Abstract

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.

摘要

我们提出区间删失递归森林 (ICRF)，这是一种用于区间删失生存数据的迭代树集成方法。这种非参数回归估计器解决了现有基于树的方法的分裂偏差问题，并以自洽的方式迭代更新生存估计。为区间删失数据制定一致的分裂规则，使用袋外样本监测收敛，并应用核平滑。ICRF 在雪崩和国家死亡率数据的模拟和应用中始终一致并显示出高预测准确性。CRAN 上提供了一个 R 包 icrf。本文的补充文件可在线获取。