评估多个推算算法对药理动力学模型性能的影响:基于模拟的研究
Thomas Duflot1,2, Lucie Fayette3, Céline Konecki4
1Department of Pharmacology, University of Reims Champagne-Ardenne, Reims University Hospital, PPF UR 3801, Reims, France. thomas.duflot@chu-rouen.fr.
多重归算 (MI) 算法有效地处理缺失的药理动力学 (PK) 数据,最高可达20%的缺失. 森林小姐和阿米莉亚在PK建模中显示了连续共变量的前景.
科学领域:
- 药理动力学 药理动力学
- 统计建模 统计建模
- 数据推算数据的计算方法
背景情况:
- 药理动力学 (PK) 研究中缺少的数据可能会导致结果偏差.
- 多重推算 (MI) 是解决缺失数据的常用技术.
- 在PK建模中评估MI算法性能对于可靠的分析至关重要.
研究的目的:
- 比较五个MI算法的性能.
- 评估MI算法的能力,以保存共变量分布和PK参数估计.
- 确定一个口服吸收的单间PK模型的最佳MI算法.
主要方法:
- 在完全随机缺失 (MCAR) 机制下,对四个共变量进行模拟缺失数据 (5-75%).
- 测试了五个MI算法:小鼠,阿米莉亚,森林小姐,rMIDAS,XGBoost.
- 在Monolix2024R1®中使用绝对/相对误差和一致度指标评估性能.
主要成果:
- 森林小姐和阿米莉亚在连续共变量中显示较低的误差;二元变量被认为是差的.
- 小鼠在5%的缺失率表现最好,而森林小姐在20%的缺失率表现出色.
- 增加的失踪率减少了协变效应,增加了个体间的差异,但个体参数估计仍然准确.
结论:
- 在MCAR下,MI方法在PK建模中对共变量归算有效,在MCAR下,缺失率高达20%.
- 建议对高级建模和贝叶斯方法进行进一步研究.
- 了解缺失的数据机制对于强大的 PK 分析至关重要.
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