Joint Models for Longitudinal and Survival Data [ESP72]
Monday to Friday (5 mornings)
Prof. Dimitris Rizopoulos, PhD
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.
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.
//Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//
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.
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
PLEASE NOTE THIS DOES NOT APPLY TO THE RESEARCH MASTERS (120 EC points)
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:
- 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.