EPM5010 Survival analysis
Faculty of Medicine, Nursing and Health Sciences
EPM5010 Survival analysis is a level 5, 6-credit-point, postgraduate unit from the Faculty of Medicine, Nursing and Health Sciences, offered in 2021 in Semester 1 at Alfred Hospital. It needs EPM5003, EPM5004, EPM5002, EPM5014 and MPH5040.
- Credit points
- 6
- Offered in 2021
- Semester 1
- Alfred Hospital
- Assessment
- No exam
- 1 task
This is the 2021 handbook entry. See the 2023 entry.
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Requisites
Before EPM5010
Prerequisites
Pass these before you enrol.
After EPM5010
No unit lists EPM5010 as a prerequisite in the 2021 handbook.
Enrolment rules
Overview
Biostatistical applications of survival analysis with emphasis on underlying theoretical and computational issues, practical interpretation and communication of results. Case studies, students will explore the various methods for handling survival data. Kaplan-Meier curve definition and its extension, survival prospects using logrank test and confidence intervals for relative risks, graphical displays and assessing underlying assumptions. Mantel-Haenszel method's connection to survival analysis. Cox proportional hazards model for handling continuous covariates. Various extensions of this model, including time-dependent covariates, multiple outcomes and censored linear regression model.
Offerings in 2021
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Alfred Hospital | De |
Assessment
- Written assignments100%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Understand the major theoretical and computational issues underlying survival analysis.
- 2
Develop appropriate survival analysis strategies based on unit matter considerations, including choice of models, control for confounding and appropriate parameterisation.
- 3
Be proficient at using at least two different statistical software packages (e.g. Strata, Excel) to perform survival analysis.
- 4
Understand the construction, use and interpretation of appropriate graphs for showing results and checking statistical assumptions.
- 5
Express the results of statistical analyses of censored data in language suitable for (a) communication to medical investigators (b) publication in biomedical or epidemiological journals
- 6
Appreciate the role of newer techniques including parametric non-modelling, floating odds ratios and competing risks.
Workload and teaching
- Teaching approachOnline learning
For full details of the teaching approach to this unit, please refer to Moodle.
Learning resources
Required resources
See Moodle for details about required resources.
Contacts
- Chief Examiners
- Professor Andrew Forbes