多臂强盗的应用在剂量确定临床设计中
1Kyowa Kirin Co., Ltd, Japan; The Institute of Statistical Mathematics, Japan.
Artificial intelligence in medicine
|December 2, 2023
概括
本研究探讨了用于优化剂量定位临床试验的多武器强盗算法. 使用后置模式和规范普森采样的新方法在有效识别最大耐受剂量方面显示出希望.
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:
- 剂量确定试验的目的是有效地确定最大耐受剂量 (MTD).
- 传统的方法可能很慢,需要很多参与者.
- 多武器强盗算法为适应性决策提供了一个框架.
研究的目的:
- 适应多臂强盗算法用于剂量确定临床试验.
- 为了评估普森采样,规范普森采样和贪算法.
- 提出和评估一种基于后方模式的新型剂量确定方法.
主要方法:
- 在9个不同的场景中进行了模拟研究.
- 评估的算法包括普森采样,规范普森采样和贪的方法.
- 开发并测试了一种利用后置模式选择剂量的新方法.
主要成果:
- 普森采样在剂量确定研究中典型的小样本大小中表现出很高的变化.
- 规范的普森采样和贪的算法证明了性能的提高.
- 拟议的后置模式方法在模拟中显示出具有竞争力和有前途的结果.
结论:
- 多武器强盗方法,特别是有规范化或基于模式的选择,对于剂量确定是有效的.
- 这些适应性方法有可能提高早期临床试验的效率.
- 对基于后置模式的设计进行进一步的研究有必要进行临床应用.
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