Statistical methods for Health Technology Assessment and Evidence Based Medicine MED5372
- 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
This course will deliver the fundamentals of statistical methodology that underpin health technology assessment and evidence based medicine.
10 week online course comprising 10 lectures and 10 accompanying practical exercises. The lectures will be 45 mins/1 hr in duration and the exercise associated with each lecture will take a notional 2 hours for the student to complete. Each week the academic lead will contribute to and answer questions on a discussion board.
Requirements of Entry
Written Assignment/Coursework - Students will be given data sets and a series of research questions, which they will need to answer by applying methods from the course and writing up as a report (90% - 2000 words). The remaining 10% assessment mark will be evaluated based upon student engagement and contribution with the discussion forum throughout the course.
This course aims to equip students with the necessary statistical skills so they can analyse and interpret data that commonly arise from health technology assessments and evidence based medicine more generally. Furthermore, to provide students with the necessary background knowledge and experience so they can critically appraise published work from a statistical perspective.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ Create and critically assess the ouput from applying different approaches of summarising quantitative data.
■ Critically discuss the concepts of Frequentist and Bayesian statistics and the differences between them.
■ Critically discuss the concepts of probability and probability distributions.
■ Critically interpret and evaulate the characteristics of common measures of effect size in HTA / EBM.
■ Create, interpret and critically discuss output after undertaking statistical inference from a Frequentist perspective.
■ Create, interpret and critically discuss output after undertaking statistical inference from a Bayesian perspective.
■ Create, interpret and critically discuss output after undertaking linear and logistic regression within the framework of generalised linear models.
■ Critically discuss the fundamentals of survival analysis.
■ Critically appraise HTA / EBM literature from a statistical perspective.
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.