Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Anschutz Medical Campus, Aurora, CO, United States of America.
Department of Quantitative Health Sciences & Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, United States of America.
PLoS One. 2022 Aug 2;17(8):e0272367. doi: 10.1371/journal.pone.0272367. eCollection 2022.
This article discusses and compares statistical designs of basket trial, from both frequentist and Bayesian perspectives. Baskets trials are used in oncology to study interventions that are developed to target a specific feature (often genetic alteration or immune phenotype) that is observed across multiple tissue types and/or tumor histologies. Patient heterogeneity has become pivotal to the development of non-cytotoxic treatment strategies. Treatment targets are often rare and exist among several histologies, making prospective clinical inquiry challenging for individual tumor types. More generally, basket trials are a type of master protocol often used for label expansion. Master protocol is used to refer to designs that accommodates multiple targets, multiple treatments, or both within one overarching protocol. For the purpose of making sequential decisions about treatment futility, Simon's two-stage design is often embedded within master protocols. In basket trials, this frequentist design is often applied to independent evaluations of tumor histologies and/or indications. In the tumor agnostic setting, rarer indications may fail to reach the sample size needed for even the first evaluation for futility. With recent innovations in Bayesian methods, it is possible to evaluate for futility with smaller sample sizes, even for rarer indications. Novel Bayesian methodology for a sequential basket trial design based on predictive probability is introduced. The Bayesian predictive probability designs allow interim analyses with any desired frequency, including continual assessments after each patient observed. The sequential design is compared with and without Bayesian methods for sharing information among a collection of discrete, and potentially non-exchangeable tumor types. Bayesian designs are compared with Simon's two-stage minimax design.
本文从频率主义和贝叶斯的角度讨论和比较了篮子试验的统计设计。篮子试验用于肿瘤学研究，以研究针对特定特征（通常是遗传改变或免疫表型）的干预措施，这些特征在多种组织类型和/或肿瘤组织学中都有观察到。患者异质性已成为开发非细胞毒性治疗策略的关键。治疗靶点通常很少见，存在于几种组织学中，使得针对个体肿瘤类型的前瞻性临床研究具有挑战性。更一般地说，篮子试验是一种常用于标签扩展的主方案。主方案用于指在一个总体方案中容纳多个靶点、多种治疗方法或两者的设计。为了对治疗无效性进行连续决策，Simon 的两阶段设计通常嵌入主方案中。在篮子试验中，这种频率主义设计通常应用于肿瘤组织学和/或适应症的独立评估。在肿瘤不可知的情况下，罕见的适应症可能无法达到甚至首次评估无效所需的样本量。随着贝叶斯方法的最新创新，即使对于罕见的适应症，也可以使用更小的样本量来评估无效性。本文介绍了一种基于预测概率的用于序贯篮子试验设计的新贝叶斯方法。贝叶斯预测概率设计允许以任何所需的频率进行中期分析，包括在每个观察到的患者之后进行连续评估。该序贯设计与不使用贝叶斯方法在一组离散的、可能不可交换的肿瘤类型之间共享信息进行了比较。贝叶斯设计与 Simon 的两阶段最小最大设计进行了比较。
