  Predicting the individual risk of a clinical event using the complete patient
history is still a major challenge for personalized medicine. Among the methods
developed to compute individual dynamic predictions, the joint models have the
assets of using all the available information while accounting for dropout.
However, they are restricted to a very small number of longitudinal predictors.
Our objective was to propose an innovative alternative solution to predict an
event probability using a possibly large number of longitudinal predictors. We
developed DynForest, an extension of competing-risk random survival forests
that handles endogenous longitudinal predictors. At each node of the tree, the
time-dependent predictors are translated into time-fixed features (using mixed
models) to be used as candidates for splitting the subjects into two subgroups.
The individual event probability is estimated in each tree by the
Aalen-Johansen estimator of the leaf in which the subject is classified
according to his/her history of predictors. The final individual prediction is
given by the average of the tree-specific individual event probabilities. We
carried out a simulation study to demonstrate the performances of DynForest
both in a small dimensional context (in comparison with joint models) and in a
large dimensional context (in comparison with a regression calibration method
that ignores informative dropout). We also applied DynForest to (i) predict the
individual probability of dementia in the elderly according to repeated
measures of cognitive, functional, vascular and neuro-degeneration markers, and
(ii) quantify the importance of each type of markers for the prediction of
dementia. Implemented in the R package DynForest, our methodology provides a
novel and appropriate solution for the prediction of events from any number of
longitudinal endogenous predictors.
