20 Jan Survival Analysis in R – ONLINE
9:30 am - 5:00 pm
9:30 am - 5:00 pm
1:00 pm - 4:00 pm
statistics course | level: advanced |
for questions related to this event, contact email@example.com
affiliation: KU Leuven
Survival analysis is used for research on time to event occurrences in clinical and biomedical research, behavioral sciences, and other fields. The prototypical event is death, which accounts for the name given to these methods. But survival analysis is also appropriate for many other kinds of events, such as criminal recidivism, divorce, child-bearing, unemployment, and graduation from school. Synonyms for survival analysis are event-history-analysis and duration analysis.
This 3-day course aims at introducing participants to the basic concepts in survival analysis in a very practice driven manner. Methods sessions are followed by hands-on sessions with practical, real life examples taken from primarily medicine and public health, but also demography, social sciences and criminology.
The course is aimed at applied researchers and graduate students, and will provide a comprehensive introduction into this inference framework. In particular, we will explain when these methods should be used in practice, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data. Emphasis will be given on applications, and after the end of the course participants will be able to define appropriate survival models to answer their questions of interest.
In terms of software, we will use R and illustrate how these methods can be used using various packages. All necessary computer code will be provided during the computer lab sessions.
Day 1 morning:
Time-to-event variable, censoring, survival distribution, hazard function.
Computer Lab Session 1a.
Day 1 afternoon:
The Cox proportional hazards model, goodness-of-fit methods.
Computer Lab Session 1b.
Introduction and approaches to and estimation of competing risks.
Computer Lab Session 2.
Day 3 afternoon:
Introduction to dynamic prediction in survival analysis.
Computer Lab Session 3.
If you need to analyze longitudinal event data and you have a basic statistical background, this course is for
Tuesday 27 April, 2021 09:30-17:00
Wednesday 28 April, 2021 09:30-17:00
Thursday 29 April, 2021 13:00-16:00
To take this course, you should have a good working knowledge of the principles and practice of multiple linear regression, as well as elementary statistical inference.
We will use R/Rstudio packages to demonstrate implementation of certain methods; thus, some familiarity with R is desirable.
- “Event History Analysis with R” (2012), G. Brostrom, Chapman and Hall/CRC. ISBN 978-1-4398-3164-9.
- “Data Analysis with Competing risks and Intermediate States” (2016) R.B. Geskus, Chapman and Hall/CRC. ISBN978-1-4665-7035-1.
- “The Statistical Analysis of Failure Time Data” (2002) J. D. Kalbfleisch, R. L. Prentice , Wiley. ISBN: 978-0-471-36357-6.
- “Modelling Survival Data in Medical Research”, D. Collett (2014) Chapman and Hall/CRC. ISBN 978-1-439-85678-9.
- “Dynamic prediction in Clinical Survival Analysis” (2012), H.C. van Houwelingen and H. Putter. Chapman andHall/CRC, ISBN 978-1-4398-3533-3.
PhDs and postdocs of a Flemish University: free
Other academics: €150
Non-profit/social sector: €250
Private sector: €500
The course will be delivered ONLINE.
dr. Ruth Nysen and dr. Dries Reynders