在药物动力学建模中的量化下限下处理受审查数据的务实方法
Marie Wijk1, Roeland E Wasmann1, Karen R Jacobson2
1Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.
CPT: pharmacometrics & systems pharmacology
|March 11, 2025
概括
在量化极限 (BLQ) 以下处理数据是准确药理动力学分析的关键. 方法M7+为精确但数值不稳定的M3方法提供了稳定可靠的替代方案,特别是在模型开发中.
科学领域:
- 药理动力学 药理动力学
- 制药指标 (Pharmacometrics) 是一个指标.
- 统计建模 统计建模
背景情况:
- 准确估计药理动力学参数需要适当处理低于量化下限 (BLQ) 的数据.
- 基于概率的M3方法是精确的,但可能面临数值收问题.
- 常见的替代方案,如M1 (无视BLQ),M6 (赋值一半LLOQ) 和M7 (赋值零) 并不能完全解释归算数据中的不确定性.
研究的目的:
- 为了比较各种方法的稳定性,偏差和精度来处理BLQ数据在药理动力学建模中.
- 为了评估修改的归算方法 (M6+和M7+),这些方法会增加BLQ数据的附加剩余误差.
- 为了确定处理BLQ数据的M3方法的稳定和准确的替代方案.
主要方法:
- 使用了使用FOCE-I/Laplace估计的NONMEM软件.
- 在真实和模拟数据集上使用并行重试与扰乱的初始估计来评估稳定性.
- 通过使用双分区模型进行随机模拟和估计来评估偏差和精度.
主要成果:
- 在M3方法中,在重复试验中 (±14.7) 的目标功能值 (OFV) 显示不稳定,而M1,M6,M6+和M7+则稳定 (M7不稳定:±130).
- M3表现出最好的偏差和精度 (平均rRMSE18.7%),M6+和M7+的表现相似 (26.0%和23.3%).
- 经过修改的M6+和M7+方法证明了与M3可比的偏差和精度提高,同时保持了稳定性.
结论:
- M3的OFV的数值不稳定性给模型开发带来了挑战.
- 推算方法提供了卓越的稳定性.
- M7+提供了比M6+更简单的实现,并且是M3处理BLQ数据的实用替代方案,特别是在药理动力学模型开发过程中.
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