Survival analysis of right-censored data often arises in many areas of research including medical research. The effect of covariates (and their interactions) on survival distribution can be studied through existing methods that require pre-specifying the functional form of the covariates including their interactions. Survival trees offer a relatively flexible approach when the form of covariates' effects is unknown. We have proposed the SurvCART algorithm to construct a survival tree. There are two features that distinguish the SurvCART algorithm from the rest. First, most of the currently available survival tree construction techniques are not based on a formal test of significance and, hence, prone to spurious findings. The proposed SurvCART algorithm utilizes the “conditional inference” framework that selects splitting variable via parameter instability test and subsequently finds the optimal split based on some maximally chosen statistic. We used likelihood score-based parameter instability tests that converge to distribution with known distribution function so that the p-value can be obtained easily without any approximation. Second, the SurvCART algorithm has the flexibility to extend the concept of heterogeneity to the censoring time distribution as well, a feature that can be useful when censoring distribution is influenced by baseline covariates. We evaluated the operating characteristics of the parameter instability test and compared the performance of the SurvCART algorithm with other survival tree algorithms via simulation. Finally, the SurvCART algorithm was applied to a real data setting. The proposed method is implemented in R package LongCART available on CRAN.

中文翻译：

---

基于事件时间异质性的生存树和使用参数不稳定性测试审查分布

右删失数据的生存分析经常出现在包括医学研究在内的许多研究领域。可以通过需要预先指定协变量的函数形式（包括它们的相互作用）的现有方法来研究协变量（及其相互作用）对生存分布的影响。当协变量的影响形式未知时，生存树提供了一种相对灵活的方法。我们提出了 SurvCART 算法来构建生存树。有两个特征将 SurvCART 算法与其他算法区分开来。首先，大多数当前可用的生存树构建技术都不是基于正式的显着性测试，因此容易出现虚假结果。提出的 SurvCART 算法利用“条件推理”框架，通过参数不稳定性测试选择分裂变量，然后根据一些最大选择的统计量找到最佳分裂。我们使用了基于似然分数的参数不稳定性测试，这些测试收敛到具有已知分布函数的分布，以便无需任何近似值即可轻松获得p值。其次，SurvCART 算法还可以灵活地将异质性的概念扩展到审查时间分布，当审查分布受基线协变量影响时，这一特征非常有用。我们评估了参数不稳定测试的运行特性，并通过仿真比较了 SurvCART 算法与其他生存树算法的性能。最后，将 SurvCART 算法应用于实际数据设置。所提出的方法在 CRAN 上可用的 R 包 LongCART 中实现。