Efficient Adaptive Randomization and Stopping Rules in Multi-arm Clinical Trials for Testing a New Treatment

Abstract

Motivated by applications to confirmatory clinical trials for testing a new treatment against a placebo or active control when the new treatment has k possible treatment strategies (arms)—for example, k possible doses for a new drug—we develop an asymptotic theory for efficient outcome-adaptive randomization schemes and optimal stopping rules. Our approach consists of developing asymptotic lower bounds for the expected sample sizes from the k treatment arms and the control arm and using generalized sequential likelihood ratio procedures to achieve these bounds. Implementation details of our design and analysis and comparative simulation studies are also provided.