Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
Biostatistics. 2023 Apr 14;24(2):277-294. doi: 10.1093/biostatistics/kxab028.
Identification of the optimal dose presents a major challenge in drug development with molecularly targeted agents, immunotherapy, as well as chimeric antigen receptor T-cell treatments. By casting dose finding as a Bayesian model selection problem, we propose an adaptive design by simultaneously incorporating the toxicity and efficacy outcomes to select the optimal biological dose (OBD) in phase I/II clinical trials. Without imposing any parametric assumption or shape constraint on the underlying dose-response curves, we specify curve-free models for both the toxicity and efficacy endpoints to determine the OBD. By integrating the observed data across all dose levels, the proposed design is coherent in dose assignment and thus greatly enhances efficiency and accuracy in pinning down the right dose. Not only does our design possess a completely new yet flexible dose-finding framework, but it also has satisfactory and robust performance as demonstrated by extensive simulation studies. In addition, we show that our design enjoys desirable coherence properties, while most of existing phase I/II designs do not. We further extend the design to accommodate late-onset outcomes which are common in immunotherapy. The proposed design is exemplified with a phase I/II clinical trial in chronic lymphocytic leukemia.
在药物开发中，对于分子靶向药物、免疫疗法以及嵌合抗原受体 T 细胞治疗等方法，确定最佳剂量是一个主要的挑战。通过将剂量发现建模为贝叶斯模型选择问题，我们提出了一种自适应设计方案，通过同时结合毒性和疗效结果，在 I/II 期临床试验中选择最佳生物剂量 (OBD)。我们没有对潜在的剂量-反应曲线施加任何参数假设或形状约束，而是为毒性和疗效终点指定了无曲线模型，以确定 OBD。通过整合所有剂量水平的观察数据，所提出的设计在剂量分配上具有一致性，从而大大提高了确定正确剂量的效率和准确性。我们的设计不仅具有全新的、灵活的剂量发现框架，而且在广泛的模拟研究中表现出令人满意和稳健的性能。此外，我们还表明，我们的设计具有理想的一致性特性，而大多数现有的 I/II 期设计则没有。我们进一步将设计扩展到可容纳免疫疗法中常见的迟发结果。该设计通过慢性淋巴细胞白血病的 I/II 期临床试验进行了举例说明。
