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首页> 外文期刊>Journal of the royal statistical society >A utility-based Bayesian phase Ⅰ-Ⅱ design for immunotherapy trials with progression-free survival end point
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A utility-based Bayesian phase Ⅰ-Ⅱ design for immunotherapy trials with progression-free survival end point

机译：基于实用程序的贝叶斯Ⅰ-Ⅱ期设计用于无进展生存终点的免疫治疗试验

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Immunotherapy has been hailed as the biggest breakthrough for treating cancer since the first development of chemotherapy. The new features of immunotherapy make the traditional clinical trial paradigm increasingly inefficient and dysfunctional. We propose a Bayesian phase Ⅰ-Ⅱ design for immunotherapy trials called BDFIT to find the optimal biological dose (OBD). We jointly model the toxicity outcome, progression-free survival (PFS) and immune response. PFS and toxicity are used as the primary end points to determine the OBD, whereas the immune response is used as an ancillary end point to screen out futile doses quickly and to predict the PFS when needed. A utility function is formulated to account for the risk-benefit trade-off and to quantify the desirability of the dose. During the trial, based on accumulating data, the estimates of the model and dose desirability are continuously updated and used to guide the dose assignment and to select the OBD. The simulation study shows that the BDFIT design has desirable operating characteristics.
机译：自从化学疗法问世以来，免疫疗法已被誉为治疗癌症的最大突破。免疫疗法的新功能使传统的临床试验范式变得越来越低效且功能失调。我们提出了一种称为BDFIT的免疫治疗试验的贝叶斯Ⅰ-Ⅱ期设计，以寻找最佳生物剂量（OBD）。我们共同模拟毒性结果，无进展生存期（PFS）和免疫反应。 PFS和毒性被用作确定OBD的主要终点，而免疫反应被用作辅助终点，以快速筛选出无效剂量并在需要时预测PFS。制定了效用函数，以说明风险与收益之间的权衡并量化剂量的可取性。在试验过程中，基于积累的数据，将对模型和剂量期望值的估计值进行连续更新，并用于指导剂量分配和选择OBD。仿真研究表明，BDFIT设计具有理想的工作特性。

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