Active Clinical Trials for Personalized Medicine

作者：Minsker Stanislav; Zhao Ying Qi^*; Cheng Guang

来源：Journal of the American Statistical Association, 2016, 111(514): 875-887.

DOI：10.1080/01621459.2015.1066682

• Active learning
• Clinical trial
• Individualized treatment rule
• Personalized medicine
• Risk bound

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.

• 出版日期2016-6