Survival Analysis for Clinicians [EL015]

Course highlights

EC points




Course days

Online modules, class days: Mon 01/03, Thu 04/03, Mon 08/03, Thu 11/03, Mon 15/03, Thu 18/03. Assignment deadline 01/04.


Prof. Dimitris Rizopoulos, PhD

Course fee

€ 1180


Erasmus MC, Rotterdam NL




Knowledge of statistics and regression models.


  • Biostatistics


How to apply

Detailed information about this course:


This course will not be offered in 2021.

Survival analysis is the study of the distribution of life times, i.e. the times from an initiating event (birth, diagnosis, start of treatment) to some terminal event (relapse, death). Survival analysis is most prominently (but not only) used in the biomedical sciences. A special feature of survival data is that it takes time to observe the event of interest. A result of this seemingly innocent observation is that for a number of subjects the event is not observed, but instead it is known that it has not taken place yet. This phenomenon is called censoring and it requires special statistical methods. During the course different types of censored and truncated data will be introduced and techniques for estimating the survival function by employing both parametric and non-parametric methods will be illustrated. Also techniques for testing equality of survival functions (the log-rank test and alternatives) are discussed. Finally regression models for survival analysis, based on the hazard function (most notably the Cox proportional hazards model), will be studied in great detail. Special aspects such as time-dependent covariates and stratification will be introduced. Techniques to be used to assess the validity of the proportional hazards regression model will be discussed. The last part of the course touches on models for multivariate survival analysis, including competing risks and multi-state models and frailty models. Finally, aspects of the planning of clinical trials with lifetime data will be discussed.

Teaching methods

All aspects of the course will be illustrated with real data examples and will be practiced. In order to cater to a larger range of participants, this Survival Analysis course has had an update! Starting in 2020, the course has a blended design, which means it includes both online modules and in-class meetings. The first meeting lasts 1 hour, the following meetings last 2 hours. The course is assessed using an assignment.

Survival Analysis (EL015) is equivalent to the Survival Analysis course (ESP28) in the Erasmus Summer Programme and the part about survival analysis in Biostatistical Methods II: classical regression models (CK030).


After completing the course, the student should:Understand the concept of censoring and its implications for statistical analysis;

  • Be familiar with the most important techniques in survival analysis, such as the Kaplan-Meier estimate, the log-rank test and proportional hazards regression;
  • Understand the underlying assumptions and limitations of these techniques;
  • Be able to perform statistical analysis of time-to-event data and interpret the results.

Participant profile

Clinicians who are involved in clinical research with time-to-event data or want to learn the concepts and techniques for the analysis of such data.


Attendance, Assignment(s)

Reduction on fees


No fees are charged for participation of Erasmus MC PhD candidates

Please note that in case of cancellation or no show, the cancellation policy applies based on the full course fee.


25% reduction for all (international) PhD students without formal appointment at Erasmus MC

Upon receipt of your application you will receive a request to upload proof of enrollment as a PhD student.