Description
This module builds on the core module Economic Evaluation in Health Care and will provide students with more advanced training in economic evaluation alongside clinical trials and using decision-analytic models. The module is optional for MSC Health Economics and Decision Science students, and it is open to students on other IGH MSc/PG Dip students. The content of the course includes:
- Advanced concepts in health economic evaluation
- Methods for identifying, measuring and valuing resource use
- Methods for measuring and valuing generic health outcomes
- Estimation of costs, QALYs and cost-effectiveness alongside clinical trials
- Developing and populating a decision-analytic model
- Implementation of decision-analytical modelling in R
- Characterising different types of uncertainty
- Basic and advanced sensitivity analysis
- Equity impacts and efficiency-equity trade-offs.
- Using economic evidence to inform decision making.
Both the teaching sessions and the final assessment will require the use of the R software. While the module includes an introductory session about R, the teaching of R is beyond the scope of the module, and hence students should have some working knowledge and/or familiarity with R.
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Aims of the module
This module aims to provide studentsÌýaims to advance students’ understanding of the methods for health economic evaluation, bothÌýalongside clinical trials and using decision-analytical models. By the end of the module students should be able to conduct both types of economic evaluation, estimate uncertainty and present the results in a form that is useful for decision makers.
Learning outcomes
By the end of the course, students will be able to:
- To understand advanced concepts in health economic evaluation
- To familiarise with the methods for measuring, valuing, and analysing health care resource use and costs
- To familiarise with the methods for measuring, valuing and analysing health outcomes of prime interest to economic evaluation, such as QALYs.
- To estimate and interpret incremental cost effectiveness ratios, net monetary benefit and the uncertainty that surrounds these estimates.
- To critically discuss the key issues with extrapolating costs and effects beyond the follow-up of clinical trials
- To critically assess the role of modelling for informing health care decision making
- To explore the different stages of developing a Markov model
- To understand the role of conceptual models for identifying, searching and selecting evidence for decision models.
- To explore both deterministic and probabilistic sensitivity analysis
- To report decision uncertainty using cost-effectiveness planes and cost-effectiveness acceptability curves
- To understand key considerations when translating the findings from economic evaluations into health care decision making.
- To explore potential efficiency-equity trade-offs and familiarise with distributional cost-effectiveness analysis.
Method of delivery
Students will be taught primarily through lectures. Computer-based tutorials, using R software, will enable students to consolidate concepts and techniques through a hands-on approach. The module does not require students to have previous experience with R, and an introduction to R session is included as part of the module. Exercises and computer practicals will be provided ahead of the tutorial sessions to encourage self-study. Moodle will be used to provide a repository of course materials.
Essential reading:
Drummond et al (2015). Methods for the Economic Evaluation of Health Care Programmes. 4th edition. Oxford Medical Publications
Briggs et al (2006) Decision Modelling for Health Economic Evaluation. Oxford Handbooks for Health Economic Evaluation.
Gray et al (2010) Applied Methods of Cost-effectiveness Analysis in Healthcare. Oxford Handbooks for Health Economic Evaluation.
Neumann et al (2016) Cost-effectiveness in Health and Medicine. 2nd Edition. Oxford University Press.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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