Active Clinical Trials for Personalized Medicine

• 成果类型: Article
• 署名作者: Minsker, Stanislav; Zhao, Ying-Qi; Cheng, Guang
• 署名单位: University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1066682
• 发表日期: 2016
• 页码: 875-887
• 关键词: enrichment designs; rates

摘要

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.