CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search
CAPITAL：通过约束政策树搜索识别最优子群体AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要围绕异质性处理效应、平均治疗收益约束及治疗获益子群体选择展开。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
CAPITAL：通过约束政策树搜索识别最优子群体
- 研究问题
- 个性化治疗中，如何在达到预设临床平均收益的同时，选出尽可能多的获益患者，并用可解释规则表达该子群体？
- 方法与思路
- 作者基于处理与协变量交互的对比函数推导最优子群体规则，再以约束政策树搜索在可解释决策树类内寻找规则。
- 材料陈述的贡献
- 摘要称给出最优规则的两种等价形式，支持限制负效应患者纳入的多重约束，并扩展到限制平均生存时间结局。
- 阅读关注点
- 应核对临床收益约束与子群体规模目标、理论最优规则和树类搜索的关系，以及处理效应估计误差如何影响选择。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明因果识别假设、搜索保证及模拟和真实应用的具体结果。
原文摘要
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.