Subgroup identification based on differential effect search-A recursive partitioning method for establishing response to treatment in patient subpopulations
Subgroup identification based on differential effect search-A recursive partitioning method for establishing response to treatment in patient subpopulations
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DOI：
10.1002/sim.4289
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发表时间：
2011-09-20
影响因子：
2
通讯作者：
Enas, Gregory
中科院分区：
文献类型：
--
作者：
Lipkovich, Ilya;Dmitrienko, Alex;Enas, Gregory
我们提出了一种新的递归分区方法，用于识别具有增强治疗效果的受试者亚组，该方法基于差分效应搜索算法。其思想是通过递归地将数据库划分为每个父组的两个子组来构建子组的集合，使得两个子组中的一个子组内的治疗效果与另一个子组相比最大化。数据分割的过程继续，直到满足预定义的停止条件。该方法类似于“交互树”方法，其允许在分裂标准中并入治疗-分裂交互。然而，与其他基于树的方法不同，该方法仅在协变量空间的特定区域内搜索，并生成多个潜在感兴趣的子组。我们开发了这种方法，并提供了感兴趣的关键主题，包括使用不同的分裂标准生成多个有前途的子组，通过交叉验证选择复杂性参数的最佳值，并解决I型错误率膨胀固有的数据挖掘应用程序使用基于重采样的方法。我们评估的操作特性的程序使用模拟研究，并说明了该方法与临床试验的例子。版权所有（C）2011约翰威利父子有限公司
We propose a novel recursive partitioning method for identifying subgroups of subjects with enhanced treatment effects based on a differential effect search algorithm. The idea is to build a collection of subgroups by recursively partitioning a database into two subgroups at each parent group, such that the treatment effect within one of the two subgroups is maximized compared with the other subgroup. The process of data splitting continues until a predefined stopping condition has been satisfied. The method is similar to 'interaction tree' approaches that allow incorporation of a treatment-by-split interaction in the splitting criterion. However, unlike other tree-based methods, this method searches only within specific regions of the covariate space and generates multiple subgroups of potential interest. We develop this method and provide guidance on key topics of interest that include generating multiple promising subgroups using different splitting criteria, choosing optimal values of complexity parameters via cross-validation, and addressing Type I error rate inflation inherent in data mining applications using a resampling-based method. We evaluate the operating characteristics of the procedure using a simulation study and illustrate the method with a clinical trial example. Copyright (C) 2011 John Wiley & Sons, Ltd.