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.