Postgraduate Course: Advanced Epidemiology (PUHR11062)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course will introduce advanced epidemiological concepts and explore quantitative methods required for the interpretation and conduct of epidemiological studies. This course will be of particular relevance to participants who plan to undertake epidemiological research with a good grasp of quantitative methods. |
| Course description |
The aim of this course is to enable participants to understand a range of epidemiological concepts beyond those taught in introductory courses, to interpret advanced quantitative methods used in epidemiological studies, and to apply these methods in their own research.
Topics covered include: missing data, causal inference, directed acyclic graphs, effect modification, measurement error, regression dilution bias, diagnostic testing.
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Course Delivery Information
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| Academic year 2017/18, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
98 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Assignment comprising a report of analyses of a dataset. |
| Feedback |
Individual feedback will be given on the assignment. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Discuss the problems caused by missing data, describe mechanisms of how missing data arise, and understand analytical methods used to analyse datasets with missing data
- Describe the concept of casual inference, use directed acyclic diagrams to represent possible causal pathways, and describe analytical approaches exploring causality in epidemiological data
- Distinguish effect modification from confounding, understand the concept of effect modification on additive and multiplicative scales, describe the importance of effect modification in generalising results from studies, and demonstrate methods of presenting effect modification
- Describe how random measurement error affects the estimates of associations between exposure and outcome variables, understand how this relates to regression dilution bias, and recall methods used to deal with random measurement error.
- Explain statistical and epidemiological methods underpinning diagnostic testing
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Reading List
| Individual reading lists will be provided with each lecture |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Additional Class Delivery Information |
The course will consist of a mixture of lectures, tutorials, computer lab sessions. |
| Keywords | epidemiology,causal inference,missing data |
Contacts
| Course organiser | Dr Nazir Lone
Tel:
Email: nazir.lone@ed.ac.uk |
Course secretary | Mr Stuart Mallen
Tel: (0131 6)50 3227
Email: Stuart.Mallen@ed.ac.uk |
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