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Curricular information is subject to change
On successful completion of this module students should be able to explain the fundamental principles of survival analysis and why survival data requires special methods. They should be able to demonstrate knowledge and understanding of the concept of censored data and the methods developed to deal with it and with common distributions used in survival analysis. They should be able to produce Kaplan-Meier graphs, explain the use of the well known Cox model and accelerated failure time models. They will be able to demonstrate a critical understanding of how the methods they have learnt can be placed into the context of standard statistical theory. They will have developed skills in analysing sets of survival data and interpreting the results using SAS (and R). Postgraduate students are expected to demonstarte a deeper knowledge and understanding than undergraduates.
| Student Effort Type | Hours |
|---|---|
| Lectures | 24 |
| Computer Aided Lab | 10 |
| Specified Learning Activities | 36 |
| Autonomous Student Learning | 75 |
| Total | 145 |
Probability and distribution theory. Inference and hypothesis testing. Linear models, ANOVA and multiple regression.
| Description | % of Final Grade | Timing |
|---|---|---|
| Examination: 2 hour exam | 80 |
2 hour End of Trimester Exam |
| Assignment: Approximately 8 assignments | 20 |
Varies over the Trimester |
Compensation
This module is not passable by compensation
Resit Opportunities
End of Semester Exam
Remediation
If you fail this module you may repeat, resit or substitute where permissible