Exploration of Heterogeneous Treatment Effects via Concave Fusion
Exploration of Heterogeneous Treatment Effects via Concave Fusion
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DOI：
10.1515/ijb-2018-0026
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发表时间：
2020-05-01
影响因子：
1.2
通讯作者：
Liu, Mingming
中科院分区：
文献类型：
--
作者：
Ma, Shujie;Huang, Jian;Liu, Mingming
了解治疗异质性对于精准医学的发展至关重要，精准医学旨在针对具有相似特征的患者亚组制定医疗治疗方案。实现这一目标的挑战之一是我们通常没有关于治疗效果的患者分组信息的先验知识。为了解决这个问题，我们考虑一种异质回归模型，该模型允许治疗变量的系数与未知的分组信息相关。我们开发了一种凹融合惩罚方法来估计分组结构和特定于子组的治疗效果，并推导出乘数算法的交替方向方法用于其实现。我们还研究了所提出方法的理论特性，并表明在适当的条件下，存在一个局部最小化器，该局部最小化器等于基于高概率真实分组信息的先验知识的 Oracle 最小二乘估计器。这为使用所提出的方法对亚组特异性治疗效果进行统计推断提供了理论支持。所提出的方法在模拟研究中得到说明，并用艾滋病临床试验小组研究的真实数据进行说明。
Understanding treatment heterogeneity is essential to the development of precision medicine, which seeks to tailor medical treatments to subgroups of patients with similar characteristics. One of the challenges of achieving this goal is that we usually do not have a priori knowledge of the grouping information of patients with respect to treatment effect. To address this problem, we consider a heterogeneous regression model which allows the coefficients for treatment variables to be subject-dependent with unknown grouping information. We develop a concave fusion penalized method for estimating the grouping structure and the subgroup-specific treatment effects, and derive an alternating direction method of multipliers algorithm for its implementation. We also study the theoretical properties of the proposed method and show that under suitable conditions there exists a local minimizer that equals the oracle least squares estimator based on a priori knowledge of the true grouping information with high probability. This provides theoretical support for making statistical inference about the subgroup-specific treatment effects using the proposed method. The proposed method is illustrated in simulation studies and illustrated with real data from an AIDS Clinical Trials Group Study.