巴德:一个无的两阶段剂量优化设计,集成补充和适应性随机化
Yixuan Zhao1, Rachael Liu2, Jianchang Lin2
1Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Clinical trials (London, England)
|July 8, 2025
概括
新的回填和适应性随机化剂量优化 (BARD) 设计减少了药物开发的样本大小和试验持续时间. 这种方法改善了最佳剂量识别,并保持了平衡的患者随机化.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 药理学 药理学是指药理学的学科.
背景情况:
- 对于剂量优化而言,两阶段设计是常见的,但需要大样本大小.
- 确定最大耐受剂量是剂量优化中的关键步骤.
研究的目的:
- 引入补充和适应性随机化剂量优化 (BARD) 设计.
- 为了解决传统的两阶段剂量优化设计的样本大小限制.
- 减少临床试验的持续时间,提高最佳剂量识别的准确性.
主要方法:
- BARD的设计将回充整合到第一阶段的剂量升级中.
- 它无地结合了第一阶段和第二阶段的患者,使用共变量适应性随机化.
- 使用模拟研究来评估BARD设计的性能.
主要成果:
- 与传统方法相比,BARD显著减少了样本大小.
- 该设计提高了确定最佳剂量的准确性.
- 在随机化中保持共变量平衡,确保无偏差的剂量比较.
结论:
- 巴德为剂量优化提供了一种高效的解决方案,满足了Project Optimus的要求.
- BARD的设计增强了患者的注册和数据生成.
- 自由可用的软件支持实施BARD设计.
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