Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York.
Medical Oncology, Northwell Health Cancer Institute, New Hyde Park, New York.
Stat Med. 2024 Dec 20;43(29):5534-5547. doi: 10.1002/sim.10264. Epub 2024 Oct 31.
Broadening eligibility criteria in cancer trials has been advocated to represent the intended patient population more accurately. The advantages are clear in terms of generalizability and recruitment, however there are some important considerations in terms of design for efficiency and patient safety. While toxicity may be expected to be homogeneous across these subpopulations, designs should be able to recommend safe and precise doses if subpopulations with different toxicity profiles exist. Dose-finding designs accounting for patient heterogeneity have been proposed, but existing methods assume that the source of heterogeneity is known. We propose a broadened eligibility dose-finding design to address the situation of unknown patient heterogeneity in phase I cancer clinical trials where eligibility is expanded, and multiple eligibility criteria could potentially lead to different optimal doses for patient subgroups. The design offers a two-in-one approach to dose-finding by simultaneously selecting patient criteria that differentiate the maximum tolerated dose (MTD), using stochastic search variable selection, and recommending the subpopulation-specific MTD if needed. Our simulation study compares the proposed design to the naive approach of assuming patient homogeneity and demonstrates favorable operating characteristics across a wide range of scenarios, allocating patients more often to their true MTD during the trial, recommending more than one MTD when needed, and identifying criteria that differentiate the patient population. The proposed design highlights the advantages of adding more variability at an early stage and demonstrates how assuming patient homogeneity can lead to unsafe or sub-therapeutic dose recommendations.
扩大癌症试验的纳入标准已被提倡,以更准确地代表预期的患者人群。从推广性和招募的角度来看,这具有明显的优势,但在设计方面,还需要考虑到效率和患者安全性的一些重要因素。虽然这些亚人群中的毒性可能是同质的,但如果存在毒性特征不同的亚人群,设计应该能够推荐安全和精确的剂量。已经提出了考虑患者异质性的剂量发现设计,但现有方法假设异质性的来源是已知的。我们提出了一种扩大纳入标准的剂量发现设计,以解决在扩大纳入标准的 I 期癌症临床试验中未知患者异质性的情况,其中多个纳入标准可能导致不同的亚组患者最佳剂量。该设计提供了一种二合一的方法来进行剂量发现,同时使用随机搜索变量选择来选择区分最大耐受剂量 (MTD) 的患者标准,并在需要时推荐特定于亚组的 MTD。我们的模拟研究将所提出的设计与假设患者同质性的简单方法进行了比较,并在广泛的场景中展示了良好的操作特性,即在试验期间更频繁地将患者分配到其真实的 MTD,在需要时推荐多个 MTD,并识别区分患者人群的标准。该设计强调了在早期增加更多变异性的优势,并说明了假设患者同质性如何会导致不安全或低于治疗剂量的建议。