Issues in Clinical Data Modelling
Information about the course objectives, learning and teaching activities and assessment.
The course will expose students to the challenges of gathering, accessing and modelling clinical data. It will consist of a series of lectures by clinical researchers, and will involve a one day clinic visit that will give students an opportunity to experience first hand the complexities of clinical data gathering. Upon completion of the course, the students will have acquired:
- The ability to critically assess and discuss the challenges associated with clinical data modelling across a variety of contexts and diseases, in particular with respect to noise in the data, patient stratification and regulatory and ethical issues;
- Familiarity with the data acquisition protocols in one area of biomedicine through a shadowing experience.
Timetable
The course will be delivered as weekly lectures, held at 2pm on Wednesdays.
Assessment
The course assessment will consist of short written report (10 pages max) detailing and critically reflecting on the clinical data modelling challenges encountered by the student during the shadowing experience. The students will then give a short presentation (20 min plus questions) to the course organiser and the rest of the class on their shadowing experience and the modelling lessons learnt there. Topics to be covered include:
- motivation: why is data being collected?
- protocol: what data is being collected and how?
- analysis: which methods are currently used to analyse the data in the lab?
- possible developments: how could analysis methods be improved? what are the challenges to doing so?
- implications: what are the broader implications of clinical research in this area?
Feedback on assessed coursework will be provided within two weeks, and will include formative comments on work in relation to concepts studied in the course. Report drafts will be reviewed by peers, the course instructor, and individual supervisors under a provided rubric.