PDF(Pubmed)
Abstract：
A group of variables are commonly seen in diagnostic medicine when multiple prognostic factors are aggregated into a composite score to represent the risk profile. A model selection method considers these covariates as all-in or all-out types. Model selection procedures for grouped covariates and their applications have thrived in recent years, in part because of the development of genetic research in which gene-gene or gene-environment interactions and regulatory network pathways are considered groups of individual variables. However, little has been discussed on how to utilize grouped covariates to grow a classification tree. In this paper, we propose a nonparametric method to address the selection of split variables for grouped covariates and their following selection of split points. Comprehensive simulations were implemented to show the superiority of our procedures compared to a commonly used recursive partition algorithm. The practical use of our method is demonstrated through a real data analysis that uses a group of prognostic factors to classify the successful mobilization of peripheral blood stem cells.
摘要：
当将多个预后因素汇总为复合评分以代表风险状况时，在诊断医学中通常会看到一组变量。模型选择方法将这些协变量视为全进或全出类型。分组协变量的模型选择程序及其应用近年来蓬勃发展，部分原因是遗传研究的发展，其中基因-基因或基因-环境相互作用和调节网络途径被认为是个体变量的群体。然而,关于如何利用分组协变量来生长分类树的讨论很少。在本文中,我们提出了一种非参数方法来解决分组协变量的分割变量的选择及其随后的分割点的选择。进行了全面的仿真，以显示与常用的递归分区算法相比，我们的程序的优越性。通过使用一组预后因素对成功动员外周血干细胞进行分类的真实数据分析，证明了我们方法的实际使用。
