Abstract Subgroup identification serves as an important step towards precision medicine which has attracted great attention recently. On the other hand, longitudinal data with dropouts often arises in medical research. However there is little work in subgroup identification considering this data type. Therefore, in this paper we propose a new subgroup identification method based on concave fusion penalization and median regression for longitudinal data with dropouts. In order to deal with missingness, we introduce multiply robust weights which allow multiple models for the probability of being observed. As long as one of the models is correctly specified, the proposed estimator is able to achieve oracle property in the case of missingness. Furthermore, we develop an efficient algorithm and propose a modified Bayesian information criterion to select penalization parameter. The asymptotic properties of the proposed method is established under some regularity conditions. The numerical performance is illustrated in simulations and the proposed method is applied to the quality of life data from a breast cancer trail.

中文翻译：

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通过中值回归对具有丢失的纵向数据进行稳健的子组识别

摘要 亚群鉴定是近年来备受关注的精准医学的重要一步。另一方面，在医学研究中经常出现具有辍学的纵向数据。然而，考虑到这种数据类型，在子组识别方面的工作很少。因此，在本文中，我们提出了一种基于凹融合惩罚和中值回归的新的子组识别方法，用于具有丢失的纵向数据。为了处理缺失，我们引入了多重稳健权重，允许多个模型被观察到的概率。只要正确指定了其中一个模型，所提出的估计器就能够在缺失的情况下实现预言机属性。此外，我们开发了一种有效的算法并提出了一种改进的贝叶斯信息准则来选择惩罚参数。该方法的渐近性质是在一定的规律性条件下建立的。在模拟中说明了数值性能，并且将所提出的方法应用于来自乳腺癌踪迹的生活质量数据。