Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Novartis Pharma AG, Basel, Switzerland.
BMC Med Res Methodol. 2021 Apr 14;21(1):69. doi: 10.1186/s12874-021-01218-9.
Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial.
Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing schedules to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model.
In a simulation study, the developed approach is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-predictive prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example in oncology is publicly available ( https://github.com/gunhanb/TITEPK_sequential ).
In phase I oncology trials with sequential multiple schedules, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.
肿瘤学中传统的 I 期剂量递增试验方法仅基于单一治疗方案。然而，最近在同一试验中更频繁地研究了多种方案。
在这里，我们考虑序贯 I 期试验，一旦完成另一种方案的剂量递增，试验就会采用新的方案（例如每日或每周给药）。目的是利用已完成和正在进行的方案的信息来为下一个剂量组的剂量水平提供决策依据。为此，我们采用了时间事件药代动力学（TITE-PK）模型，该模型最初是为同时研究多种方案而开发的。TITE-PK 使用药代动力学（PK）模型整合来自多个方案的信息。
在一项模拟研究中，使用基于荟萃分析预测的先验方法，将开发的方法与桥接连续再评估方法和贝叶斯逻辑回归模型进行比较。在大多数情况下，与比较方法相比，TITE-PK 在推荐可接受剂量和避免过度毒性剂量方面在序贯 I 期试验中表现更好。此外，在模拟试验中需要类似数量的患者时，TITE-PK 可以获得更好的性能。对于涉及一种方案的情况，TITE-PK 在可接受剂量推荐方面与替代方案具有相似的性能。在肿瘤学中实施说明性序贯 I 期试验示例的 R 和 Stan 代码可在公共平台上获取（https://github.com/gunhanb/TITEPK_sequential）。
在具有序贯多种方案的肿瘤学 I 期试验中，充分利用所有相关信息非常重要。对于这些试验，建议使用结合 PK 原理的 TITE-PK，以组合信息。
