In recent years, research interest in personalised treatments has been growing. However, treatment effect heterogeneity and possibly time-varying treatment effects are still often overlooked in clinical studies. Statistical tools are needed for the identification of treatment response patterns, taking into account that treatment response is not constant over time. We aim to provide an innovative method to obtain dynamic treatment effect phenotypes on a time-to-event outcome, conditioned on a set of relevant effect modifiers. The proposed method does not require the assumption of proportional hazards for the treatment effect, which is rarely realistic. We propose a spline-based survival neural network, inspired by the Royston-Parmar survival model, to estimate time-varying conditional treatment effects. We then exploit the functional nature of the resulting estimates to apply a functional clustering of the treatment effect curves in order to identify different patterns of treatment effects. The application that motivated this work is the discontinuation of treatment with Mineralocorticoid receptor Antagonists (MRAs) in patients with heart failure, where there is no clear evidence as to which patients it is the safest choice to discontinue treatment and, conversely, when it leads to a higher risk of adverse events. The data come from an electronic health record database. A simulation study was performed to assess the performance of the spline-based neural network and the stability of the treatment response phenotyping procedure. In light of the results, the suggested approach has the potential to support personalized medical choices by assessing unique treatment responses in various medical contexts over a period of time.

翻译：近年来，个性化治疗的研究兴趣日益增长。然而，在临床研究中，治疗效果异质性以及可能随时间变化的治疗效果常常仍被忽视。我们需要统计工具来识别治疗响应模式，同时考虑到治疗效果并非随时间恒定。我们旨在提供一种创新方法，以获取基于一组相关效应修饰因子的时间-事件结局的动态治疗效果表型。所提出的方法不需要假设治疗效果的比例风险（这在现实中很少成立）。我们提出了一种基于样条的生存神经网络，灵感来源于Royston-Parmar生存模型，以估计时变的条件治疗效果。随后，我们利用所得估计的函数性质，对治疗效果曲线进行功能聚类，以识别不同的治疗效果模式。推动这项工作的应用是心力衰竭患者中断盐皮质激素受体拮抗剂（MRAs）治疗的情况，目前尚缺乏明确证据表明对哪些患者中断治疗是最安全的选择，以及相反情况下何时会导致更高的不良事件风险。数据来源于电子健康记录数据库。我们进行了模拟研究，以评估基于样条的神经网络的性能以及治疗响应表型程序的稳定性。结果表明，所建议的方法具有潜力，通过评估不同医疗背景下随时间变化的独特治疗响应，支持个性化医疗选择。