Personalized medicine is a medical procedure that receives considerable scientific and commercial attention. The goal of personalized medicine is to assign the optimal treatment regime for each individual patient, according to his/her personal prognostic information. When there are a large number of pretreatment variables, it is crucial to identify those important variables that are necessary for treatment decision making. In this paper, we study two information criteria: the concordance and value information criteria, for variable selection in optimal treatment decision making. We consider both fixed-$p$ and high dimensional settings, and show our information criteria are consistent in model/tuning parameter selection. We further apply our information criteria to four estimation approaches, including robust learning, concordance-assisted learning, penalized A-learning and sparse concordance-assisted learning, and demonstrate the empirical performance of our methods by simulations.

中文翻译：

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一致性和价值信息标准，用于最佳治疗决策

个性化医学是一种受到相当科学和商业关注的医疗程序。个性化医学的目标是根据每个患者的个人预后信息为其分配最佳治疗方案。当存在大量的预处理变量时，至关重要的是要确定对于治疗决策而言必要的那些重要变量。在本文中，我们研究了两个信息准则：一致性和价值信息准则，用于最优治疗决策中的变量选择。我们同时考虑了固定的$ p $和高维设置，并表明我们的信息标准在模型/调整参数选择中是一致的。我们进一步将信息标准应用于四种估算方法，包括稳健的学习，协调辅助的学习，