适应性设计,以确定最佳的处理方法,其中前两个是Thompson采样和加速
1GBDS, Bristol Myers Squibb, Boudry, Switzerland.
Pharmaceutical statistics
|August 12, 2023
概括
这项研究引入了新的自适应性临床试验方法,Top-Two Thompson Sampling (TTTS) 和一个更简单的变体 (TTTS2),以有效地确定最佳癌症治疗方法. 这些以机器学习为灵感的方法通过在药物开发的早期阶段最大限度地接触优质疗法来改善患者的治疗结果.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 适应性临床试验对于有效的药物开发至关重要.
- 早期确定最佳治疗臂可以最大限度地提高患者的益处.
- 现有的多武装强盗 (MAB) 方法专注于识别错误率.
研究的目的:
- 将MAB方法适应临床试验环境,优先考虑患者累积益处.
- 引入和评估Top-Two Thompson Sampling (TTTS) 和一种新型变体 (TTTS2),用于药物开发中典型的小样本大小.
- 为了提高适应性设计的性能,在识别优越的治疗中.
主要方法:
- 使用顶部两种普森采样 (TTTS) 和一个简化的变体 (TTTS2).
- 开发了一种针对较小样本规模的TTTS加速方法.
- 进行了广泛的模拟,以评估在典型药物开发场景中的性能.
主要成果:
- TTTS和TTTS2在确定最佳治疗臂方面表现出有效性.
- 提出的方法在小样本设置中表现出强的表现.
- 加速方法在药物开发环境中提高了性能.
结论:
- TTTS和TTTS2是适应性临床试验的有效和实用方法.
- 这些方法提高了优质治疗方法的识别,同时增加了患者的累积益处.
- 拟议的方法为早期药物开发提供了优势,样本规模有限.
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