Approaches to handling missing or “problematic” pharmacology data: Pharmacokinetics
Abstract 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 inf...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-04-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12611 |
| _version_ | 1819091602881118208 |
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| author | Donald J. Irby Mustafa E. Ibrahim Anees M. Dauki Mohamed A. Badawi Sílvia M. Illamola Mingqing Chen Yuhuan Wang Xiaoxi Liu Mitch A. Phelps Diane R. Mould |
| author_facet | Donald J. Irby Mustafa E. Ibrahim Anees M. Dauki Mohamed A. Badawi Sílvia M. Illamola Mingqing Chen Yuhuan Wang Xiaoxi Liu Mitch A. Phelps Diane R. Mould |
| author_sort | Donald J. Irby |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-21T22:42:20Z |
| format | Article |
| id | doaj.art-4bf8b31dc4b54808be5ad450089a9a56 |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-12-21T22:42:20Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-4bf8b31dc4b54808be5ad450089a9a562022-12-21T18:47:49ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062021-04-0110429130810.1002/psp4.12611Approaches to handling missing or “problematic” pharmacology data: PharmacokineticsDonald J. Irby0Mustafa E. Ibrahim1Anees M. Dauki2Mohamed A. Badawi3Sílvia M. Illamola4Mingqing Chen5Yuhuan Wang6Xiaoxi Liu7Mitch A. Phelps8Diane R. Mould9Division of Pharmaceutics and Pharmacology College of Pharmacy The Ohio State University Columbus OH USADepartment of Pharmaceutical Sciences Skaggs School of Pharmacy and Pharmaceutical Sciences University of Colorado Anschutz Medical Campus Aurora CO USADivision of Pharmaceutics and Pharmacology College of Pharmacy The Ohio State University Columbus OH USADivision of Pharmaceutics and Pharmacology College of Pharmacy The Ohio State University Columbus OH USADepartment of Experimental and Clinical Pharmacology College of Pharmacy University of Minnesota Minneapolis MN USADivision of Pharmaceutics and Pharmacology College of Pharmacy The Ohio State University Columbus OH USADivision of Clinical Pharmacology Department of Pediatrics University of Utah School of Medicine Salt Lake City UT USADivision of Clinical Pharmacology Department of Pediatrics University of Utah School of Medicine Salt Lake City UT USADivision of Pharmaceutics and Pharmacology College of Pharmacy The Ohio State University Columbus OH USAProjections Research Inc Phoenixville PA USAAbstract 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.https://doi.org/10.1002/psp4.12611 |
| spellingShingle | Donald J. Irby Mustafa E. Ibrahim Anees M. Dauki Mohamed A. Badawi Sílvia M. Illamola Mingqing Chen Yuhuan Wang Xiaoxi Liu Mitch A. Phelps Diane R. Mould Approaches to handling missing or “problematic” pharmacology data: Pharmacokinetics CPT: Pharmacometrics & Systems Pharmacology |
| title | Approaches to handling missing or “problematic” pharmacology data: Pharmacokinetics |
| title_full | Approaches to handling missing or “problematic” pharmacology data: Pharmacokinetics |
| title_fullStr | Approaches to handling missing or “problematic” pharmacology data: Pharmacokinetics |
| title_full_unstemmed | Approaches to handling missing or “problematic” pharmacology data: Pharmacokinetics |
| title_short | Approaches to handling missing or “problematic” pharmacology data: Pharmacokinetics |
| title_sort | approaches to handling missing or problematic pharmacology data pharmacokinetics |
| url | https://doi.org/10.1002/psp4.12611 |
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