Estimating individualized treatment rules for ordinal treatments.
Estimating individualized treatment rules for ordinal treatments.
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作者：
Chen J;Fu H;He X;Kosorok MR;Liu Y
精准医学是一个新兴的疾病治疗和预防科学课题，它考虑到个体患者的特征。这是临床研究的一个重要方向，近年来提出了许多统计方法。获得最优个体治疗规则（ITR）是精准医疗的主要目标之一，ITR可以根据每个患者的具体特征做出治疗选择决策。最近，结果加权学习（OWL）被提出通过最大化预期临床结果来估计二元治疗环境中的最佳ITR。然而，对于常规治疗设置，如个体化剂量发现，如何使用OWL尚不清楚。本文提出了一种用序处理估计ITR的新方法。特别地，我们提出了一种分段凸损失函数的数据复制技术。在一定条件下，我们建立了所得估计ITR的Fisher一致性，并得到了收敛性和风险界性质。模拟实例和对2型糖尿病观察性研究数据集的应用表明，与现有的替代方法相比，所提出的方法具有很强的竞争力。
Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been proposed recently. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient’s specific characteristics. Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for ordinal treatment settings, such as individualized dose finding, it is unclear how to use OWL. In this paper, we propose a new technique for estimating ITR with ordinal treatments. In particular, we propose a data duplication technique with a piecewise convex loss function. We establish Fisher consistency for the resulting estimated ITR under certain conditions, and obtain the convergence and risk bound properties. Simulated examples and an application to a dataset from a type 2 diabetes mellitus observational study demonstrate the highly competitive performance of the proposed method compared to existing alternatives.
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影响因子：
1.9
作者：
Zhang B;Tsiatis AA;Laber EB;Davidian M
通讯作者：
Davidian M
影响因子：
4.5
作者：
Qian M;Murphy SA
通讯作者：
Murphy SA
影响因子：
1.9
作者：
Xu Y;Yu M;Zhao YQ;Li Q;Wang S;Shao J
通讯作者：
Shao J
DOI：
10.1080/01621459.2012.695674
发表时间：
2012-09-01
影响因子：
3.7
作者：
Zhao Y;Zeng D;Rush AJ;Kosorok MR
通讯作者：
Kosorok MR
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V