在基于模型的剂量检测临床试验设计中包括共变体
Adrien Ollier1, Pavel Mozgunov1
1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Statistics in medicine
|January 24, 2025
概括
在基于模型的I期剂量检测研究中纳入患者共变量可以提高安全性. 忽略预后共变量显著减少了正确的剂量选择,并增加了过量服用的风险.
科学领域:
- 临床药理学 临床药理学
- 生物统计学 生物统计学
- 药物开发 药物开发
背景情况:
- 基于模型的设计越来越多地用于I期剂量检测研究.
- 当前的方法往往假定患者有统一的毒性风险,忽视个体特征.
- 在临床试验中,患者的共同变量可以显著影响剂量-毒性关系.
研究的目的:
- 评估在基于模型的I期剂量确定中包含或省略患者共变量的影响.
- 评估对目标剂量建议和患者安全的影响.
- 建议改进统计模型,用于协变体的纳入.
主要方法:
- 研究了几种可变惩罚标准,用于共变量选择.
- 模拟的I期剂量检测场景与连续和二进制共变量.
- 提出并评估了贝叶斯逻辑回归模型 (BLRM),使用贝叶斯 LASSO 和 Spike-and-Slab priors.
主要成果:
- 忽略预后共变量导致正确选择的比例较低,并增加了过量服用.
- 包括共变量通常会改善操作特性,但可能会略微减少正确的选择.
- 使用贝叶斯 LASSO 和 Spike-and-Slab 的 BLRM 在处理变量包含方面表现出卓越的性能.
结论:
- 在I期试验中,患者共变量对于准确的剂量发现和安全至关重要.
- 当共变量对毒性产生影响时,标准方法可能是不理想的.
- 拟议的贝叶斯模型提供了一个可靠的方法,用于对共变量进行调整的剂量升级.
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