MAS3912 : Survival Analysis
- Offered for Year: 2018/19
- Module Leader(s): Mrs Carol Andrew
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
|Semester 2 Credit Value:||10|
To provide an appreciation of the need for and an understanding of, the principal statistical methods required in the analysis of survival data.
There are many areas where interest focuses on data which measures the time to some event. In recent decades the principal application for such data has been how long patients survive before some event occurs. The event may be death or it may be the recurrence of a disease which had been in remission, or some other event. Applications are not solely medical: how long it takes a battery to run down or how long a component in a machine lasts before it fails are just two industrial examples. Such data are known as survival data, or sometimes lifetime data, and their analysis is called survival analysis. The main complication with survival data is that many observations will be ‘censored’, i.e. they are only partially observed. For example, when a trial of a new treatment for cancer is terminated many of the patients will still be alive. Therefore the survival times of those who died will be known exactly whereas for those still alive at the end of the trial, their survival time is only known to exceed their present survival. Methods for dealing with this form of data will be considered.
Outline Of Syllabus
Time-to-event data, censoring patterns. Non-parametric survival analysis: calculation of Kaplan-Meier estimates; use of log-rank statistics. Parametric survival analysis: exponential, Weibull and log-logistic distributions; likelihood analysis of effect of covariates. Proportional hazards model: partial likelihood; diagnostics; time-varying effects. Frailty. Prediction and explained variation.
|Guided Independent Study||Assessment preparation and completion||1||13:00||13:00||Revision for unseen exam|
|Guided Independent Study||Assessment preparation and completion||1||2:00||2:00||Unseen exam|
|Scheduled Learning And Teaching Activities||Lecture||3||1:00||3:00||Problem classes|
|Scheduled Learning And Teaching Activities||Lecture||2||1:00||2:00||Revision lectures|
|Scheduled Learning And Teaching Activities||Lecture||25||1:00||25:00||Formal lectures|
|Guided Independent Study||Independent study||2||6:00||12:00||Preparation for problem-solving exercises|
|Guided Independent Study||Independent study||1||22:00||22:00||Studying, practising and gaining understanding of course material|
|Guided Independent Study||Independent study||3||3:00||9:00||Review of problem-solving exercises and project|
|Guided Independent Study||Independent study||1||12:00||12:00||Preparation for project|
Teaching Rationale And Relationship
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the students’ abilities at applying the theory to solving problems. Tutorials are used to identify and resolve specific queries raised by students and to allow students to receive individual feedback on marked work. In addition, office hours (two per week) will provide an opportunity for more direct contact between individual students and the lecturer.
The format of resits will be determined by the Board of Examiners
|Prob solv exercises||2||M||5||Problem solving exercises|
|Prob solv exercises||2||M||10||Individual project|
Assessment Rationale And Relationship
A substantial formal unseen examination is appropriate for the assessment of the material in this module. The problem solving exercises are expected to consist of two assignments of equal weight: the exact nature of assessment will be explained at the start of the module. The exercises and the project allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; these are thus formative as well as summative assessments.