Survival Analysis

Course code BIOS6030
Not available in 2016
2016 Course Timetables


Aims to enable students to understand the impact of computers and the corresponding availability of data sets (often very large data sets) on the way we think about data and proceed to analyse or report on it. Explores biostatistical applications of survival analysis with an emphasis on underlying theoretical and computational issues, practical interpretation and communication of results.

This course is offered in conjunction with the Biostatistics Collaboration of Australia (BCA).


Distance Education - Callaghan

  • Semester 1 - 2017

Learning Outcomes

On successful completion of the course students will be able to:

1. Understand the nature of survival data, especially censoring and truncation;

2. Summarise and display survival data using nonparametric methods;

3. Analyse survival data using the Cox proportional hazards model, including: hypothesis testing; diagnostic testing; the use of stratification and time-dependent variables, where appropriate; and interpret results;

4. Analyse survival data using parametric (accelerated failure-time) models;

5. Formulate, implement and interpret multiple-event models, and select appropriate model;

6. Determine sample size for a simple survival analysis;

7. Produce appropriate displays for publication.


Through a series of case studies, students will explore the various methods for handling survival data. These begin with the Kaplan-Meier curve definition and its extension to the comparison of survival prospects of several groups of courses using the logrank test and confidence intervals for relative risks, emphasising graphical displays and assessing underlying assumptions. The connection between Mantel-Haenszel methods and survival analysis is thus emphasised. The Cox proportional hazards model is introduced as a method for handling continuous covariates. Various extensions of this model, including time-dependent covariates and multiple outcomes, are considered, as well as the censored linear regression model.


Must be enrolled in Graduate Diploma of Medical Biostatistics or Master of Medical Statistics to enrol in this course. Pre-requisites: must have successfully completed BIOS6040, BIOS6050, BIOS6070, BIOS6170, and EPID6420.

Assessment Items

Written Assignment: Essays / Written Assignments

Written Assignment: Short answer exercises

Contact Hours

Distance Education - Callaghan

Self-Directed Learning

Self-Directed 8 hour(s) per Week for Full Term

The hours are an indication only.