Postgraduate Course: Statistical Modelling for Epidemiology (PUHR11064)
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) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This 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
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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-requisites | Data Analysis for Epidemiology (PUHR11063) or equivalent course plus knowledge of R statistical programming environment. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2017/18, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Flexible |
| Course Start Date |
07/08/2017 |
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 %
|
| 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:
- Match model type (linear regression, logistic regression or survival analysis) to outcome data and understand underlying assumptions
- Fit models and check underlying assumptions
- Think critically about and use statistical models to solve epidemiological problems
- Use statistical software confidently and interpret and communicate model output
- Autonomously undertake your own statistical modelling
|
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. |
| Keywords | Statistics,statistical methods,R,modelling,regression,linear,logistic,survival analysis |
Contacts
| Course organiser | Dr Niall Anderson
Tel: (0131 6)50 3212
Email: |
Course secretary | Mrs Rosemary Porteous
Tel: (0131 6)50 9835
Email: |
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