title: 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 haskpossible treatment strategies (arms)-for example,kpossible 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 thektreatment 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.
authors.affiliation: Department of Statistics, Stanford University, Stanford, California, USA.
authors.forename: Tze Leung
authors.initials: TL
authors.lastname: Lai
authors.affiliation: Department of Statistics, Stanford University, Stanford, California, USA.
authors.forename: Olivia Yueh-Wen
authors.initials: OY
authors.lastname: Liao
keywords: Adaptive allocation; Information bound; Interim analysis; Internal pilot; Multi-armed bandit theory; Multiple treatment