对于生存分析应用，我们提出了一种新的程序来识别具有较大治疗效果的亚组，重点关注治疗可能有害的亚组。这种方法被称为森林搜索，相对简单且灵活。根据指示危害的风险比阈值筛选和选择所有可能的亚组，并根据标准 Cox 模型进行评估。通过逆转治疗的作用，人们可以寻求确定实质性益处。我们应用分裂一致性标准来识别被认为“与伤害最大程度一致”的子组。子群识别的 1 类误差和功效可以通过数值积分快速近似。为了帮助推理，我们描述了一个引导偏差校正的 Cox 模型估计器，其方差由 Jacknife 近似估计。我们在模拟中提供了对操作特性的详细评估，并与虚拟孪生和广义随机森林进行比较，我们发现该提案具有良好的性能。特别是，在我们的模拟设置中，我们发现所提出的方法可以有利地控制 1 类错误，从而以更高的功效和分类精度错误地识别异质性，从而实现实质性异质效应。为来自肿瘤学和 HIV 临床试验的公开数据集提供了两个真实数据应用程序。© 2024 John Wiley

For survival analysis applications we propose a novel procedure for identifying subgroups with large treatment effects, with focus on subgroups where treatment is potentially detrimental. The approach, termed forest search, is relatively simple and flexible. All-possible subgroups are screened and selected based on hazard ratio thresholds indicative of harm with assessment according to the standard Cox model. By reversing the role of treatment one can seek to identify substantial benefit. We apply a splitting consistency criteria to identify a subgroup considered "maximally consistent with harm." The type-1 error and power for subgroup identification can be quickly approximated by numerical integration. To aid inference we describe a bootstrap bias-corrected Cox model estimator with variance estimated by a Jacknife approximation. We provide a detailed evaluation of operating characteristics in simulations and compare to virtual twins and generalized random forests where we find the proposal to have favorable performance. In particular, in our simulation setting, we find the proposed approach favorably controls the type-1 error for falsely identifying heterogeneity with higher power and classification accuracy for substantial heterogeneous effects. Two real data applications are provided for publicly available datasets from a clinical trial in oncology, and HIV.© 2024 John Wiley & Sons Ltd.