A Concave Pairwise Fusion Approach to Subgroup Analysis
A Concave Pairwise Fusion Approach to Subgroup Analysis
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DOI：
10.1080/01621459.2016.1148039
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发表时间：
2017-01-01
影响因子：
3.7
通讯作者：
Huang, Jian
中科院分区：
文献类型：
--
作者：
Ma, Shujie;Huang, Jian
制定个体化治疗策略的一个重要步骤是正确识别异质人群的亚群，以便对每个亚群进行特定治疗。本文考虑的问题是使用从具有不同均值的子群组成的总体中抽取的样本，以及某些协变量。我们提出了一种基于回归模型的亚组分析的惩罚方法，其中异质性是由未观察到的潜在因素驱动的，因此可以用受试者特定的截距来表示。我们将凹罚函数应用于截距的两两差。此过程自动将观察结果分成子组。为了实现所提出的方法，我们开发了一种具有凹惩罚的乘法器交替方向方法，并证明了其收敛性。我们还建立了所提估计量的理论性质，并确定了恢复组间信号最小差的阶数要求。这些结果为亚群分析的统计推断提供了良好的依据。通过对克利夫兰心脏病数据集的模拟研究和分析，进一步说明了我们提出的方法。本文的补充材料可在网上获得。
An important step in developing individualized treatment strategies is correct identification of subgroups of a heterogeneous population to allow specific treatment for each subgroup. This article considers the problem using samples drawn from a population consisting of subgroups with different mean values, along with certain covariates. We propose a penalized approach for subgroup analysis based on a regression model, in which heterogeneity is driven by unobserved latent factors and thus can be represented by using subject-specific intercepts. We apply concave penalty functions to pairwise differences of the intercepts. This procedure automatically divides the observations into subgroups. To implement the proposed approach, we develop an alternating direction method of multipliers algorithm with concave penalties and demonstrate its convergence. We also establish the theoretical properties of our proposed estimator and determine the order requirement of the minimal difference of signals between groups to recover them. These results provide a sound basis for making statistical inference in subgroup analysis. Our proposed method is further illustrated by simulation studies and analysis of a Cleveland heart disease dataset. Supplementary materials for this article are available online.