Open AccessHandling Missing Dosing History in Population Pharmacokinetic Modeling: An Extension to MDM Method
Author(s) -
Wang Yuhuan,
Liu Xiaoxi
Publication year - 2019
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12374
Subject(s) - dosing , missing data , initialization , computer science , robustness (evolution) , population , pharmacokinetics , statistics , medicine , mathematics , pharmacology , machine learning , biology , biochemistry , environmental health , gene , programming language
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 ( MDM 2) 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 MDM 2 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.