Joint models for longitudinal and time-to-event data are often employed to
calculate dynamic individualized predictions used in numerous applications of
precision medicine. Two components of joint models that influence the accuracy
of these predictions are the shape of the longitudinal trajectories and the
functional form linking the longitudinal outcome history to the hazard of the
event. Finding a single well-specified model that produces accurate predictions
for all subjects and follow-up times can be challenging, especially when
considering multiple longitudinal outcomes. In this work, we use the concept of
super learning and avoid selecting a single model. In particular, we specify a
weighted combination of the dynamic predictions calculated from a library of
joint models with different specifications. The weights are selected to
optimize a predictive accuracy metric using V-fold cross-validation. We use as
predictive accuracy measures the expected quadratic prediction error and the
expected predictive cross-entropy. In a simulation study, we found that the
super learning approach produces results very similar to the Oracle model,
which was the model with the best performance in the test datasets. All
proposed methodology is implemented in the freely available R package JMbayes2