HSC3101 : Decision Modelling for Health Data Science
HSC3101 : Decision Modelling for Health Data Science
- Offered for Year: 2026/27
- Module Leader(s): Dr Gurdeep Sagoo
- Lecturer: Mr Stephen Rice, Mr Giovany Orozco Leal
- Owning School: Population Health Sciences
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
| Code | Title |
|---|---|
| MAS2901 | Statistical Inference |
Pre Requisite Comment
MAS2903 and MAS2906 also desirable rather than essential.
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
The aim of this module is to provide an understanding of decision modelling methods used in the evaluation and analysis of technologies used in health care.
Decision analysis and modelling has been used widely across many disciplines but in health care it is an established analytic framework to inform decision making under uncertain circumstances. Health Economists play a vital role in helping to quantify the impact of uncertainty in order to inform clinical and health care decision making at population and individual levels. Over the last two decades in the UK, NICE (the National Institute for Health and Care Excellence) have been major drivers of developments in the analytical methods used by health economists, with the aim of making the understanding of uncertainty more transparent in decision making when implementing technologies into wide-spread use with the UK NHS.
This course will cover well-established methods used in decision modelling with examples from real studies that the module teaching team are involved in.
Outline Of Syllabus
What is economic evaluation and why is it useful? The role of decision analysis in economic evaluation and health data science. Key aspects of decision modelling. Methods for more advanced decision modelling. Analysing and presenting outputs from decision modelling. Uncertainty in decision making and value of information analysis.
Learning Outcomes
Intended Knowledge Outcomes
At the end of the module it is expected that a student will be able to:
1. Outline how and why decision modelling is used in health data science.
2. Summarise the processes in developing decision models and what issues need to be addressed.
3. Describe the advantages and disadvantages of different types of decision modelling.
4. Describe the need and rationale for best reporting standards in presenting decision models.
Intended Skill Outcomes
At the end of the module a student will be able to:
1. Determine the most appropriate decision model to develop for a given decision problem.
2. Design and construct a decision model (based in Microsoft Excel).
3. Propose a suitable approach to analysing a clinical trial.
4. Report outputs and sensitivity analyses according to best reporting standards.
Students will develop skills across the cognitive domain (Bloom’s taxonomy, 2001 revised edition):
remember, understand, apply, analyse, evaluate and create.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 20 | 1:00 | 20:00 | Formal lectures |
| Guided Independent Study | Assessment preparation and completion | 2 | 4:00 | 8:00 | Completion of in course assessments |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | Unseen exam |
| Scheduled Learning And Teaching Activities | Lecture | 2 | 1:00 | 2:00 | Problem classes |
| Scheduled Learning And Teaching Activities | Lecture | 2 | 1:00 | 2:00 | Revision lectures |
| Scheduled Learning And Teaching Activities | Practical | 3 | 1:00 | 3:00 | Computer practicals |
| Guided Independent Study | Independent study | 22 | 1:00 | 22:00 | Preparation time for lectures |
| Guided Independent Study | Independent study | 2 | 1:30 | 3:00 | Review of coursework |
| Guided Independent Study | Independent study | 25 | 1:00 | 25:00 | Background reading on lectured content |
| Guided Independent Study | Independent study | 13 | 1:00 | 13:00 | Revision for unseen exam |
| Total | 100:00 |
Jointly Taught With
| Code | Title |
|---|---|
| HSC8101 | Decision Modelling for Health Data Science with Advanced Topics |
Teaching Rationale And Relationship
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem classes are used to help develop the students’ abilities at applying the theory to solving problems. Practical classes are used to help the students’ ability to apply the methods in practice.
The teaching methods are appropriate to allow students to develop a wide range of skills. From understanding basic concepts and facts to higher-order thinking.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 120 | 2 | A | 60 | Written exam, comprising a Section A and a Section B |
Exam Pairings
| Module Code | Module Title | Semester | Comment |
|---|---|---|---|
| Decision Modelling for Health Data Science with Advanced Topics | 2 | N/A |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Prob solv exercises | 2 | M | 40 | Coursework 2. Up to 10 page typeset report based upon a set assignment comprising open-ended questions |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Prob solv exercises | 2 | M | Coursework 1. 40 minute class test, conducted during one of the timetabled one hour lecture slots |
Assessment Rationale And Relationship
A substantial formal unseen practical examination is appropriate for the assessment of the material in this module. The format of the examination will enable students to reliably demonstrate their own knowledge, understanding and practical application (in Microsoft Excel) of learning outcomes.
The coursework assignments allow the students to develop their critical appraisal techniques, to highlight their understanding of the methods and rationale learnt in the module, to assess their progress and to receive feedback; the summative assessment has a secondary formative purpose as well as its primary summative purpose.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- HSC3101's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- HSC3101's past Exam Papers
General Notes
N/A
Welcome to Newcastle University Module Catalogue
This is where you will be able to find all key information about modules on your programme of study. It will help you make an informed decision on the options available to you within your programme.
You may have some queries about the modules available to you. Your school office will be able to signpost you to someone who will support you with any queries.
Disclaimer
The information contained within the Module Catalogue relates to the 2026 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, staffing changes, and student feedback. Module information for the 2027/28 entry will be published here in early-April 2027. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.