Joint Models for Longitudinal and Survival Data [ESP72]

Course highlights

EC points


Start date


End date


Course days

Monday to Friday (5 mornings)


Prof. Dimitris Rizopoulos, PhD

Course fee

€ 490


Erasmus MC, Rotterdam NL




This course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood and regression models. Also, a basic knowledge of R would be beneficial but is not required.


  • Biostatistics


Go to the ESP website

Detailed information about this course:


Faculty: Prof. Dimitris Rizopoulos, PhD

In follow-up studies, different types of outcomes are collected for each subject. These include longitudinally measured responses (e.g., biomarkers) and the time until an event of interest occurs (e.g., disease onset or death). These outcomes are often separately analyzed, but on many occasions, it is of scientific interest to study their association. This research question has given rise to the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First when the focus is on a survival outcome, and we wish to account for the effect of endogenous time-dependent covariates measured with error, and second when the focus is on the longitudinal outcome, and we wish to correct for non-random dropout.

The course is explanatory rather than mathematically rigorous. Therefore emphasis is given in sufficient detail for participants to obtain a clear view of the different joint modeling approaches and how they should be used in practice. To this end, the course features a number of computer practicals in R showcasing the use of these models.


The course will explain which joint models can be used depending on the research questions to be answered and which model-building strategies are currently available.

Participants will be able to:

  • construct and fit an appropriate joint model in R,
  • correctly interpret the obtained results, and
  • extract additional useful information (e.g., plots) to communicate the results.

Participant profile

Professional statisticians, clinical researchers, clinical epidemiologists, decision scientists, public health researchers working in applied environments where hierarchical modeling and survival analysis are key issues.



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.

50% reduction on our fees for participating institutes of NIHES

This 50% reduction is offered to participants formally appointed at the departments or sections participating in NIHES.

University Medical Center Rotterdam/Erasmus University Rotterdam:

  • Department of Epidemiology
  • Department of Public Health
  • Department of Psychiatry, Section of Medical Psychology and Psychotherapy
  • Department of Child and Adolescent Psychiatry/Psychology
  • Department of Medical Informatics
  • Department of General Practice
  • Erasmus School of Health Policy & Management
  • Generation R

50% reduction on our fees for NIHES affiliated partners

This 50% reduction is offered to participants formally appointed at the following NIHES affiliated partners:

University Medical Center Rotterdam/Erasmus University Rotterdam, departments of:

  • Dermatology
  • General Paediatrics
  • Plastic and Reconstructive Surgery
  • Rehabilitation Medicine

UMIT, Department of Public Health, Health Services Research and Health Technology Assessment.

25% reduction on our fees for Erasmus MC employees

For participants formally appointed at all departments of Erasmus MC (except the departments mentioned above and Erasmus MC PhD candidates).

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.