Evidence map›Paper›PMID 33715307›Full record

ArticleCPT: pharmacometrics & systems pharmacology2021

Approaches to handling missing or "problematic" pharmacology data: Pharmacokinetics.

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

Abstract readComparative Study
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Donald J IrbyDivision of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH, USA.
Mustafa E IbrahimDepartment of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Anees M DaukiDivision of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH, USA.
Mohamed A BadawiDivision of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH, USA.
Sílvia M IllamolaDepartment of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA.
Mingqing ChenDivision of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH, USA.
Yuhuan WangDivision of Clinical Pharmacology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA.
Xiaoxi LiuDivision of Clinical Pharmacology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA.
Mitch A PhelpsDivision of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH, USA.
Diane R MouldDivision of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH, USA.

Funding

The Chesapeake-Ohio Pharmacokinetics Core for The ETCTNU24CA247648 · NCI · JOHNS HOPKINS UNIVERSITY · PI Jan Hendrik Beumer, Mitch A Phelps · 2020 to 2026
$3.6M
Overcoming IMiD resistance in MyelomaR01CA201382 · NCI · OHIO STATE UNIVERSITY · PI HOFMEISTER, CRAIG C., PHELPS, MITCH A · 2016 to 2020
$2.6M
NCI NIH HHS R01 CA201382NCI NIH HHS U24 CA247648
6 · The paper itself

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.

Indexed as

AdultAgedBiasClinical Trials as TopicComputer SimulationDrug StabilityFemaleHumansMaleMiddle AgedModels, BiologicalModels, StatisticalPatient CompliancePharmacokineticsPharmacologySelection Bias

Identifiers

PMID33715307
PMCPMC8099444

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.