In oncology dose-finding trials, due to staggered enrollment, it might be desirable to make dose-assignment decisions in real-time in the presence of pending toxicity outcomes, for example, when the dose-limiting toxicity is late-onset. Patients' time-to-event information may be utilized to facilitate such decisions. We review statistical frameworks for time-to-event modeling in dose-finding trials and summarize existing designs into two classes: TITE designs and POD designs. TITE designs are based on inference on toxicity probabilities, while POD designs are based on inference on dose-finding decisions. These two classes of designs contain existing individual designs as special cases and also give rise to new designs. We discuss and study the theoretical properties of these designs, including large-sample convergence properties, coherence principles, and the underlying decision rules. To facilitate the use of these designs in practice, we introduce efficient computational algorithms and review common practical considerations, such as safety rules and suspension rules. Finally, the operating characteristics of several designs are evaluated and compared through computer simulations.

翻译：在肿瘤剂量探索试验中，由于患者入组时间错开，可能需要在毒副反应结果尚未完全确定的情况下（例如，当剂量限制性毒性表现为迟发性时）实时做出剂量分配决策。可借助患者的至事件发生时间信息来辅助此类决策。我们综述了剂量探索试验中基于至事件发生时间建模的统计框架，并将现有设计归纳为两大类：TITE设计和POD设计。TITE设计基于对毒性概率的推断，而POD设计则基于对剂量探索决策的推断。这两类设计将现有的个体设计作为特例包含在内，并可衍生出新设计。我们讨论并研究了这些设计的理论性质，包括大样本收敛性质、一致性原则及潜在的决策规则。为促进这些设计在实际中的应用，我们引入了高效的计算算法，并总结了常见的实践考量因素，如安全规则和暂停规则。最后，通过计算机模拟评估并比较了多种设计的运作特性。