Estimation of Heterogeneous Restricted Mean Survival Time Using Random Forest.
Estimation of Heterogeneous Restricted Mean Survival Time Using Random Forest.
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DOI：
10.3389/fgene.2020.587378
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发表时间：
2020
影响因子：
3.7
通讯作者：
Li H
中科院分区：
文献类型：
--
作者：
Liu M;Li H
异质性限制性平均生存时间（hRMST）的估计和预测具有重要的临床意义，它可以在删失和个体协变量存在的情况下提供一个易于解释和具有临床意义的生存函数总结。现有的hRMST建模方法依赖于生存分布的比例风险或其他参数假设。本文针对右删失生存数据，提出了一种基于随机森林的hRMST估计，并证明了该估计的中心极限定理。此外，我们提出了一个计算高效的建设的置信区间的hRMST。我们的模拟结果表明，由此产生的置信区间有正确的覆盖概率的hRMST，和随机森林为基础的估计hRMST有较小的预测误差比参数模型时，模型被错误指定。我们将该方法应用于癌症基因组图谱（TCGA）项目的卵巢癌数据集，以预测hRMST，并显示出比现有方法更好的预测性能。使用R和C++的软件实现srf可在https://github.com/lmy1019/SRF上获得。
Estimation and prediction of heterogeneous restricted mean survival time (hRMST) is of great clinical importance, which can provide an easily interpretable and clinically meaningful summary of the survival function in the presence of censoring and individual covariates. The existing methods for the modeling of hRMST rely on proportional hazards or other parametric assumptions on the survival distribution. In this paper, we propose a random forest based estimation of hRMST for right-censored survival data with covariates and prove a central limit theorem for the resulting estimator. In addition, we present a computationally efficient construction for the confidence interval of hRMST. Our simulations show that the resulting confidence intervals have the correct coverage probability of the hRMST, and the random forest based estimate of hRMST has smaller prediction errors than the parametric models when the models are mis-specified. We apply the method to the ovarian cancer data set from The Cancer Genome Atlas (TCGA) project to predict hRMST and show an improved prediction performance over the existing methods. A software implementation, srf using R and C++, is available at https://github.com/lmy1019/SRF.