Personalized medicine, a paradigm of medicine tailored to a patient's characteristics, is an increasingly attractive field in health care. An important goal of personalized medicine is to identify a subgroup of patients, based on baseline covariates, that benefits more from the targeted treatment than other comparative treatments. Most of the current subgroup identification methods only focus on obtaining a subgroup with an enhanced treatment effect without paying attention to subgroup size. Yet, a clinically meaningful subgroup learning approach should identify the maximum number of patients who can benefit from the better treatment. In this paper, we present an optimal subgroup selection rule (SSR) that maximizes the number of selected patients, and in the meantime, achieves the pre-specified clinically meaningful mean outcome, such as the average treatment effect. We derive two equivalent theoretical forms of the optimal SSR based on the contrast function that describes the treatment-covariates interaction in the outcome. We further propose a ConstrAined PolIcy Tree seArch aLgorithm (CAPITAL) to find the optimal SSR within the interpretable decision tree class. The proposed method is flexible to handle multiple constraints that penalize the inclusion of patients with negative treatment effects, and to address time to event data using the restricted mean survival time as the clinically interesting mean outcome. Extensive simulations, comparison studies, and real data applications are conducted to demonstrate the validity and utility of our method.
翻译:个人医学是一种适合病人特点的医学范例,它是一个越来越有吸引力的保健领域。个性医学的一个重要目标是根据基线共变法确定一个病人分组,这种分组比其他比较治疗更能从有针对性的治疗中获益。目前的分组识别方法大多只侧重于获得一个分组,其治疗效果更大,而没有注意分组的大小。然而,一个具有临床意义的分组学习方法应当确定能够从更好的治疗中受益的病人的最大数量。在本文件中,我们提出了一个最佳分组选择规则(SSR),以尽量增加选定病人的人数,同时实现事先确定的临床上有意义的平均结果,如平均治疗效果。我们根据描述结果中的治疗-共变异作用的对比功能,得出两种等同的最佳改革理论形式。我们进一步建议采用ConstrAined PolICcy Tree seArch arch arg AL (CAPITAL) 方法,以便在可解释的决策树类中找到最佳的体系。拟议方法可以灵活地处理多种限制因素,即惩罚具有负面临床效果的病人,例如平均治疗效果的临床试验结果。我们利用模拟数据来证明实际结果。