Subgroup Analysis via Recursive Partitioning
Subgroup Analysis via Recursive Partitioning
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DOI：
10.2139/ssrn.1341380
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Xiaogang Su;Chih-Ling Tsai;Hansheng Wang;D. Nickerson;Bogong Li
中科院分区：
文献类型：
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作者：
Xiaogang Su;Chih-Ling Tsai;Hansheng Wang;D. Nickerson;Bogong Li
亚组分析是比较分析的一个组成部分，其中评估治疗对反应的影响是核心利益。其目标是确定不同亚人群治疗效果的异质性。在本文中，我们采用递归划分的思想并引入交互树（IT）过程来进行子组分析。 IT程序自动促进了许多客观定义的亚组，在其中一些亚组中，治疗效果显着，而在另一些亚组中，治疗效果可以忽略不计，甚至是负面效果。继承了标准 CART（Breiman 等人，1984）方法来构建树结构。此外，为了提取导致治疗效果异质性的因素，通过交互树的随机森林提供了变量重要性度量。模拟实验和人口普查工资数据分析均用于说明。
Subgroup analysis is an integral part of comparative analysis where assessing the treatment effect on a response is of central interest. Its goal is to determine the heterogeneity of the treatment effect across subpopulations. In this paper, we adapt the idea of recursive partitioning and introduce an interaction tree (IT) procedure to conduct subgroup analysis. The IT procedure automatically facilitates a number of objectively defined subgroups, in some of which the treatment effect is found prominent while in others the treatment has a negligible or even negative effect. The standard CART (Breiman et al., 1984) methodology is inherited to construct the tree structure. Also, in order to extract factors that contribute to the heterogeneity of the treatment effect, variable importance measure is made available via random forests of the interaction trees. Both simulated experiments and analysis of census wage data are presented for illustration.