PE4S120 - Understanding the Use of Data in Health Technology Assessment 13 Mar 2017 - 31 Aug 2030 | Version 2

Associated Module Information

Module Code: PE4S120
Module Title: Understanding the Use of Data in Health Technology Assessment
Faculty: Faculty of Life Sciences and Education
Faculty Group: Allied Health and Chiropractic
Faculty Sub Group: Clinical Services
Module Leader: Karl New
Module Team:
First Intended Intake: MAR 2017 Final Year of Intake: 2030
Date Closed:
Credit Value: 20 Credit Level: 7
Language: English
Percentage of Module Taught in Welsh: 0
Equivalent Module:
HECOS codes: 100260 - healthcare science
HECOS Code Weighting: 100

Document Version Information

Version 2
Valid From 13 Mar 2017
Valid To 31 Aug 2030

Module Aims

Develop a critical understanding of data interpretation including levels of evidence and statistical interpretation of data.

Content Summary

Indicative content to include topics outlined below and/or any other relevant current topics to fulfil the module aims and learning outcomes:

Topics may include:

Additional Data Sources: Real World Data, Expert Opinion, The Patient’s Voice

Statistics: Confidence intervals and p-values

Statistics: Hazard ratios and NNT

Statistics: Bayesian statistics and Cox model

Statistics: Sensitivity Analysis and Marginal Analysis

Statistics: Indirect Comparison and Meta-Analysis

Learning and Teaching Methods

Activity Type Hours
Independent Study 80
Directed Study 40
Seminars 40
Problem/Challenge-based Learning 40
Total Hours Selected 200

Learning Outcomes

# Learning Outcome
LO1 Critically evaluate the validity of published data.
LO2 Critically appraise statistical interpretation of scientific data related to health economics.

Module Requisites

N/A

Assessment Criteria

Assessment Category Assessment Type Description Duration Word Count Weight (%) Best of? Pass Mark
Synchronous Online Assessment Time-constrained assessment (Online) 1 Assessment to be completed in a specific timescale, which is neither an invigilated examination nor a piece of coursework. To be completed over an extended period (e.g. a 2-hour task to be completed within 48 hours, or a 24-hour assessment). Learna: End of module 1-hour Case-based Multiple-choice Examination related to Understanding the Use of Data in Health Technology Assessment to be completed within a 10-day period. 60 N/A 30 No 40
Asynchronous Assessment Self Reflective Assessment 1 A personal record of a student’s learning experiences. It requires students to record and reflect upon their observations and responses to situations, which can then be used later to explore and analyse ways of thinking and being in context. Generally involves critical diaries, learning logs and written / visual journals. Learna: Reflective journal. 0 600 10 No 40
Asynchronous Assessment Project 1 A detailed analysis of a topic, involving some original research undertaken by the candidate who makes use of data and/or primary sources. Learna: Completion of an individual/group task related to Understanding the Use of Data in Health Technology Assessment. 0 1000 20 No 40
Asynchronous Assessment Case study 1 A concentrated inquiry into a single case or subject. Learna: Case-based scenarios and a discussion forum related to Understanding the Use of Data in Health Technology Assessment. 0 2500 40 No 40

Assessment Matrix

Assessment Type Learning Outcomes
LO1 LO2
Time-constrained assessment (Online) 1
Self Reflective Assessment 1
Project 1
Case study 1

Reading List

The module reading list is available to the students to access via electronic links on the learning platform (Moodle).

In addition to the list below, each list is supplemented with up-to-date peer-reviewed journal articles and studies that are continually refreshed as guidelines change and new treatments and technologies emerge.

Additional reading resources are also provided where relevant by the tutors during the running of the module in the discussion forum to keep the reading resources current and relevant.

Indicative Module 4 Reading:

Afzali, H.A., H., Karnon, J., Theou, O., Beilby, J., Cesari, M. and Visvanathan, R. (2019) ‘Structuring a conceptual model for cost-effectiveness analysis of frailty interventions’, PloS one, 14(9), p.e0222049.

Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.B., Poole, C., Goodman, S.N. and Altman, D.G. (2016) ‘Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations’, European journal of epidemiology, 31(4), pp.337-350.

CEBM. (2020). Number needed to treat (NNT). [Online]

YHEC (2016) Bayesian Analysis. [Online]

Padmasawitri, T.A., Saragih, S.M., Frederix, G.W., Klungel, O. and Hövels, A.M. (2020) Managing Uncertainties Due to Limited Evidence in Economic Evaluations of Novel Anti-Tuberculosis Regimens: A Systematic Review, PharmacoEconomics-open, pp.1-11.

Grimm, S.E., Fayter, D., Ramaekers, B.L., Petersohn, S., Riemsma, R., Armstrong, N., Pouwels, X., Witlox, W., Noake, C., Worthy, G. and Kleijnen, J. (2019) ‘Pembrolizumab for Treating Relapsed or Refractory Classical Hodgkin Lymphoma: An Evidence Review Group Perspective of a Nice Single Technology Appraisal’, Pharmacoeconomics, pp.1-13.