Department of Biostatistics and Informatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA.
Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.
Stat Methods Med Res. 2021 Mar;30(3):785-798. doi: 10.1177/0962280220975187. Epub 2020 Dec 2.
Breakthroughs in cancer biology have defined new research programs emphasizing the development of therapies that target specific pathways in tumor cells. Innovations in clinical trial design have followed with master protocols defined by inclusive eligibility criteria and evaluations of multiple therapies and/or histologies. Consequently, characterization of subpopulation heterogeneity has become central to the formulation and selection of a study design. However, this transition to master protocols has led to challenges in identifying the optimal trial design and proper calibration of hyperparameters. We often evaluate a range of null and alternative scenarios; however, there has been little guidance on how to synthesize the potentially disparate recommendations for what may be optimal. This may lead to the selection of suboptimal designs and statistical methods that do not fully accommodate the subpopulation heterogeneity. This article proposes novel optimization criteria for calibrating and evaluating candidate statistical designs of master protocols in the presence of the potential for treatment effect heterogeneity among enrolled patient subpopulations. The framework is applied to demonstrate the statistical properties of conventional study designs when treatments offer heterogeneous benefit as well as identify optimal designs devised to monitor the potential for heterogeneity among patients with differing clinical indications using Bayesian modeling.
癌症生物学的突破定义了新的研究计划，强调开发针对肿瘤细胞特定途径的疗法。临床试验设计的创新也随之而来，采用包容性纳入标准和对多种疗法和/或组织学进行评估来定义主方案。因此，亚群异质性的特征已成为制定和选择研究设计的核心。然而，这种向主方案的转变导致在确定最佳试验设计和超参数正确校准方面面临挑战。我们经常评估一系列零假设和替代假设情景；然而，关于如何综合潜在的最佳建议，以确定最佳方案，几乎没有指导。这可能导致选择次优设计和统计方法，这些设计和方法不能充分适应亚群异质性。本文提出了在纳入患者亚群中存在治疗效果异质性的情况下，校准和评估主方案候选统计设计的新优化标准。该框架用于展示常规研究设计在治疗具有异质性获益时的统计特性，并确定旨在监测具有不同临床指征的患者之间潜在异质性的最佳设计，使用贝叶斯建模。
