|Title||Active Clinical Trials for Personalized Medicine.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Minsker, Stanislav, Ying-Qi Zhao, and Guang Cheng|
|Journal||J Am Stat Assoc|
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 Journal||J Am Stat Assoc|
|Original Publication||Active clinical trials for personalized medicine.|
|PubMed Central ID||PMC5179145|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
U10 CA180819 / CA / NCI NIH HHS / United States
Active Clinical Trials for Personalized Medicine.