Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P. R. China.
Biom J. 2022 Oct;64(7):1192-1206. doi: 10.1002/bimj.202100297. Epub 2022 May 17.
Biomarker-guided phase II trials have become increasingly important for personalized cancer treatment. In this paper, we propose a Bayesian two-stage sequential enrichment design for such biomarker-guided trials. We assumed that all patients were dichotomized as marker positive or marker negative based on their biomarker status; the positive patients were considered more likely to respond to the targeted drug. Early stopping rules and adaptive randomization methods were embedded in the design to control the number of patients receiving inferior treatment. At the same time, a Bayesian hierarchical model was used to borrow information between the positive and negative control arms to improve efficiency. Simulation results showed that the proposed design achieved higher empirical power while controlling the type I error and assigned more patients to the superior treatment arms. The operating characteristics suggested that the design has good performance and may be useful for biomarker-guided phase II trials for evaluating anticancer therapies.
生物标志物指导的 II 期临床试验在癌症个体化治疗中变得越来越重要。本文提出了一种基于贝叶斯两阶段序贯富集设计的方法。我们假设所有患者根据其生物标志物状态分为标志物阳性或标志物阴性；阳性患者被认为更有可能对靶向药物有反应。该设计中嵌入了提前终止规则和适应性随机化方法，以控制接受较差治疗的患者数量。同时，使用贝叶斯层次模型在阳性和阴性对照组之间借用信息以提高效率。模拟结果表明，该设计在控制 I 类错误的同时实现了更高的经验效力，并将更多的患者分配到了优势治疗组。操作特征表明，该设计具有良好的性能，可用于评估抗癌治疗的生物标志物指导的 II 期临床试验。
