Full course description
Date: 9th July, 2026
Modality: In-person
Duration: One day, 9:00am-5:00pm
Price: $550 (incl. GST)
This course explains how survival data (time-to-event data) is different from other commonly collected data types in health research. Many aspects of data analysis are very similar for survival data and binary or continuous data, but there are some important challenges.
Participants will learn about censoring and truncation of time-to-event data, competing risks and recurrent events. They will do exploratory and inferential data analyses, and fit regression models to the data, including models with time-varying predictors.
Course Structure and topics overed:
The single-day course consists of four sessions. The first three sessions then have exercises for the students and a discussion of the exercises.
Topics covered: censoring and truncation, competing risks, recurrent events; exploratory data analysis; the Cox proportional hazards model.
Learning Outcomes:
Students should be able to:
- Explain censoring and truncation and their importance in time-to-event analysis
- Recognise when interval censoring is likely to cause problems
- Fit Cox proportional hazards models in Stata or R
- Arrange data for time-varying predictors
- Explain the advantages and disadvantages of time-varying predictors
- describe why competing risks cause problems in analysis
Who should attend?
Participants should have basic clinical or epidemiology-based training in statistics, plus the ability to fit a logistic regression model in R or Stata and a laptop with suitable software. They should have an interest in carrying out quantitative health research.
Payment for short courses is normally by credit card. If you need to pay by invoice or require assistance, please email shortcourses@auckland.ac.nz. Please note the University does not issue individual invoices for amounts less than $500 New Zealand dollars.
By enrolling in a Short Course at the University of Auckland you agree to the Short Course Terms and Conditions