错误指定的共同变量模型在使用固定效应和完全随机效应模型时对包含和省略偏差的影响
Joakim Nyberg1, E Niclas Jonsson2
1Pharmetheus AB, Kungsängstull 4, Uppsala, 753 19, Sweden. joakim.nyberg@pharmetheus.com.
Journal of pharmacokinetics and pharmacodynamics
|February 22, 2025
概括
在药量测量模型中的范围缩小,当相关共变量被遗漏时,可能会出现遗漏偏差,特别是在固定效应模型中. 无关共变量带来的包含偏差不那么令人担忧,随机效应模型在这种情况下表现更好.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 药理动力学/药理动力学 (PK/PD) 建模
- 统计建模 统计建模
背景情况:
- 识别共变量对于解释药量测量模型中个体间的变异性至关重要,从而使个性化剂量策略成为可能.
- 共变量"范围缩小"涉及将经过测试的关系限制在科学上可信的关系上,这是为了简化模型开发的常见做法.
研究的目的:
- 调查共变量范围减少对错误指定的药量计模型中的参数估计准确度和精度的影响.
- 根据不同的建模方法,评估遗漏偏差 (遗漏相关共变量) 和包含偏差 (包括无关共变量) 的风险.
主要方法:
- 模拟了100个数据集,使用一个单间模型,具有第一阶吸收,变化的参数 (CL,V,Ka) 和体重 (WT) 作为共变量.
- 使用14个固定效应模型 (FEM) 和2个完全随机效应模型 (FREM) 估计的参数,探索各种共同变量-参数关系和IIV相关性.
- 将估计的参数与模拟值进行比较,以评估准确性 (偏差) 和精度.
主要成果:
- 错误指定的FEM表现出对遗漏偏差的敏感性,影响共变系数和个体间可变性 (IIV) 参数.
- 在错误指定的模型中包含不相关的共变量并没有引入显著的包含偏差,估计的影响接近于零.
- 完全随机效应模型 (FREM) 在处理错误指定的共同变量模型时,优于固定效应模型 (FEM).
结论:
- 虽然包含偏差在错误指定的模型中不是一个重大问题,但在共变量范围减少过程中,省略偏差带来了相当大的风险.
- 这些发现强调了仔细选择共变量的重要性,特别是在使用固定效应建模时,以避免参数估计中的偏差.
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