子群识别与个性化治疗政策：混合两阶段工作流程教程
Subgroup Identification and Individualized Treatment Policies: A Tutorial on the Hybrid Two-Stage Workflow
AI总结：
本文提出混合两阶段工作流程，结合统计推断与机器学习方法，用于识别治疗效应异质性并制定个性化治疗政策。
摘要：
临床研究中的患者往往表现出治疗效应异质性（HTE）。经典子群分析提供推断工具来检验效应修饰，而现代机器学习方法估计条件平均治疗效应（CATE）以实现个体层面的预测。每种方法都有局限性：以推断为主的方案可能牺牲预测实用性，而以预测为主的方案通常缺乏统计保证。我们提出了一种混合两阶段工作流程，整合了这些视角。第一阶段应用统计推断来检验是否存在可信的治疗效应异质性，并防止伪发现。第二阶段将异质性证据转化为个性化治疗政策，并通过交叉拟合双重鲁棒（DR）指标和奈曼-皮尔逊（NP）约束下的危害进行评估。我们通过基于模拟数据和真实ACTG 175 HIV试验的实例展示了该流程。本教程提供了实用的实施检查表，并讨论了与以赞助商为导向的HTE工作流程的联系，提供了一条从异质性评估到个性化治疗政策的透明且可审计的路径。
英文摘要：
Patients in clinical studies often exhibit heterogeneous treatment effect (HTE). Classical subgroup analyses provide inferential tools to test for effect modification, while modern machine learning methods estimate the Conditional Average Treatment Effect (CATE) to enable individual level prediction. Each paradigm has limitations: inference focused approaches may sacrifice predictive utility, and prediction focused approaches often lack statistical guarantees. We present a hybrid two-stage workflow that integrates these perspectives. Stage 1 applies statistical inference to test whether credible treatment effect heterogeneity exists with the protection against spurious findings. Stage 2 translates heterogeneity evidence into individualized treatment policies, evaluated by cross fitted doubly robust (DR) metrics with Neyman-Pearson (NP) constraints on harm. We illustrate the workflow with working examples based on simulated data and a real ACTG 175 HIV trial. This tutorial provides practical implementation checklists and discusses links to sponsor oriented HTE workflows, offering a transparent and auditable pathway from heterogeneity assessment to individualized treatment policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
