysis and developing an economic decision model to assess cost-effectiveness.

Advanced Evidence Synthesis

This topic will start with a review of basic meta-analysis for different outcomes. You will then use a Bayesian implementation of meta-analysis and meta-regression methods, using the WinBUGS software and discuss the advantages of using this approach. More advanced methods such as mixed treatment comparisons (or network meta-analysis), a very important generalisation of the standard meta-analysis model increasingly used in HTA when more than two alternative treatment options exist, meta-analysis methods for the evaluation of diagnostic tests, and multivariate meta-analysis of correlated outcomes (with application to the evaluation of surrogate endpoints) will be introduced.

Decision Modelling

During this part of the module we will start by outlining the motivation for and principles of decision modelling. Both clinical decision models (weighing up benefits and side-effects of treatments) and economic decision models (assessing cost-effectiveness) will be covered. Simple decision trees and then Markov models will be described. After the basic principles of decision modelling are covered, methods for estimating individual parameters for such models will be considered. This includes the integration of meta-analyses with decision models. Given there is usually (considerable) uncertainty in any decision problem, value of information methods, to assess whether conducting further studies to reduce this uncertainty would provide good value for money, will be presented briefly. Finally a demonstration of research carried out at Leicester will be given.