The tree-based methodology has been widely applied to identify predictors of health outcomes in medical studies. However, the classical tree-based approaches do not pay particular attention to treatment assignment and thus do not consider prediction in the context of treatment received. In recent years, attention has been shifting from average treatment effects to identifying moderators of treatment response, and tree-based approaches to identify subgroups of subjects with enhanced treatment responses are emerging. In this study, we extend and present modifications to one of these approaches (Zhang et al., 2010 [29]) to efficiently identify subgroups of subjects who respond more favorably to one treatment than another based on their baseline characteristics. We extend the algorithm by incorporating an automatic pruning step and propose a measure for assessment of the predictive performance of the constructed tree. We evaluate the proposed method through a simulation study and illustrate the approach using a data set from a clinical trial of treatments for alcohol dependence. This simple and efficient statistical tool can be used for developing algorithms for clinical decision making and personalized treatment for patients based on their characteristics.

中文翻译：

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一种用于个性化医疗决策的改进分类树方法

基于树的方法已被广泛应用于确定医学研究中健康结果的预测因素。然而，经典的基于树的方法并不特别关注治疗分配，因此不考虑在接受治疗的背景下进行预测。近年来，注意力已经从平均治疗效果转移到确定治疗反应的调节因素，并且出现了基于树的方法来确定具有增强治疗反应的受试者亚组。在这项研究中，我们扩展并提出了对其中一种方法的修改（Zhang 等人，2010 [29]），以根据其基线特征有效地识别对一种治疗反应比另一种治疗更有利的受试者亚组。我们通过合并自动修剪步骤扩展了算法，并提出了一种评估构建树的预测性能的措施。我们通过模拟研究评估所提出的方法，并使用来自酒精依赖治疗临床试验的数据集来说明该方法。这种简单而有效的统计工具可用于根据患者的特征开发用于临床决策和个性化治疗的算法。