D-learning to estimate optimal individual treatment rules
D-learning to estimate optimal individual treatment rules
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DOI：
10.1214/18-ejs1480
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发表时间：
2018-01-01
影响因子：
1.1
通讯作者：
Liu, Yufeng
中科院分区：
文献类型：
--
作者：
Qi, Zhengling;Liu, Yufeng
近年来，由于患者对不同治疗的反应可能存在异质性，对患者最佳个体化治疗规则（ITR）的探索引起了广泛关注。最佳ITR是基于患者特征的决策函数，用于最大化预期临床结果的治疗。目前的文献主要集中在两类方法，基于模型和基于分类的方法。基于模型的方法依赖于结果的条件均值的估计，而不是直接针对最佳ITR的决策边界。因此，它们可能会产生次优决策。相比之下，虽然基于分类的方法通过将问题转换为加权分类来直接针对最佳ITR，但这些方法依赖于对所有主题使用正确的权重，这可能导致模型错误指定。为了克服这些方法的潜在缺点，我们提出了一种简单灵活的一步方法来直接学习（D学习）最优ITR，而无需模型和权重规范。多类别的D-学习也提出了多个治疗的情况下。提出了一种新的效果度量，以量化患者治疗的相对强度。我们证明了估计的一致性，并为所提出的D-学习建立了严格的有限样本误差界。数值研究，包括模拟和真实的数据的例子来证明D-学习的竞争力。
Recent exploration of the optimal individual treatment rule (ITR) for patients has attracted a lot of attentions due to the potential heterogeneous response of patients to different treatments. An optimal ITR is a decision function based on patients' characteristics for the treatment that maximizes the expected clinical outcome. Current literature mainly focuses on two types of methods, model-based and classification-based methods. Model-based methods rely on the estimation of conditional mean of outcome instead of directly targeting decision boundaries for the optimal ITR. As a result, they may yield suboptimal decisions. In contrast, although classification based methods directly target the optimal ITR by converting the problem into weighted classification, these methods rely on using correct weights for all subjects, which may cause model misspecification. To overcome the potential drawbacks of these methods, we propose a simple and flexible one-step method to directly learn (D-learning) the optimal ITR without model and weight specifications. Multi-category D-learning is also proposed for the case with multiple treatments. A new effect measure is proposed to quantify the relative strength of an treatment for a patient. We show estimation consistency and establish tight finite sample error bounds for the proposed D-learning. Numerical studies including simulated and real data examples are used to demonstrate the competitive performance of D-learning.