Immunotherapy is an innovative treatment approach that harnesses a patient’s immune system to treat cancer. It has provided an alternative and complementary treatment modality to conventional chemotherapy. Combining immunotherapy with cytotoxic chemotherapy agent has become the leading trend and the most active research field in oncology. To accommodate this growing trend, we propose a Bayesian phase I/II dose-finding design to identify the optimal biological dose combination (OBDC), defined as the dose combination with the highest desirability in the risk-benefit trade-off. We propose new statistical models to describe the relationship between the doses and treatment outcomes, including immune response, toxicity and progression-free survival (PFS). During the trial, based on accrued data, we continuously update model estimates and adaptively assign patients to dose combinations with high desirability. The simulation study shows that our design has desirable operating characteristics.

中文翻译：

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结合免疫治疗剂和化疗剂的癌症临床试验贝叶斯 I/II 期设计

免疫疗法是一种创新的治疗方法，它利用患者的免疫系统来治疗癌症。它为传统化疗提供了一种替代和补充的治疗方式。免疫治疗与细胞毒化疗药物相结合已成为肿瘤学的主导趋势和最活跃的研究领域。为了适应这种不断增长的趋势，我们提出了贝叶斯 I/II 期剂量寻找设计来确定最佳生物剂量组合 (OBDC)，定义为在风险-收益权衡中具有最高可取性的剂量组合。我们提出了新的统计模型来描述剂量与治疗结果之间的关系，包括免疫反应、毒性和无进展生存期 (PFS)。在试验期间，根据累积数据，我们不断更新模型估计值，并自适应地将患者分配到具有高合意性的剂量组合。仿真研究表明，我们的设计具有理想的操作特性。