  Interval-censored data analysis is important in biomedical statistics for any
type of time-to-event response where the time of response is not known exactly,
but rather only known to occur between two assessment times. Many clinical
trials and longitudinal studies generate interval-censored data; one common
example occurs in medical studies that entail periodic follow-up. In this paper
we propose a survival forest method for interval-censored data based on the
conditional inference framework. We describe how this framework can be adapted
to the situation of interval-censored data. We show that the tuning parameters
have a non-negligible effect on the survival forest performance and guidance is
provided on how to tune the parameters in a data-dependent way to improve the
overall performance of the method. Using Monte Carlo simulations we find that
the proposed survival forest is at least as effective as a survival tree method
when the underlying model has a tree structure, performs similarly to an
interval-censored Cox proportional hazards model fit when the true relationship
is linear, and outperforms the survival tree method and Cox model when the true
relationship is nonlinear. We illustrate the application of the method on a
tooth emergence data set.
