The primary goal of dose allocation in phase I trials is to minimize patient exposure to subtherapeutic or excessively toxic doses, while accurately recommending a phase II dose that is as close as possible to the maximum tolerated dose (MTD). Fan et al. (2012) introduced a curve-free Bayesian decision-theoretic design (CFBD), which leverages the assumption of a monotonic dose-toxicity relationship without directly modeling dose-toxicity curves. This approach has also been extended to drug combinations for determining the MTD (Lee et al., 2017). Although CFBD has demonstrated improved trial efficiency by using fewer patients while maintaining high accuracy in identifying the MTD, it may artificially inflate the effective sample sizes for the updated prior distributions, particularly at the lowest and highest dose levels. This can lead to either overshooting or undershooting the target dose. In this paper, we propose a modification to CFBD's prior distribution updates that balances effective sample sizes across different doses. Simulation results show that with the modified prior specification, CFBD achieves a more focused dose allocation at the MTD and offers more precise dose recommendations with fewer patients on average. It also demonstrates robustness to other well-known dose finding designs in literature.

翻译：一期临床试验中剂量分配的主要目标是最小化患者暴露于亚治疗剂量或过度毒性剂量的风险，同时准确推荐尽可能接近最大耐受剂量（MTD）的二期试验剂量。Fan等人（2012）提出了无曲线贝叶斯决策理论设计（CFBD），该方法利用剂量-毒性关系的单调性假设，而无需直接对剂量-毒性曲线进行建模。此方法亦被扩展至药物组合以确定MTD（Lee等人，2017）。尽管CFBD通过使用更少患者同时保持高MTD识别准确度，已证明能提升试验效率，但它可能人为地增大更新后先验分布的有效样本量，尤其是在最低和最高剂量水平。这可能导致目标剂量的过高或过低估计。本文提出对CFBD先验分布更新过程的改进，以平衡不同剂量间的有效样本量。模拟结果表明，采用改进的先验设定后，CFBD能在MTD处实现更集中的剂量分配，并以更少的平均患者数提供更精确的剂量推荐。该设计亦展现出对文献中其他知名剂量探索方法的鲁棒性。