Open AccessApproaches to handling missing or “problematic” pharmacology data: Pharmacokinetics
Author(s) -
Irby Donald J.,
Ibrahim Mustafa E.,
Dauki Anees M.,
Badawi Mohamed A.,
Illamola Sílvia M.,
Chen Mingqing,
Wang Yuhuan,
Liu Xiaoxi,
Phelps Mitch A.,
Mould Diane R.
Publication year - 2021
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/psp4.12611
Subject(s) - missing data , covariate , computer science , set (abstract data type) , data set , data mining , bridge (graph theory) , statistics , medicine , machine learning , mathematics , artificial intelligence , programming language
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.