Trees and splines in survival analysis.
Trees and splines in survival analysis.
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DOI：
10.1177/096228029500400305
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发表时间：
1995-09-01
影响因子：
2.3
通讯作者：
Kooperberg, C
中科院分区：
文献类型：
--
作者：
Intrator, O;Kooperberg, C
在过去的几年中，几个非参数替代考克斯比例风险模型出现在文献中。这些方法扩展了从回归分析到删失生存数据分析的公知技术。在本文中，我们讨论了基于（划分）树和（多项式）样条的方法，分析了两个数据集使用生存树和HARE，并比较了这两种方法的优缺点。HARE的优势之一是其模型拟合程序对潜在风险模型的比例性进行了隐式检查。它还提供了一个显式模型的条件风险函数，这使得它非常方便地获得图形摘要。另一方面，基于树的方法自动将数据集划分为生存历史相似的病例组。生存树和HARE得到的结果往往是互补的。生存分析中的树和样条函数应该为数据分析师提供两个有用的工具来分析生存数据。
During the past few years several nonparametric alternatives to the Cox proportional hazards model have appeared in the literature. These methods extend techniques that are well known from regression analysis to the analysis of censored survival data. In this paper we discuss methods based on (partition) trees and (polynomial) splines, analyse two datasets using both Survival Trees and HARE, and compare the strengths and weaknesses of the two methods. One of the strengths of HARE is that its model fitting procedure has an implicit check for proportionality of the underlying hazards model. It also provides an explicit model for the conditional hazards function, which makes it very convenient to obtain graphical summaries. On the other hand, the tree-based methods automatically partition a dataset into groups of cases that are similar in survival history. Results obtained by survival trees and HARE are often complementary. Trees and splines in survival analysis should provide the data analyst with two useful tools when analysing survival data.