Subgroup selection in clinical trials is essential for identifying patient groups that react differently to a treatment, thereby enabling personalised medicine. In particular, subgroup selection can identify patient groups that respond particularly well to a treatment or that encounter adverse events more often. However, this is a post-selection inference problem, which may pose challenges for traditional techniques used for subgroup analysis, such as increased Type I error rates and potential biases from data-driven subgroup identification. In this paper, we present two methods for subgroup selection in regression problems: one based on generalised linear modelling and another on isotonic regression. We demonstrate how these methods can be used for data-driven subgroup identification in the analysis of clinical trials, focusing on two distinct tasks: identifying patient groups that are safe from manifesting adverse events and identifying patient groups with high treatment effect, while controlling for Type I error in both cases. A thorough simulation study is conducted to evaluate the strengths and weaknesses of each method, providing detailed insight into the sensitivity of the Type I error rate control to modelling assumptions.

翻译：临床试验中的亚组选择对于识别对治疗反应不同的患者群体至关重要，从而促进个体化医疗的发展。具体而言，亚组选择能够识别出对治疗反应特别良好或更频繁出现不良事件的患者群体。然而，这是一个选择后推断问题，可能对用于亚组分析的传统技术构成挑战，例如增加I类错误率以及数据驱动亚组识别带来的潜在偏倚。本文提出了两种用于回归问题中亚组选择的方法：一种基于广义线性模型，另一种基于保序回归。我们展示了如何在临床试验分析中利用这些方法进行数据驱动的亚组识别，重点关注两个不同的任务：识别不会出现不良事件的安全患者群体，以及识别具有高治疗效应的患者群体，并在两种情况下均控制I类错误。通过全面的模拟研究评估了每种方法的优缺点，深入揭示了I类错误率控制对建模假设的敏感性。