MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Stat Methods Med Res. 2021 May;30(5):1273-1287. doi: 10.1177/0962280221995961. Epub 2021 Mar 10.
Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.
贝叶斯自适应随机化是一种启发式方法，旨在根据试验中累积数据的趋势，将更多患者随机分配到据称更优的治疗组。这种方法的许多统计方面已经得到了探讨，并与其他方法进行了比较；然而，只有有限的工作集中于改进其性能，并为其在实际试验中的应用提供指导。这种方法的一个不理想的特性是，在某些情况下，该程序将随机分配患者到较差的治疗组，这在其应用中引起了关注。在这里，我们提出了一种自适应裁剪方法，通过结合一个数据驱动的函数来纠正这个问题，该函数旨在最小化在试验早期将患者分配到较差治疗组的机会。此外，我们提出了一种实用方法来促进随机化程序的选择。我们将反映在试验中为较差治疗组（s）分配患者的惩罚的成本与试验内和试验外所有受益于试验的患者一起纳入我们的效用函数中。我们展示了广泛场景下的选择策略。
