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DRPS : Course Catalogue : Deanery of Molecular, Genetic and Population Health Sciences : Public Health Research

Postgraduate Course: Advanced Epidemiology (PUHR11062)

Course Outline
SchoolDeanery of Molecular, Genetic and Population Health Sciences CollegeCollege of Medicine and Veterinary Medicine
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThis 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Introduction to Epidemiology (PUHR11032) OR Introduction to epidemiology for public policy (IPHP11022)
Co-requisites Students MUST also take: Further Statistics (PUHR11051) AND Statistical Modelling (PUHR11040)
Prohibited Combinations Other requirements Equivalent experience also acceptable. Participants enrolling in this advanced course should have a strong grasp of quantitative epidemiological and statistical methods.
Course Delivery Information
Academic year 2018/19, Not available to visiting students (SS1) 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 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
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:
  1. 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
  2. Describe the concept of casual inference, use directed acyclic diagrams to represent possible causal pathways, and describe analytical approaches exploring causality in epidemiological data
  3. 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
  4. 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.
  5. Explain statistical and epidemiological methods underpinning diagnostic testing
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.
Keywordsepidemiology,causal inference,missing data
Course organiserDr Nazir Lone
Course secretaryMr Stuart Mallen
Tel: (0131 6)50 3227
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