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Survival Analysis - MAT00039M

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  • Department: Mathematics
  • Credit value: 10 credits
  • Credit level: M
  • Academic year of delivery: 2022-23

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations


Additional information

This module shares teaching (lectures, practicals) with the MAT00018H module, but tutorials for the two modules are separate. The fortnightly assignments, as well as the examination paper, of MAT00018H and MAT00039M differ, with the latter containing more challenging elements.

Module will run

Occurrence Teaching period
A Autumn Term 2022-23

Module aims

To present a statistical methodology for the analysis of survival time data stemming from medical experiments where patients are subjected to a treatment.

Module learning outcomes

  • Provide descriptive statistics and graphical summaries of information contained in data from survival experiments in different types of studies.
  • Use estimation and hypothesis testing for inference.
  • Use proportional hazards regression techniques to make inferences about the possible relationship between survival time and potential risk factors.

Module content

Syllabus

  • Censoring and truncation.

  • Basic quantifiers of survival: survival, hazard and cumulative hazard functions.

  • Non-parametric estimation of the survival and cumulative hazard functions.

  • Log-rank test.

  • Parametric models for survival data: exponential, Weibull, log-normal, log-logistic.

  • Accelerated Failure time model.

  • Proportional Hazards Property.

  • Cox Proportional Hazards Model.

  • Variable and Model selection.

  • Checking the Proportional Hazards assumption.

  • Stratified Cox PH model.

  • Cox-Snell, Martingale and Schoenfeld residuals.

Indicative assessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Module feedback

Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.

Indicative reading

J P Klein & M L Moeschberger, Survival Analysis: Techniques for Censored and Truncated Data.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.