论文标题
回归树的累积发病率功能
Regression Trees for Cumulative Incidence Functions
论文作者
论文摘要
在过去的十年中，使用累积发生率功能来表征某种类型的事件的风险已变得越来越流行。使用参数，非参数和半参数方法处理了建模，估计和推理的问题。努力开发机器学习方法的合适扩展，例如回归树和相关的集成方法，才刚刚开始。在本文中，我们开发了一种新的方法来构建回归树，以估算竞争风险设置中的累积发生率曲线。所提出的方法采用增强的Brier评分风险估计量作为建筑和修剪树木的主要基础。使用R统计软件包可以轻松实现所提出的方法。模拟研究表明，我们在竞争风险设置中的方法的实用性。来自辐射疗法肿瘤学组（试验9410）的数据用于说明这些新方法。
The use of cumulative incidence functions for characterizing the risk of one type of event in the presence of others has become increasingly popular over the past decade. The problems of modeling, estimation and inference have been treated using parametric, nonparametric and semi-parametric methods. Efforts to develop suitable extensions of machine learning methods, such as regression trees and related ensemble methods, have begun only recently. In this paper, we develop a novel approach to building regression trees for estimating cumulative incidence curves in a competing risks setting. The proposed methods employ augmented estimators of the Brier score risk as the primary basis for building and pruning trees. The proposed methods are easily implemented using the R statistical software package. Simulation studies demonstrate the utility of our approach in the competing risks setting. Data from the Radiation Therapy Oncology Group (trial 9410) is used to illustrate these new methods.
