Chen Jingxiang, Fu Haoda, He Xuanyao, Kosorok Michael R, Liu Yufeng
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.
Eli Lilly and Company, Indianapolis, Indiana, U.S.A.
Biometrics. 2018 Sep;74(3):924-933. doi: 10.1111/biom.12865. Epub 2018 Mar 13.
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 article, 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.
精准医学是一个新兴的疾病治疗与预防科学话题，它考虑了个体患者的特征。这是临床研究的一个重要方向，最近已经提出了许多统计方法。精准医学的主要目标之一是获得一个最优个体治疗规则（ITR），该规则可根据每位患者的具体特征帮助做出治疗选择决策。最近，已经提出了结果加权学习（OWL）方法，通过最大化预期临床结果来估计二元治疗设置中的这种最优ITR。然而，对于序贯治疗设置，如个体化剂量探索，尚不清楚如何使用OWL。在本文中，我们提出了一种用于估计序贯治疗ITR的新技术。具体而言，我们提出了一种具有分段凸损失函数的数据复制技术。我们在某些条件下为所得估计的ITR建立了费舍尔一致性，并获得了收敛性和风险界性质。模拟示例以及对2型糖尿病观察性研究数据集的应用表明，与现有替代方法相比，所提出的方法具有极具竞争力的性能。
