Minsker Stanislav, Zhao Ying-Qi, Cheng Guang
J Am Stat Assoc. 2016;111(514):875-887. doi: 10.1080/01621459.2015.1066682. Epub 2016 Aug 18.
Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this article, we propose a cost-effective estimation method from an active learning perspective. In particular, our method recruits only the "most informative" patients (in terms of learning the optimal ITRs) from an ongoing clinical trial. Simulation studies and real-data examples show that our active clinical trial method significantly improves on competing methods. We derive risk bounds and show that they support these observed empirical advantages. Supplementary materials for this article are available online.
个体化治疗规则（ITRs）根据患者的个体特征来定制治疗方案。它们可以显著改善患者护理，因此越来越受欢迎。随机临床试验期间收集的数据通常用于估计最优的个体化治疗规则。然而，这些试验通常运行成本高昂，而且它们并非设计用于高效估计个体化治疗规则。在本文中，我们从主动学习的角度提出一种具有成本效益的估计方法。具体而言，我们的方法仅从正在进行的临床试验中招募“信息最丰富”的患者（就学习最优个体化治疗规则而言）。模拟研究和实际数据示例表明，我们的主动临床试验方法显著优于其他竞争方法。我们推导了风险边界，并表明它们支持这些观察到的经验优势。本文的补充材料可在线获取。
