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Interval-censored data analysis is important in biomedical statistics for anytype 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 clinicaltrials and longitudinal studies generate interval-censored data; one commonexample occurs in medical studies that entail periodic follow-up. In this paperwe propose a survival forest method for interval-censored data based on theconditional inference framework. We describe how this framework can be adaptedto the situation of interval-censored data. We show that the tuning parametershave a non-negligible effect on the survival forest performance and guidance isprovided on how to tune the parameters in a data-dependent way to improve theoverall performance of the method. Using Monte Carlo simulations we find thatthe proposed survival forest is at least as effective as a survival tree methodwhen the underlying model has a tree structure, performs similarly to aninterval-censored Cox proportional hazards model fit when the true relationshipis linear, and outperforms the survival tree method and Cox model when the truerelationship is nonlinear. We illustrate the application of the method on atooth emergence data set.

An Ensemble Method for Interval-Censored Time-to-Event Data