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

Postgraduate Course: Statistical Modelling for Epidemiology (PUHR11064)

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)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis online course describes the main principles of statistical modelling and introduces three types of model commonly used in epidemiological studies: linear regression, logistic regression and survival analysis.
Course description This online course is designed to help students who have already studied the common statistical methods for 1 and 2 group comparisons increase their knowledge and practical skills by introducing the principles and practice of statistical modelling. Three main types of model will be described (linear, logistic and survival models), drawing out both the unique features and similarities, as well as discussing good practice in diagnostic model checking, variable selection and model building (general topics applicable to any type of model fitting process).

Topics to be covered include:
- simple and multifactorial linear models, including ANOVA models
- binary logistic regression
- Kaplan-Meier plots and log-rank tests
- Cox proportional hazards model
- methods for assessing appropriate formats for including explanatory variables
- variable selection methods
- diagnostic methods

The course will consist of a mix of recorded lectures, readings, and practical exercises conducted in the open source R statistical programming language. For each practical, students will be encouraged to discuss their analysis plans, share useful code shortcuts and discuss their results on the discussion boards. The data sets for these exercises will be based on real epidemiological data sets and/ or common problems, although the principles will be applicable more broadly across a wide range of medical and scientific research.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Data Analysis for Epidemiology (PUHR11063) or equivalent course plus knowledge of R statistical programming environment.
Information for Visiting Students
Pre-requisitesData Analysis for Epidemiology (PUHR11063) or equivalent course plus knowledge of R statistical programming environment.
High Demand Course? Yes
Course Delivery Information
Academic year 2016/17, Not available to visiting students (SS1) Quota:  None
Course Start Flexible
Course Start Date 01/08/2016
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 5, Seminar/Tutorial Hours 1, Online Activities 35, Feedback/Feedforward Hours 5, Formative Assessment Hours 5, Revision Session Hours 1, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 46 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 40%: Portfolio (online assessment), consisting of student's selection of their best contributions to the analysis plan discussion forum.

60%: Project, involving analysis of a data set and a written report of the results.
Feedback Each week's activity will mirror the main course assessment (project) at a smaller scale, and therefore students will receive peer and tutor feedback throughout the course on key aspects of their approach to fitting and interpreting statistical models.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. select and execute suitable statistical models for continuous, binary and survival outcome variables in common situations.
  2. critically interpret linear, logistic and survival models.
  3. use model-based approaches to explore interactions and confounding.
  4. implement the principles of good practice in model building and validation.
Reading List
1. Kirkwood BR & Sterne AC (2003) Essential Medical Statistics (2nd Edition). Blackwood: Oxford.

2. Campbell MJ, Machin D and Walters SJ (2007): Medical Statistics, a Textbook for the Health Sciences, 4th edition. Wiley: Chichester.

3. Knell, RJ (2015) Introductory R [eBook];

4. Dalgaard P (2008) Introductory Statistics with R. Springer, 2nd edition.
Additional Information
Graduate Attributes and Skills The skills developed by this course are key for most types of epidemiological enquiry, and thus fall broadly under the overarching Enquiry and Lifelong Learning attribute. In particular, the core tasks of analysis and project work involve problem solving, critical thinking and evaluation, which map closely to the Research and Enquiry cluster. However, this will also foster Personal and Intellectual Autonomy, contributing to the student's ability to conceive, design, execute and interpret epidemiological research.
KeywordsStatistics,statistical methods,R,modelling,regression,linear,logistic,survival analysis
Course organiserDr Niall Anderson
Tel: (0131 6)50 3212
Course secretaryMiss Sarah Gordon
Tel: (0131 6)51 7112
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