Introduction to Survival Analysis: Online - (2 days)

Survival analysis is used to find out the time it takes for an event of interest to occur (e.g., death, birth, recidivism).


This course is designed as an introduction to survival analysis.



This course will be offered online via Zoom And will run to the following timetable:

  • 10.30am - 11.30am: Instructional session
  • 12.30pm - 2.00pm: Instructional session
  • 3.00pm - 4.30pm: Exercises and discussion


Please note: Courses will run on Australian Eastern Standard Time (GMT +10)



Workshop - runs over 2 days

Dr Joanna Dipnall is an applied statistician with interests in the advanced statistical methods, including machine learning and deep learning techniques. She completed her Honours in Econometrics with Monash University and her PhD with IMPACT SRC, School of Medicine, Deakin University. Joanna works extensively with registry and linked medical data and collaborates extensively with the Faculty of IT at Monash to supervise Masters and PhD students to integrate artificial intelligence within health research. Joanna teaches within the Monash Biostatistics Unit and is the Unit Co-coordinator for the Monash Masters of Health Data Analytics course. Joanna has taught advanced statistical methods for many years at universities and for ACSPRI.

About this course: 

Survival analysis is used to find out the time it takes for an event of interest to occur (e.g., death, birth, recidivism). It is used in a variety of fields such as cancer studies to investigate time to death, criminology studies to investigate the time until re-offending, engineering studies for “failure-time analysis” or time until a product fails.

Survival analysis can be used to determine the probability that a participant survives to a set number of years; to evaluate if there are differences in survival between key groups (e.g., drug versus placebo); or even what factors affect participants' chances of survival.

Competing risks analysis is a form of survival analysis that takes into account competing events that may influence survival rates (e.g., death).

Discussion of some of the uses of survival analysis in publications will be discussed at the end of the course.


Course syllabus: 

This course is broken up into the following sections:


Part I: Overview of Survival Analysis
Part II: Kaplan Meyer Curves and Log Rank Tests
Part III: Cox Proportional Hazard Regression
Part IV: Competing Risks Regression
Part V: Survival Analysis in Publications


Participants will be given time to do some exercises on their own to practise what they have learned.

Exercises and solutions will be provided in Stata, R and Python software.



Course format: 

This workshop will take place online using Zoom.

You will need your own computer with Stata, or Python and/or R installed, and an internet connection.

A second screen/monitor is recommended.

Recommended Background: 

This course assumes that participants have:


(1) Sound familiarity with at least one of the three software packages Stata, R and/or Python.

(2) sufficient understanding of statistics to be able to comprehend the material covered in the course outline, such as a basic grounding in multiple regression (e.g., linear, logistic, Poisson).

(3) access to either Stata, R and/or Python.

(4) some experience in using Microsoft Word and Excel or their equivalent.

(5) experience using a text editor such as Notepad.


Recommended Texts: 

Course notes will be supplied. Please include a shipping address when you enrol. Your notes will be express posted to this address.


No specific references are suggested but a number will be supplied with the notes handed out for the course.