Active Clinical Trials for Personalized Medicine.

TitleActive Clinical Trials for Personalized Medicine.
Publication TypeJournal Article
Year of Publication2016
AuthorsMinsker, Stanislav, Ying-Qi Zhao, and Guang Cheng
JournalJ Am Stat Assoc
Date Published2016

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.

Alternate JournalJ Am Stat Assoc
Original PublicationActive clinical trials for personalized medicine.
PubMed ID28018014
PubMed Central IDPMC5179145
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
U10 CA180819 / CA / NCI NIH HHS / United States