Postgraduate Course: Advanced Epidemiology (PUHR11062)
|Deanery of Molecular, Genetic and Population Health Sciences
|College of Medicine and Veterinary Medicine
|Credit level (Normal year taken)
|SCQF Level 11 (Postgraduate)
|Not available to visiting students
|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.
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.
Course Delivery Information
|Academic year 2018/19, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
|Assignment comprising a report of analyses of a dataset.
|Individual feedback will be given on the assignment.
|No Exam Information
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
|Individual reading lists will be provided with each lecture
|Graduate Attributes and Skills
|Additional Class Delivery Information
|The course will consist of a mixture of lectures, tutorials, computer lab sessions.
|epidemiology,causal inference,missing data
|Dr Nazir Lone
|Mr Stuart Mallen
Tel: (0131 6)50 3227