论文标题
针对靶向疗法的双重剂量优化的贝叶斯设计
A Bayesian design for dual-agent dose optimization with targeted therapies
论文作者
论文摘要
在本文中，我们在两个阶段提出了I-II期设计，以结合靶向靶向疗法。该设计的激励是由已发表的案例研究结合在一起，该案例研究结合了MEK和PIK3CA抑制剂。较高剂量水平不一定会转化为较高功效反应的环境。因此，目标是通过预先指定的理想风险效果折衷的剂量组合确定剂量组合。我们提出了一个灵活的立方样条，以模拟功效响应的边际分布。在第一阶段，通过过量控制（EWOC）原理升级后，将患者分配，而在II期中，我们根据不断更新的模型参数将患者自适应地随机随机随机地将患者随机性随机性地随机化。提出了一项仿真研究，以评估设计在不同情况下的性能，并评估其对样本量的敏感性并模拟错误指定。与最近发表的生物药物的剂量发现算法相比，我们的设计在鉴定最佳剂量组合方面更安全，更有效。
In this article, we propose a phase I-II design in two stages for the combination of molecularly targeted therapies. The design is motivated by a published case study that combines a MEK and a PIK3CA inhibitors; a setting in which higher dose levels do not necessarily translate into higher efficacy responses. The goal is therefore to identify dose combination(s) with a prespecified desirable risk-benefit trade-off. We propose a flexible cubic spline to model the marginal distribution of the efficacy response. In stage I, patients are allocated following the escalation with overdose control (EWOC) principle whereas, in stage II, we adaptively randomize patients to the available experimental dose combinations based on the continuously updated model parameters. A simulation study is presented to assess the design's performance under different scenarios, as well as to evaluate its sensitivity to the sample size and to model misspecification. Compared to a recently published dose finding algorithm for biologic drugs, our design is safer and more efficient at identifying optimal dose combinations.
