Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, USA.
Department of Statistics, Florida State University, Tallahassee, FL, USA.
Clin Trials. 2021 Dec;18(6):673-680. doi: 10.1177/17407745211052486. Epub 2021 Oct 24.
Contemporary Phase I oncology trials often include efficacy expansion in various tumor indications post dose finding. Preliminary anti-tumor activity from efficacy expansion can aid Go/No-Go decision for Phase 2 or Phase 3 initiation. Tumor cohorts in efficacy expansion are commonly analyzed independently in practice, which are often underpowered due to small sample size. Pooled analysis is also sometimes conducted, but it ignores the heterogeneity of the anti-tumor activity across cohorts.
We propose an optimal one-stage design and analysis strategy for the efficacy expansion to assess whether the treatment is effective. Allowing heterogeneous anti-tumor effects across tumor cohorts, inactive cohorts are pruned, and the potentially active cohorts are pooled together to gain study power. For a prospective design with a target power, the total sample size across all cohorts is minimized; or for an ad hoc analysis with pre-specified sample size for each cohort, the pruning criteria are optimized to achieve maximum power. The global type I error is controlled after proper multiplicity adjustment, and a penalty adjusted significance level is used for the pooled test.
Simulation studies show that the proposed optimal design has desirable operating characteristics in increasing the overall power and detecting more true positive tumor cohorts.
The proposed optimal design and analysis strategy provides a practical approach to design and analyze heterogeneous efficacy expansion cohorts in a basket setting with global type I and type II error being controlled.
当代肿瘤学 I 期临床试验通常在确定剂量后在各种肿瘤适应证中进行疗效扩展。来自疗效扩展的初步抗肿瘤活性可以辅助 2 期或 3 期启动的 Go/No-Go 决策。在实践中，疗效扩展中的肿瘤队列通常独立进行分析，但由于样本量小，通常效力不足。有时也会进行汇总分析，但它忽略了队列间抗肿瘤活性的异质性。
我们提出了一种用于疗效扩展的最优单阶段设计和分析策略，以评估治疗是否有效。允许肿瘤队列之间存在异质性抗肿瘤作用，剔除无活性的队列，并将潜在的活性队列汇集在一起以获得研究效力。对于具有目标效力的前瞻性设计，最小化所有队列的总样本量；对于具有每个队列预先指定样本量的特定分析，优化剔除标准以获得最大效力。适当的多重调整后控制总体 I 类错误，并使用调整后的显著性水平进行汇总检验。
模拟研究表明，所提出的最优设计在提高总体效力和检测更多真实阳性肿瘤队列方面具有理想的操作特征。
所提出的最优设计和分析策略提供了一种实用的方法，可用于设计和分析篮子设置中具有总体 I 类和 II 类错误控制的异质性疗效扩展队列。
