CE_10C: Bayesian Joint Models for Longitudinal and Survival Data, with Applications in R (Added Fee)

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). 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. 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.