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

Combining Dynamic Predictions from Joint Models for Longitudinal and Time-to-Event Data using Bayesian Model Averaging