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.

Objectives

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.

Assessment