title: Statistical inference for response adaptive randomization procedures with adjusted optimal allocation proportions.
abstract: Seamless phase II/III clinical trials have attracted increasing attention recently. They mainly use Bayesian response adaptive randomization (RAR) designs. There has been little research into seamless clinical trials using frequentist RAR designs because of the difficulty in performing valid statistical inference following this procedure. The well-designed frequentist RAR designs can target theoretically optimal allocation proportions, and they have explicit asymptotic results. In this paper, we study the asymptotic properties of frequentist RAR designs with adjusted target allocation proportions, and investigate statistical inference for this procedure. The properties of the proposed design provide an important theoretical foundation for advanced seamless clinical trials. Our numerical studies demonstrate that the design is ethical and efficient.
authors.affiliation: a Department of Biostatistics , The University of Texas School of Public Health at Houston , Houston , Texas , USA.
authors.forename: Hongjian
authors.initials: H
authors.lastname: Zhu
mesh_terms: D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic / Q000706:statistics & numerical data*; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation / Q000706:statistics & numerical data*; D006801:Humans; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*
keywords: Adaptive design; asymptotic properties; clinical trial; seamless phase II/III; statistical inference