Available in 2017, 2018

Course handbook


This course introduces the fundamental concepts of time-to-event data analysis. Following a review of the basic principles (censoring, truncation, survival distributions, hazard functions, partial likelihood estimation techniques), students will be given a range of applied case studies in the field of medicine to analyse in SAS software.

Availability2017 Course Timetables2018 Course Timetables

WebLearn GradSchool

  • Semester 2 - 2017
  • Semester 1 - 2018

Learning outcomes

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

1. Understand the basic theoretic and applied principles of survival analysis

2. Analyse survival data using appropriate routines in SAS software

3. Check underlying model assumptions

4. Generate diagnostic plots

5. Compute sample size and power for survival models

6. Prepare publication-ready summary tables and write-up of results


Survival analysis remains one of the most important tools in the field of medical research. Topics covered in this course will include basic parametric (Coale-McNeil, Exponential, Generalized F, Generalized Log Gamma, Gompertz, Weibull), non-parametric (Kaplan-Meier, Nelson-Aalen, Mantel-Haenszel), and semi-parametric (proportional hazards) methods of survival analysis.  Additional topics will include competing risks, time-dependent covariates, frailty models, accelerated failure time models, the Fine-Gray model, Makeham-type models, Wei-Lin-Weissfeld multivariate failure time models, and piecewise exponential survival models.  Methods of model checking such as Martingale and Schoenfeld deviance residuals plots also will be covered.


To enrol in this course you must have successfully completed BIOS6050.

Assessment items

Written Assignment: Essays / Written Assignments

Written Assignment: Short answer exercises

Contact hours

WebLearn GradSchool

Self-Directed Learning

Self-Directed 6 hour(s) per Week for Full Term

Suggest 8-12 hours time commitment per week (guide only)