Division of Clinical Pharmacology, Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah, USA.
CPT Pharmacometrics Syst Pharmacol. 2019 Jan;8(1):39-49. doi: 10.1002/psp4.12374. Epub 2019 Jan 17.
A major challenge in population pharmacokinetic modeling is handling data with missing or potentially incorrect dosing records. Leaving such records untreated or "commented out" will cause bias in parameter estimates. Several approaches were previously developed to address this challenge. Published in 2004, the missing dose method (MDM) demonstrated its robustness in handling missing dosing history in pharmacokinetic (PK) modeling. In this study, we presented two new extensions: a modified MDM method (MDM2) and a compartment initialization method (CIM). Their performance was examined with a large batch of simulated PK studies. For each method, 8,000 models were run, including different model structures, dosing routes, and missing dosing record scenarios. Both MDM2 and CIM exhibited robust performance and improved parameter estimation results. Specifically, CIM consistently outperformed other methods in fixed-effect and random-effect PK parameter estimation. The new methods demonstrate great potential in addressing missing dosing records challenges in PK analysis.
在群体药代动力学建模中,一个主要的挑战是处理存在缺失或潜在错误剂量记录的数据。不对这些记录进行处理或“注释掉”,将导致参数估计出现偏差。之前已经开发了几种方法来解决这个挑战。发表于 2004 年的缺失剂量方法(MDM)在处理药代动力学(PK)建模中的缺失剂量历史记录方面表现出了稳健性。在这项研究中,我们提出了两种新的扩展:一种改进的 MDM 方法(MDM2)和一种隔室初始化方法(CIM)。我们使用大量模拟 PK 研究来检验它们的性能。对于每种方法,我们运行了 8000 个模型,包括不同的模型结构、给药途径和缺失剂量记录场景。MDM2 和 CIM 都表现出了稳健的性能和改进的参数估计结果。具体来说,CIM 在固定效应和随机效应 PK 参数估计方面始终优于其他方法。这些新方法在解决 PK 分析中缺失剂量记录的挑战方面具有很大的潜力。