A K-nearest neighbors survival probability prediction method
A K-nearest neighbors survival probability prediction method
复制标题
DOI：
10.1002/sim.5673
复制
发表时间：
2013-05-30
影响因子：
2
通讯作者：
Zenios, S. A.
中科院分区：
文献类型：
--
作者：
Lowsky, D. J.;Ding, Y.;Zenios, S. A.
本文介绍了一种右删失数据的非参数生存预测方法。该方法通过使用K个最相似的训练观察结果构建（加权）KaplanMeier估计量来生成生存曲线预测。每个观测都有一组相关的协变量，协变量空间上的度量用于测量观测之间的相似性。我们将我们的方法应用于肾移植数据集，以生成移植物存活率的患者特异性分布，并应用于明确违反比例风险假设的模拟数据集。我们比较了我们的方法与标准的考克斯模型和随机生存森林方法的性能。版权所有（c）2012约翰威利父子有限公司
We introduce a nonparametric survival prediction method for right-censored data. The method generates a survival curve prediction by constructing a (weighted) KaplanMeier estimator using the outcomes of the K most similar training observations. Each observation has an associated set of covariates, and a metric on the covariate space is used to measure similarity between observations. We apply our method to a kidney transplantation data set to generate patient-specific distributions of graft survival and to a simulated data set in which the proportional hazards assumption is explicitly violated. We compare the performance of our method with the standard Cox model and the random survival forests method. Copyright (c) 2012 John Wiley & Sons, Ltd.