Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Decision Analytic Modelling and Early Health Technology Assessment MED5628

  • Academic Session: 2022-23
  • School: School of Health and Wellbeing
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes
  • Taught Wholly by Distance Learning: Yes

Short Description

The course will introduce students to Early Health Technology Assessment (HTA) which uses the tools of HTA to assist developers of health technologies in the design and placement of their technologies. It is also used by decision makers in health to estimate a technology's impact on health and costs. Decision analytic modelling is a method in HTA which is widely used. This course will teach students to develop decision models in the context of early HTA.


This course is made up of lectures and practical classes in semester 2.

Requirements of Entry


Excluded Courses





Decision model (50%) ILO2

Written report (2,000 words approx- 50%) ILO1,3,4

Course Aims

The aim of this course is to introduce students to core material in the area of early health technology assessment (HTA). This will include the rationale for conducting early HTA, its features and a range of commonly used methods. The course also teaches practical skills involved in developing a decision analytic model.

Intended Learning Outcomes of Course

By the end of this course students will be able to:


1. Critically compare how early HTA differs from HTA undertaken later in a technology lifecycle

2. Design and build decision trees and markov models including probabilistic sensitivity analysis and value of information analysis

3. Critically appraise methods of stakeholder engagement in terms of their appropriateness to the resources available

4. Critically evaluate the quality of early economic evaluations

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.