As a new strategy for treatment, which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules for patients who have heterogeneous responses to treatment is one of the important areas in developing personalized medicine. As more and more information per individual is being collected in clinical studies and not all of the information is relevant for treatment discovery, variable selection becomes increasingly important in discovering individualized treatment rules. In this article, we develop a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. We show that the resulting estimator of the treatment rule is consistent and establish variable selection consistency and the asymptotic distribution of the estimators. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data. Copyright © 2015 John Wiley & Sons, Ltd.

中文翻译：

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关于具有惩罚性结果加权学习的最优个性化治疗选择的稀疏表示。

作为一种考虑个体异质性的新治疗策略，个性化医学的兴趣日益浓厚。发现对治疗反应不同的患者的个体化治疗规则是开发个性化药物的重要领域之一。随着临床研究中每个人的信息越来越多，并且并非所有信息都与治疗发现相关，变量选择在发现个性化治疗规则中变得越来越重要。在本文中，我们开发了一种基于惩罚性结果加权学习的变量选择方法，通过该方法，最佳治疗规则被视为分类问题，其中，每个受试者的加权均与其临床结果成正比。我们证明了处理规则的结果估计量是一致的，并建立了变量选择的一致性和估计量的渐近分布。通过模拟研究和对慢性抑郁数据的分析证明了所提出方法的性能。版权所有©2015 John Wiley＆Sons，Ltd.