Introduction to Survival/Time-to-Event Data Analysis
27 June 2022–28 June 2022, 9:30 am–1:00 pm
This course introduces the concept of modelling time-to event data, commonly known as survival analysis. Log-rank tests and Cox proportional hazard regression models are used to determine associations between different factors and the event occurring.
Centre for Applied Statistics Courses
Sign up to General Mailing List NOTE: Due to the coronavirus outbreak, all courses will now be delivered online through a live video feed. You can expect the same level of group and individual support as you would have received in our face-to-face courses.
Survival/time-to-event analysis is appropriate when the outcome of interest is an event and that event has not occurred for everyone in the dataset. The outcome can be something negative (for example death, recurrence of tumour) or something positive (for example, recovery, task completion).
The simplest analysis is the log-rank test that assesses differences according to a single factor. Cox proportional hazards regression is appropriate to investigate the rate at which the event occurs according to several potential predictors.
This course gives an introduction to time-to-event (survival) data for non-statisticians, and covers the following topics:
- Use of Kaplan-Meier
- Life tables
- Cox regression analyses
- Hazard ratios
- How to set the data up for analysis
- Including interaction terms in the models
- Assessing model interpretation of SPSS output
SPSS outputs are given and we consider how to interpret these to determine the best model and to assess goodness-of-fit. The course will be of use to users of alternative statistical packages too as the concepts discussed throughout the course are generally applicable. The dataset may be taken away and analysed within other packages.
Note that this course does not involve hands on use of a stats package, we will consider only pre-prepared printouts.
External Delegates (Non-UCL) £150.00 UCL Staff, Students, Alumni £75.00 * Staff and Doctoral Students from ICH/GOSH FREE †
* Valid UCL email address and/or UCL alumni number required upon registration. Please note, this category does not include hospital staff unless you hold an official contract with the university.
† Limited free spaces available. If there are no free places remaining, Staff and Doctoral Students from ICH/GOSH can still register at the UCL rate.
Finally, please note that no refunds will be given for non-attendance or cancellations made within 5 working days of the start of the course. For delegates attending courses on funded places, a £50 fee will be charged for late cancellation, non-attendance or partial-attendance.
- Future Dates
Dates Time Apply TBC 9.30am - 1.00pm Sign up to the general mailing list
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"Really enjoyed the course. It was very useful. The presentation was excellent. Very clear. Good pace. Allowed a good amount of time for questions and clarifying issues. Also enjoyed the practicals. Felt the course length was appropriate. Probably my favourite course out of all the ones I have done (and I have done a lot!)"
"Presentation was very well done. Student participation was spot on. Overall thank you for a very good course. I would recommend it to anyone interested in the subject"
"Overall a very good course. The quality of the teaching staff was high and they came across very friendly and open to questions. It was a good refresher course to me."
"Thank you. This was the greatest and clearest statistics course/lecture I have ever attended."
"I thought the content, presentation and detail of the course was of a high quality. The facilitator was very knowledge in the area and was able to target the course at a level that was suitable for an Introductory course. I would highly recommend this course to others."
"I found this course really useful. I really enjoyed the fact that it focused a lot on interpreting the results from other studies, rather than just how to do the analysis."
"Great course, well presented, the mini questions sections really helped solidify the content."