Optimal adaptive allocation using deep reinforcement learning in a dose-response study.

ABSTRACT: Estimation of the dose-response curve for efficacy and subsequent selection of an appropriate dose in phase II trials are important processes in drug development. Various methods have been investigated to estimate dose-response curves. Generally, these methods are used with equal allocation of subjects for simplicity; nevertheless, they may not fully optimize performance metrics because of nonoptimal allocation. Optimal allocation methods, which include adaptive allocation methods, have been proposed to overcome the limitations of equal allocation. However, they rely on asymptotics, and thus sometimes cannot efficiently optimize the performance metric with the sample size in an actual clinical trial. The purpose of this study is to construct an adaptive allocation rule that directly optimi

SUBMITTER: Matsuura K 

PROVIDER: S-EPMC9298337 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature