Interval Censored Recursive Forests

We propose interval censored recursive forests (ICRF), an iterative tree ensemble method for interval censored survival data. This nonparametric regression estimator addresses the splitting bias problem of existing tree-based methods and iteratively updates survival estimates in a self-consistent manner. Consistent splitting rules are developed for interval censored data, convergence is monitored using out-of-bag samples, and kernel-smoothing is applied. The ICRF is uniformly consistent and displays high prediction accuracy in both simulations and applications to avalanche and national mortality data. An R package icrf is available on CRAN. Supplementary files for this article are available online.

Automated summary

ICRF is an iterative tree ensemble method designed for interval censored survival data, addressing splitting bias and updating survival estimates in a self-consistent manner. The method shows high prediction accuracy and uniform consistency in simulations and applications to avalanche and national mortality data.