The module will introduce the basic concepts of survival analysis and to describe and explain how survival analysis can be applied to medical data in other application areas, with emphasis on the use of appropriate statistical software.
Pre-requisites: MATH2010 AND MATH2011
Having successfully completed this module you will be able to:
Introduction to survival analysis; standard survival time distributions; survivor, hazard and related functions; censoring.
Kaplan-Meier and related estimators for the survivor function; comparison of survivor functions using log-rank and related non-parametric tests.
Properties of survival time distributions and maximum likelihood estimation with right-censored data.
Accelerated failure time models and Cox proportional hazards models: application, model-building, diagnostics, interpretation.
Examples of survival analyses from the literature: critical review.
Survival analyses using statistical packages such as SAS and R.
Lectures, tutorials, formative problem sheet, summative coursework, support materials on Blackboard, private study.
| Type | Hours |
|---|---|
| Independent Study | 63 |
| Teaching | 12 |
| Total study time | 75 |
Hosmer DW, Lemeshow S & May S (2008). Applied Survival Analysis. Wiley.
Kleinbaum DG (2005). Survival Analysis: A Self-Learning Text. Springer.
Collett D (2014). Modelling Survival Data in Medical Research. CRC/Chapman & Hall.
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Repeat type: Internal & External