Subgroup analysis based on structured mixed-effects models for longitudinal data
Subgroup analysis based on structured mixed-effects models for longitudinal data
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DOI：
10.1080/10543406.2020.1730867
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发表时间：
2020-03
影响因子：
1.1
通讯作者：
Juan Shen;A. Qu
中科院分区：
文献类型：
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作者：
Juan Shen;A. Qu
摘要近年来，亚组分析已成为识别未知亚组成员的重要工具。然而，对于纵向数据，亚组分析仍处于研究阶段。在本文中，我们提出了一个结构化的混合效应的纵向数据模型的亚组分布，并确定亚组成员的同时。在拟议的结构化混合效应模型中，异质性治疗效应被建模为来自两组分混合模型的随机效应，而混合模型的成员资格被纳入使用逻辑模型相对于一些协变量。我们的方法的一个优点是，我们能够通过EM型算法，保持亚组成员随着时间的推移不变的治疗效果的估计。我们的数值研究和真实的数据的例子表明，所提出的模型优于其他竞争的方法。
ABSTRACT In recent years, subgroup analysis has emerged as an important tool to identify unknown subgroup memberships. However, subgroup analysis is still under-studied for longitudinal data. In this paper, we propose a structured mixed-effects approach for longitudinal data to model subgroup distribution and identify subgroup membership simultaneously. In the proposed structured mixed-effects model, the heterogeneous treatment effect is modeled as a random effect from a two-component mixture model, while the membership of the mixture model is incorporated using a logistic model with respect to some covariates. One advantage of our approach is that we are able to derive the estimation of the treatment effects through an EM-type algorithm which keeps the subgroup membership unchanged over time. Our numerical studies and real data example demonstrate that the proposed model outperforms other competing methods.