Piecewise exponential survival trees with time-dependent covariates
Piecewise exponential survival trees with time-dependent covariates
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DOI：
10.2307/2533668
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发表时间：
1998-12-01
期刊：
影响因子：
1.9
通讯作者：
Soong, SJ
中科院分区：
文献类型：
--
作者：
Huang, X;Chen, SD;Soong, SJ
生存树方法是生存分析中半参数考克斯回归的非参数替代方法。本文提出了一种基于树的协变量时变截尾生存数据的估计方法。所提出的方法假设一个非常一般的模型的风险函数，是完全非参数。递归分割算法使用似然估计过程在分段指数结构下生长树，该结构以与时间无关的协变量并行的方式处理时间相关的协变量。一般来说，节点处的估计危险给出了特定时间段内一组个体的风险。在树选择过程中，实现了交叉验证和自举响应技术。通过对真实的数据的仿真和应用，表明该方法具有良好的性能。
Survival trees methods are nonparametric alternatives to the semiparametric Cox regression in survival analysis. In this paper, a tree-based method for censored survival data with time-dependent covariates is proposed. The proposed method assumes a very general model for the hazard function and is fully nonparametric. The recursive partitioning algorithm uses the likelihood estimation procedure to grow trees under a piecewise exponential structure that handles time-dependent covariates in a parallel way to time-independent covariates. In general, the estimated hazard at a node gives the risk for a group of individuals during a specific time period. Both cross-validation and bootstrap resampling techniques are implemented in the tree selection procedure. The performance of the proposed survival trees method is shown to be good through simulation and application to real data.