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Missing or erroneous information is a common problem in the analysis of pharmacokinetic (PK) data. This may present as missing or inaccurate dose level or dose time, drug concentrations below the analytical limit of quantification, missing sample times, or missing or incorrect covariate information. Several methods to handle problematic data have been evaluated, although no single, broad set of recommendations for commonly occurring errors has been published. In this tutorial, we review the existing literature and present the results of our simulation studies that evaluated common methods to handle known data errors to bridge the remaining gaps and expand on the existing knowledge. This tutorial is intended for any scientist analyzing a PK data set with missing or apparently erroneous data. The approaches described herein may also be useful for the analysis of nonclinical PK data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]