Competing Risks and Multi-State Models [EL001]

Course highlights

EC points


Start date


End date



Prof. dr. Aurélien Latouche

Course fee

€ 665


Erasmus MC, Rotterdam NL




Participants should have working knowledge of survival analysis and not feel uncomfortable with mathematical formulas. Knowledge of R is highly recommended.


  • Biostatistics


How to apply

Detailed information about this course:


Competing risks and multi-state models play an increasingly important role in the analysis of time to event data. Regarding competing risks, there is a lot of confusion regarding the proper analysis. The most important reason for the confusion is conceptual: which quantities can be estimated and what do they represent. Once the concepts are understood and the proper type of analysis has been chosen, most analyses are straightforward and can be performed with standard software for survival analysis. For multi-state models with exactly observed transition times, estimation is reasonably straightforward and the real challenge is in (dynamic) prediction.

The overarching goal of the course is to provide a solid introduction to these topics and thereby increase the analytical validity in this field.

In the first part of the course we cover competing risks analysis: what are competing risks and when do we need to take them into account; the independence assumption; cause-specific cumulative incidence; cause-specific hazard and subdistribution hazard; competing risks as a multi-state model. We will also cover regression models on both cause-specific and subdistribution hazard (Fine-Gray model) and discuss the difference in interpretation. We show how analyses can be performed with standard software. In the second part of the course, the extension to multi-state models is discussed. The course will cover topics including transition intensities and transition probabilities, nonparametric estimation and regression models, as well as methods to obtain predictions of future events, given the event history and clinical characteristics of a patient. With right censored and/or left truncated data, we show that it is possible to perform many types of analyses using standard software, using the same techniques as in multi-state representation of the competing risks model.

See 'how to apply' for the course registration period.


After successfully completing the course, the student will be able to:

  • Describe the quantities that can be estimated in a competing risks framework, how they are interpreted, and the assumptions that must be made.
  • Understand the relationship between the cause-specific hazard and cumulative incidence and describe why it is no longer one-to-one in a competing risks framework.
  • Propose a suitable statistical model for assessing a specific research hypothesis in a competing risks situation, fit the model using standard statistical software, and interpret the results.
  • Recognise when a multi-state model is appropriate, fit a multi-state model using standard statistical software, evaluate the fit of the model, and interpret the results.

Participant profile

Epidemiologists and clinical researchers. Participants should have working knowledge of survival analysis and not feel uncomfortable with mathematical formulas.
Knowledge of R is highly recommended.



Reduction on fees

PLEASE NOTE: This does not apply to the fee of the research master programmes (120 EC points)

No fees are charged for  Erasmus MC PhD candidates, provided they have an account in Hora Finita, the Erasmus University PhD registration system. When submitting the application, you can state that someone else pays your tuition fee.

In case of cancellation or no show, the cancellation policy applies based on the full course fee.


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

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