Duflot Thomas, Fayette Lucie, Konecki Céline, Seurat Jérémy, Feliu Catherine, Scala-Bertola Julien, Djerada Zoubir
Department of Pharmacology, University of Reims Champagne-Ardenne, Reims University Hospital, PPF UR 3801, Reims, France.
Department of Pharmacology, Normandie Univ, UNIROUEN, Inserm U1096, CHU Rouen, CIC-CRB 1404, F- 76000, Rouen, France.
AAPS J. 2025 Apr 17;27(4):77. doi: 10.1208/s12248-025-01066-1.
This study compared multiple imputation (MI) algorithms in a one-compartment pharmacokinetic (PK) scenario with oral absorption. Four covariates (two continuous, two dichotomous) linked to PK parameters were randomly removed under a missing completely at random (MCAR) mechanism. The aim was to identify which algorithm best preserves covariate distributions and PK parameter estimates. The original dataset included 100 individuals, each with five sampling occasions. Missing data were introduced at 5%, 20%, 50%, and 75% for the four covariates under the MCAR assumption. Five MI algorithms (Mice, Amelia, missForest, rMIDAS, XGBoost) were tested. Absolute and relative errors and concordance metrics were used to assess performance. Population and individual parameter estimates were compared across imputed and original datasets using Monolix2024R1®. MissForest (MF) and Amelia yielded lower errors for continuous covariates whereas dichotomous variables were poorly imputed. Based on objective function values, Mice perform best at 5% and MF at 20% of missingness. Increasing missingness decreased covariate effects and increased the estimated inter-individual variances. Individual parameter estimation accurately captured individual-level variability across all imputed datasets. MI methods appear effective for covariate imputation in PK modeling, offering reliable results up to 20% missingness under an MCAR mechanism. Future research should explore refined strategies, including advanced modeling frameworks and Bayesian approaches for imputation. Enhancing our understanding of missing data processes will be crucial for robust PK analyses across diverse clinical settings.
本研究在具有口服吸收的一室药代动力学（PK）场景中比较了多重填补（MI）算法。在完全随机缺失（MCAR）机制下，随机去除与PK参数相关的四个协变量（两个连续变量，两个二分变量）。目的是确定哪种算法能最好地保留协变量分布和PK参数估计值。原始数据集包括100名个体，每人有五个采样时刻。在MCAR假设下，四个协变量分别按5%、20%、50%和75%的比例引入缺失数据。测试了五种MI算法（Mice、Amelia、missForest、rMIDAS、XGBoost）。使用绝对误差、相对误差和一致性指标来评估性能。使用Monolix2024R1®对插补数据集和原始数据集的总体和个体参数估计值进行比较。对于连续协变量，missForest（MF）和Amelia产生的误差较低，而二分变量的插补效果较差。基于目标函数值，Mice在缺失率为5%时表现最佳，MF在缺失率为20%时表现最佳。缺失率增加会降低协变量效应并增加估计的个体间方差。个体参数估计准确地捕捉了所有插补数据集的个体水平变异性。MI方法在PK建模中的协变量插补方面似乎是有效的，在MCAR机制下，缺失率高达20%时能提供可靠的结果。未来的研究应探索改进策略，包括用于插补的先进建模框架和贝叶斯方法。加强我们对缺失数据过程的理解对于跨不同临床环境进行稳健的PK分析至关重要。
