Module 6: Survival Analysis and Competing Risks

Type of Course - Dates - Venue - Description - Target audience - Exam - IMPORTANT: Incorporation in DTP and reimbursement by DS
Course prerequisites - Teacher - Course material - Fees - Enrol

Type of course

Due to the safety restrictions issued by UGent concerning the corona crisis this is an online course.


Two and a half days in February 2021: February 1 and 2, from 9 am to 12.45 pm and from 2 pm to 5 pm, February 3, from 9 am to 12.45 pm.
Please note: the deadline for UGent PhD students who want a refund to open a dossier on the DS website (Application for Recognition) is December 24, 2020.


This course will take place online.


Time-to-event data are abundant in many fields: time to death or relapse in a medical setting, time to graduation or recidivism in social sciences or time to breakdown of machine parts in an industrial setting. Clearly, the actual time to an event can only be observed for those who already experienced the event of interest: your refrigerator broke down 2.5 years after purchase, while your oven is still operational after 8 years. For the latter, we only know the time to breakdown is more than 8 years.

Where standard regression techniques mishandle the information from these censored observations, survival analysis efficiently uses all available information in a correct way, given certain assumptions.

This course first covers non-parametric estimation (Kaplan Meier curves) and comparison (logrank-tests) of survival curves. Next, it introduces modeling of survival data with semi-parametric Cox Proportional Hazard models. This is extended to situations with competing events of interest (e.g. death due to cancer vs death due to other causes). The final part presents sample size calculations for trials with a time-to-event outcome.

Theory classes are alternated with practical sessions including worked examples in R.

Target audience

This course targets professionals and investigators working with time-to-event data in any setting.


Participants can, if they wish, take part in an exam. Upon succeeding in this test a certificate from Ghent University will be issued.

Please note: For UGent PhD students it is no longer necessary to participate/succeed in this exam to be able to incorporate the course in the DTP.

Incorporation in DTP and reimbursement from DS for UGent PhD students

As a UGent PhD student, to be able to incorporate this 'specialist course' in your Doctoral Training Program (DTP) and get a reimbursement of the registration fee from your Doctoral School (DS) you need to follow strict rules: please take the necessary action in time. The deadline to open a dossier on the DS website (Application for Recognition) for this course is December 24, 2020. Please note that opening a dossier does not mean that you are enrolled. You still need to enrol via the registration form on this site.

Course prerequisites

Participants are expected to have an active knowledge of regression analysis and basic statistics.


Foto Dries ReyndersDries Reynders studied Physics and Statistical Data Analysis at Ghent University.

He is an experienced teacher and is, in that role, well trained in explaining the link between mathematics and the reality it describes. Currently, he works as statistical consultant for the Stat-Gent consortium.

Course material

Slides used during this course will be made available.


A different price applies, depending on your main type of employment.

Employment Module 6 Exam
Industry/Private sector1 925 30
Non-profit, government outside AUGent2 785 30
(Doctoral)student outside AUGent2 355 30

1 If three or more employees from the same company enrol simultaneously for this course a reduction of 10% on the module price is taken into account.

2 AUGent staff and AUGent doctoral students who pay through use of an SAP internal order/invoice can participate at these special rates.

Enrol for this course