TREE-STRUCTURED PREDICTION FOR CENSORED SURVIVAL-DATA AND THE COX MODEL
TREE-STRUCTURED PREDICTION FOR CENSORED SURVIVAL-DATA AND THE COX MODEL
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DOI：
10.1016/0895-4356(94)00164-l
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发表时间：
1995-05-01
影响因子：
7.2
通讯作者：
LOU, ZY
中科院分区：
文献类型：
--
作者：
CIAMPI, A;NEGASSA, A;LOU, ZY
预测树的生存数据的分析进行了讨论。结果表明，树是有用的，不仅在总结中包含的一组协变量（预后分类）的预后信息，但也在检测和显示治疗协变量的相互作用（亚组分析）。的RECPAM方法树生长的概述，预后分类和亚组分析，然后制定的RECPAM框架内，并与先验层的考克斯比例风险模型的基础上。两个数据分析的例子。交叉验证的问题进行了讨论，在计算上更便宜的模型选择标准。
Prediction trees for the analysis of survival data are discussed. It is shown that trees are useful not only in summarizing the prognostic information contained in a set of covariates (prognostic classification), but also in detecting and displaying treatment-covariates interactions (subgroup analysis). The RECPAM approach to tree-growing is outlined; prognostic classification and subgroup analysis are then formulated within the RECPAM framework and on the basis of the Cox proportional hazards models with apriori strata. Two examples of data analysis are presented. The issue of cross-validation is discussed in relation to computationally cheaper model selection criteria.