Discovering a medication that suitable for all patients is not possible due to the fact that the reaction to medication may differ significantly across different patient subgroups. The heterogeneity of treatment effects is central to the agenda for both personalized medicine and treatment selection. To expedite the development of tailored therapies and improve the treatment efficacy, identification of subgroups that exhibit different treatment effects is thus playing an essential role. In this paper, we consider high-dimensional dense longitudinal observations which have frequent and large number of measurements with high-dimensional covariates. We offer a data-driven subgroup identification method, which incorporates the sparse boosting algorithm into homogeneity pursuit via change point detection. Extensive simulations are carried out to examine the performance of our proposed approach. We further illustrate our method by analyzing a wallaby growth dataset.

由于不同患者亚组对药物的反应可能存在显着差异，因此不可能找到适合所有患者的药物。治疗效果的异质性对于个性化医学和治疗选择都至关重要。为了加快定制疗法的开发并提高治疗效果，鉴定具有不同治疗效果的亚组因此起着至关重要的作用。在本文中，我们考虑了高维密集纵向观测，这些观测频繁且具有大量高维协变量的测量值。我们提供了一种数据驱动的子组识别方法，该方法将稀疏增强算法通过变化点检测纳入同质性追求。进行了广泛的仿真以检验我们提出的方法的性能。我们通过分析袋鼠生长数据集进一步说明我们的方法。