  With the availability of massive amounts of data from electronic health
records and registry databases, incorporating time-varying patient information
to improve risk prediction has attracted great attention. To exploit the
growing amount of predictor information over time, we develop a unified
framework for landmark prediction using survival tree ensembles, where an
updated prediction can be performed when new information becomes available.
Compared to conventional landmark prediction with fixed landmark times, our
methods allow the landmark times to be subject-specific and triggered by an
intermediate clinical event. Moreover, the nonparametric approach circumvents
the thorny issue of model incompatibility at different landmark times. In our
framework, both the longitudinal predictors and the event time outcome are
subject to right censoring, and thus existing tree-based approaches cannot be
directly applied. To tackle the analytical challenges, we propose a
risk-set-based ensemble procedure by averaging martingale estimating equations
from individual trees. Extensive simulation studies are conducted to evaluate
the performance of our methods. The methods are applied to the Cystic Fibrosis
Patient Registry (CFFPR) data to perform dynamic prediction of lung disease in
cystic fibrosis patients and to identify important prognosis factors.
