Survival Analysis - MAT00018H

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  • Department: Mathematics
  • Module co-ordinator: Dr. Agostino Nobile
  • Credit value: 10 credits
  • Credit level: H
  • Academic year of delivery: 2019-20

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Module will run

Occurrence Teaching cycle
A Autumn Term 2019-20

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

At the end of this module you should be able to:

  • 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

[Pre-requisites for Natural Sciences students: Statistics Option MAT00033I.]

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.

Assessment

Task Length % of module mark
University - closed examination
Survival Analysis
2 hours 100

Special assessment rules

None

Reassessment

Task Length % of module mark
University - closed examination
Survival Analysis
2 hours 100

Module feedback

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

Indicative reading

Klein, J.P. and Moeschberger, M.L. (2003). Survival Analysis: Techniques for Censored and Truncated Data, Second edition, Springer.



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring 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 by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.