Most phase I trials in oncology aim to find the maximum tolerated dose (MTD) based on the occurrence of dose limiting toxicities (DLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance. Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose-finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of DLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/PD), whereas the other DLT (DLT_o) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of DLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose-allocation stage of the trial, once all data, including PK/PD measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTD-regimen compared to the recommendation of the dose-allocation method implemented. Our joint approaches can also predict the DLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.

中文翻译：

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评估剂量方案的早期肿瘤学试验的双变量毒性结果的贝叶斯模型

大多数肿瘤学 I 期试验旨在根据剂量限制毒性 (DLT) 的发生情况找到最大耐受剂量 (MTD)。除了剂量外，评估给药方案可能会提高药物耐受性。此外，对于某些分子，双变量毒性终点可能比单一终点更合适。然而，标准剂量发现设计并未考虑同一设计中的多剂量方案和双变量毒性终点。在这种情况下，在 I 期激励试验之后，我们建议使用药代动力学和药效学 (PK/PD) 对第一种 DLT（细胞因子释放综合征）进行建模，而另一种 DLT（DLT _o) 用累积剂量建模。我们开发了三种方法来模拟 DLT 的联合分布，将其定义为两种毒性类型的二元二元结果，在关于毒性之间相关性的各种假设下：独立模型、copula 模型和条件模型。一旦所有数据（包括 PK/PD 测量值）可用，我们开发的贝叶斯方法将在试验的剂量分配阶段结束时应用。通过广泛的模拟研究对这些方法进行了评估，该研究表明，与实施的剂量分配方法的建议相比，它们可以提高选择真正 MTD 方案的性能。