Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA.
Stat Med. 2024 Dec 10;43(28):5401-5411. doi: 10.1002/sim.10256. Epub 2024 Oct 18.
We propose a phase I/II trial design to support dose-finding when the optimal biological dose (OBD) may differ in two prespecified patient subgroups. The proposed design uses a utility function to quantify efficacy-toxicity trade-offs, and a Bayesian model with spike and slab prior distributions for the subgroup effect on toxicity and efficacy to guide dosing and to facilitate identifying either subgroup-specific OBDs or a common OBD depending on the resulting trial data. In a simulation study, we find the proposed design performs nearly as well as a design that ignores subgroups when the dose-toxicity and dose-efficacy relationships are the same in both subgroups, and nearly as well as a design with independent dose-finding within each subgroup when these relationships differ across subgroups. In other words, the proposed adaptive design performs similarly to the design that would be chosen if investigators possessed foreknowledge about whether the dose-toxicity and/or dose-efficacy relationship differs across two prespecified subgroups. Thus, the proposed design may be effective for OBD selection when uncertainty exists about whether the OBD differs in two prespecified subgroups.
我们提出了一项 I/II 期临床试验设计，以支持在最佳生物学剂量 (OBD) 在两个预先指定的患者亚组中可能不同的情况下进行剂量探索。所提出的设计使用效用函数来量化疗效-毒性权衡，以及具有尖峰和板条先验分布的贝叶斯模型，用于毒性和疗效对亚组的影响，以指导给药，并有助于根据试验数据确定亚组特异性 OBD 或共同 OBD。在一项模拟研究中，我们发现当两个亚组中的剂量-毒性和剂量-疗效关系相同时，所提出的设计几乎与忽略亚组的设计表现相同，而当这些关系在亚组之间不同时，所提出的设计与每个亚组内的独立剂量探索设计表现几乎相同。换句话说，如果研究人员预先知道剂量-毒性和/或剂量-疗效关系是否在两个预先指定的亚组中不同，那么所提出的适应性设计与将选择的设计表现相似。因此，当存在关于 OBD 是否在两个预先指定的亚组中不同的不确定性时，所提出的设计可能对 OBD 选择有效。
