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.

翻译：临床研究中的患者常表现出异质性治疗效应（HTE）。经典亚组分析提供了检验效应修饰的推断工具，而现代机器学习方法则通过估计条件平均治疗效应（CATE）以实现个体层面的预测。两种范式各有局限：以推断为中心的方法可能牺牲预测效用，而以预测为中心的方法往往缺乏统计保证。我们提出一种融合这两种视角的混合双阶段工作流程。第一阶段运用统计推断来检验是否存在可信的治疗效应异质性，并防范虚假发现。第二阶段将异质性证据转化为个体化治疗策略，并通过交叉拟合的双稳健（DR）度量进行评估，同时施加关于损害风险的奈曼-皮尔逊（NP）约束。我们通过基于模拟数据和真实ACTG 175 HIV试验的实例演示该工作流程。本教程提供了实用的实施检查清单，讨论了与申办方导向的HTE工作流程的关联，为从异质性评估到个体化治疗策略的转化提供了透明且可审计的路径。