标题： 在曲线无关的贝叶斯决策论剂量寻找试验设计中平衡不同剂量下的先验有效样本量 显示英文标题

标题： Balancing the effective sample size in prior across different doses in the curve-free Bayesian decision-theoretic design for dose-finding trials

摘要： 一期临床试验中药物分配的主要目标是在最小化患者接触亚治疗剂量或毒性过高的剂量的同时，尽可能准确地推荐一个接近最大耐受剂量（MTD）的二期剂量。 范等人 （2012年）引入了一种无曲线贝叶斯决策论设计（CFBD），该设计利用了剂量-毒性关系单调性的假设，而无需直接对剂量-毒性曲线进行建模。 这种方法也被扩展到药物组合以确定MTD （李等人，2017年）。 尽管CFBD通过使用更少的患者展示了改进的试验效率，同时在识别MTD方面保持了高准确性，但它可能会人为地增加更新先验分布的有效样本量，尤其是在最低和最高剂量水平。 这可能导致剂量过高或过低。 本文提出了一种修改CFBD先验分布更新的方法，以平衡不同剂量的有效样本量。 模拟结果显示，使用修改后的先验规格后，CFBD在MTD处实现了更集中的剂量分配，并提供了更精确的剂量建议，平均需要更少的患者。 它还展示了对文献中其他著名的剂量寻找设计的鲁棒性。 显示英文摘要

摘要： 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.