The joint modeling of longitudinal and time-to-event data is an active area
of statistics research that has received a lot of attention in the recent
years. More recently, a new and attractive application of this type of models
has been to obtain individualized predictions of survival probabilities and/or
of future longitudinal responses. The advantageous feature of these predictions
is that they are dynamically updated as extra longitudinal responses are
collected for the subjects of interest, providing real time risk assessment
using all recorded information. The aim of this paper is two-fold. First, to
highlight the importance of modeling the association structure between the
longitudinal and event time responses that can greatly influence the derived
predictions, and second, to illustrate how we can improve the accuracy of the
derived predictions by suitably combining joint models with different
association structures. The second goal is achieved using Bayesian model
averaging, which, in this setting, has the very intriguing feature that the
model weights are not fixed but they are rather subject- and time-dependent,
implying that at different follow-up times predictions for the same subject may
be based on different models