Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
Université Versailles St Quentin & INSERM U900 STAMPM, Institut Curie, Paris, France.
Biostatistics. 2022 Jul 18;23(3):721-737. doi: 10.1093/biostatistics/kxaa054.
An important tool to evaluate the performance of a dose-finding design is the nonparametric optimal benchmark that provides an upper bound on the performance of a design under a given scenario. A fundamental assumption of the benchmark is that the investigator can arrange doses in a monotonically increasing toxicity order. While the benchmark can be still applied to combination studies in which not all dose combinations can be ordered, it does not account for the uncertainty in the ordering. In this article, we propose a generalization of the benchmark that accounts for this uncertainty and, as a result, provides a sharper upper bound on the performance. The benchmark assesses how probable the occurrence of each ordering is, given the complete information about each patient. The proposed approach can be applied to trials with an arbitrary number of endpoints with discrete or continuous distributions. We illustrate the utility of the benchmark using recently proposed dose-finding designs for Phase I combination trials with a binary toxicity endpoint and Phase I/II combination trials with binary toxicity and continuous efficacy.
评估剂量探索设计性能的一个重要工具是无参数最优基准，它为给定方案下设计的性能提供了上限。基准的一个基本假设是研究者可以按单调递增的毒性顺序安排剂量。虽然基准仍然适用于不能对所有剂量组合进行排序的联合研究，但它没有考虑到排序的不确定性。在本文中，我们提出了一种基准的推广，它考虑到了这种不确定性，从而对性能提供了更精确的上限。该基准根据每个患者的完整信息来评估每种排序发生的可能性。所提出的方法可以应用于具有离散或连续分布的任意数量终点的试验。我们使用最近提出的用于具有二进制毒性终点的 I 期联合试验和具有二进制毒性和连续疗效的 I/II 期联合试验的剂量探索设计来说明基准的效用。
