Survival Analysis - MAT00018H

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Additional information

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

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

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

 

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 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.