An introduction to the Joint Modelling of Longitudinal and Survival Data, with Applications in R, 14 to 16 October 2019

Posted on Mon, Aug 05 2019 10:42:00

Short course information

This intensive three-day course, registered as a University of Stellenbosch Short Course, will be presented at Stellenbosch under the auspices of the South African DST-NRF Centre for Epidemiological Modelling and Analysis (SACEMA). The course will take place at the Stellenbosch Institute for Advanced Study (StIAS), from 9 am to 5 pm daily. The course will be presented Prof. Dr. Dimitris Rizopoulos, of the Department of Biostatistics, Erasmus University Medical Center, the Netherlands.

The deadline for registration is 31 September 2019.

For participants within South Africa, the course fee is R6000 for early bird registration (payment made by 31 August) and R7000 for late registration (by 31 September 2019). For international participants the fee is €500 for early bird registration, and €600 for late registration. (Note: Full payment must be processed prior to start of the course.)

The costs of accommodation, breakfast, and dinner are not included. Short-term accommodation is in high demand, so best to book early. Useful websites include AirbnbTripAdvisor, Sleeping out and

For enquiries contact The Training Coordinator, Masimba Paradza:

For course flyer, click here.


Objectives information

In follow-up studies, different types of outcomes are typically collected for each subject. These include longitudinally measured responses (e.g., biomarkers), and the time until an event of interest occurs (e.g., death, dropout). Often these outcomes are separately analyzed, but on many occasions, it is of scientific interest to study their association.

This type of research question has given rise in 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 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 focus is on the longitudinal outcome and we wish to correct for non-random dropout.

This course is aimed at applied researchers and graduate students and will provide a comprehensive introduction to this modeling framework. We will explain when these models should be used in practice, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data. Emphasis is given on applications, and after the end of the course, participants will be able to define appropriate joint models to answer their questions of interest.

Outcomes information

After this course participants should be able to identify settings in which a joint modeling approach is required. From the course, it will become clear which joint models can be used depending on the actual research questions to be answered, and which model-building strategies are currently available. Further, participants should be able to construct and fit an appropriate joint model, correctly interpret the obtained results, and extract additional useful information (e.g., plots) that can help communicate the results better.

The course will be explanatory rather than mathematically rigorous. Therefore emphasis is given in sufficient detail for participants to obtain a clear view on the different joint modeling approaches, and how they should be used in practice. To this end, we first motivate joint modeling using real datasets and then illustrate in detail the virtues and drawbacks of each of the presented joint modeling approaches. For completeness and throughout the course, references are provided to material with more technical information.

Presentation methods information

Schedule :

Introduction & Motivation: Which type of research questions requires joint modeling

Review of Mixed Models: Definitions, linear mixed model estimation, how to fit in R

Review of Mixed Models: Missing data in follow-up studies, missing data mechanisms

Review of Relative Risk Models: Definitions, Cox model, estimation, time-dependent covariates, extended Cox model

The Basic Joint Model: Definition of joint models, assumptions, estimation, comparison with time-dependent Cox model, connection with missing data

Extensions of the Basic Joint Model: Functional form, Multiple longitudinal outcomes, multiple failure times

Special topics: Dynamic predictions for the survival and longitudinal outcomes


Dr. Dimitris Rizopoulos is a professor of biostatistics at the Erasmus Medical Center Rotterdam. His research focuses on joint models for longitudinal and time-to-event data with applications in biomarker identification, precision medicine, screening and active surveillance. He currently serve as a co-Editor for Biostatistics..