We consider a formal statistical design that allows simultaneous enrollment of a main cohort and a backfill cohort of patients in a dose-finding trial. The goal is to accumulate more information at various doses to facilitate dose optimization. The proposed design, called Bi3+3, combines the simple dose-escalation algorithm in the i3+3 design and a model-based inference under the framework of probability of decisions (POD), both previously published. As a result, Bi3+3 provides a simple algorithm for backfilling patients to lower doses in a dose-finding trial once these doses exhibit safety profile in patients. The POD framework allows dosing decisions to be made when some backfill patients are still being followed with incomplete toxicity outcomes, thereby potentially expediting the clinical trial. At the end of the trial, Bi3+3 uses both toxicity and efficacy outcomes to estimate an optimal biological dose (OBD). The proposed inference is based on a dose-response model that takes into account either a monotone or plateau dose-efficacy relationship, which are frequently encountered in modern oncology drug development. Simulation studies show promising operating characteristics of the Bi3+3 design in comparison to existing designs.
翻译:我们考虑了一种正式的统计设计,允许在剂量寻找试验中同时招募主要队列和回填队列的患者。目标是在各种剂量下积累更多信息以便于剂量优化。所提出的设计称为Bi3+3,将i3+3设计中的简单剂量递增算法和基于模型的推断相结合,即概率决策(POD)框架,两者均已发表。因此,Bi3+3提供了一种在剂量寻找试验中向更低剂量充填患者的简单算法,一旦这些剂量在患者中显示出安全剖面。POD框架允许在某些回填患者仍在进行不完全毒性结果跟踪时进行给药决策,从而有可能加速临床试验。在试验结束时,Bi3+3使用毒性和疗效结果估计最优生物剂量(OBD)。所提出的推断基于剂量-反应模型,该模型考虑了现代肿瘤学药物开发中经常遇到的单调或平台剂量-效应关系。模拟研究显示Bi3+3设计的操作特征与现有设计相比具有良好的前景。