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Decision Modelling for Health Economic Evaluation - ECO00088M

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  • Department: Economics and Related Studies
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

This module provides a foundation for understanding and developing decision analytic models used in the evaluation of health care programmes and health technologies.

Related modules

Prerequisite: Evaluation of Health Care

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

The aim of this module is to provide an understanding and experience of the application of basic methods of clinical decision analysis to a range of decision problems in health care. In particular it provides:

  • a thorough understanding of the way clinical decision analysis can be used to evaluate diagnostic information;

  • a framework for the economic evaluation of alternative strategies of patient management;

  • and also informs allocative decisions about which health care technologies should be adopted and what research may be required to inform these decisions in the future.

Module learning outcomes

On completing the module the student will be able to: -

  • Structure a range of clinical decision problems as decision analytic models.

  • Construct and solve these models in a spreadsheet, conduct sensitivity analysis

  • Interpret and communicate the results of their analysis.

Identify the limitations of the models they construct and recognise circumstances when more sophisticated methods of decision analysis may be appropriate.

Module content

Week 1: Context and model conceptualisation

  • Interpreting a decision problem

  • Strategies, outcomes and states

  • Types of node within a decision tree

Practical - Structuring the test treatment decision problem in a simple example based on an imperfect diagnostic technology

Week 2: Decision trees

  • Developing the test treatment decision:

    • Mortality risk associated with test

    • Alternative treatment available

    • Optimal test cut-off

  • Calculating expected utilities of strategies

  • Alternative model structures

Practical - Implementing the test treatment decision model in a spreadsheet software

Week 3: Markov models

  • Limitations of decision trees

  • Transition probabilities and Markov trace

  • Tunnel states

  • Half-cycle correction

Practical - Using a Markov model to generate utilities in the test treatment decision problem

Week 4: Parameterisation and sensitivity analysis

  • Model structure

  • Sources of evidence

  • Deterministic (scenario) sensitivity analysis

  • Probabilistic sensitivity analysis

Practical - Conducting sensitivity analysis and interpreting results from the test treatment decision model

At the end of week 4, students will be assigned to a case study decision problem. From this point on, students will work in their groups in their own time on structuring, parameterising, executing and interpreting a decision model based on the case study. To ensure that students are on the right track there are two seminars timetabled.

Week 6/7: Case study review (model structure) student presentations

At the end of week 6 or beginning of week 7 students are required to give a presentation, as a group, to the rest of the class describing their decision problem, how they intend to structure their decision model and setting out the calculations required to parameterise that model given the evidence with which they are provided.

Week 10: Case study results student presentations

The group-work culminates in a group presentation in week 10 where students, as a group, present their decision model and findings from the case study.

Week 11: Advanced issues in decision modelling in health economics

  • Survival analysis

  • Patient level simulation and discrete event models

  • Dynamic models for infectious diseases

Practical - Using an output from a survival analysis model as an input into a decision model

Week 13: Submission deadline for individual case study report.

Indicative assessment

Task % of module mark
Essay/coursework 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 100

Module feedback

Following departmental policy, written cohort feedback will be provided online.

Indicative reading

Torrance GW, ed. Chapter 9. In: Methods for the Economic Evaluation of Health Care Programmes / Michael F. Drummond, Mark J. Sculpher, Karl Claxton, Greg Stoddart, George W. Torrance. 4th ed. Oxford University Press,; 2015:xiii, 445 p.:

Briggs A. Decision Modelling for Health Economic Evaluation /. (Claxton K, Sculpher MJ, eds.). Oxford University Press; 2006.

Briggs A. An Introduction to Markov Modelling for Economic Evaluation. PharmacoEconomics. 1998;13(4):397-409.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.