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：我们提出了一种正式的统计设计方法，允许在剂量探索试验中同时入组主队列和回填队列患者。该设计的目的是在不同剂量水平积累更多信息以促进剂量优化。所提出的设计称为Bi3+3，它结合了i3+3设计中的简单剂量递增算法与基于决策概率框架的模型推理（两者均为已有文献发表的方法）。因此，Bi3+3提供了一种简洁的算法，当试验中较低剂量在患者中展现出安全性特征时，可将患者回填至这些剂量水平。POD框架允许在部分回填患者尚未完成毒性结局随访时做出剂量决策，从而有望加速临床试验进程。试验结束时，Bi3+3同时利用毒性和疗效结局来估计最佳生物学剂量。该推理基于考虑单调或平台型剂量-疗效关系的剂量反应模型，这些关系在现代肿瘤药物开发中十分常见。模拟研究显示，与现有设计相比，Bi3+3设计具有更优的操作特性。