NDLI: Selecting Reliable Pharmacokinetic Data for Explanatory Analyses of Clinical Trials in the Presence of Possible Noncompliance

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ERRATUM: “Population Pharmacokinetic Modeling of Steady State Carbamazepine Clearance in Children, Adolescents, and Adults,” by David M. Reith, Wayne D. Hooper, John Parke, and Bruce Charles

Journal of Pharmacokinetics and Pharmacodynamics : Volume 28, Issue 3, June 2001

Journal of Pharmacokinetics and Pharmacodynamics : Volume 28, Issue 2, April 2001

Journal of Pharmacokinetics and Pharmacodynamics : Volume 28, Issue 1, February 2001

Journal of Pharmacokinetics and Pharmacodynamics : Volume 27

Journal of Pharmacokinetics and Pharmacodynamics : Volume 26

Journal of Pharmacokinetics and Pharmacodynamics : Volume 25

Selecting Reliable Pharmacokinetic Data for Explanatory Analyses of Clinical Trials in the Presence of Possible Noncompliance

Content Provider	Springer Nature Link
Author	Lu, Jianfeng Gries, Jean Michel Verotta, Davide Sheiner, Lewis B.
Copyright Year	2001
Abstract	For single-dose concentration–time data collected in clinical trials to be useful for explanatory pharmacokinetic (PK) or pharmacokinetic–pharmacodynamic (PK–PD) analyses, the following two assumptions on the data must hold: (i) the times of the concentration (PK) observations are known, and (ii) the patient's recent past dosing history (times and amounts) is known. If either (or both) of these assumptions does not hold, and data analysis proceeds as if it did, biased estimates may result. Assumption (i) usually does hold as study personnel observe and record PK sampling times. Assumption (ii) is a problem when, as is often the case for outpatient studies, one must rely on patient recall for past dosing history. This paper presents a technique to avoid assumption (ii) by identifying for deletion those PK observation occasions likely exhibiting unreliable preceding dose histories. To so identify occasions, a Bayes objective function (posterior density) for the data is maximized in its parameters for each individual. The likelihood factor of this function is a mixture pharmacostatistical model expressing the likelihood of the observed concentration(s) under three mutually exclusive events: the prescribed dose preceding the occasion was not taken at all (NT), the prescribed dose was taken at the specified time (T), or the prescribed dose was taken at an unspecified time (U). Suspect observations are identified as those whose maximum corresponding likelihood component is other than T. The approach as defined here relies on the following assumptions in addition to (i): (ii) population PK (i.e., the distribution of PK parameters in the population being sampled) is known, at least approximately, (iii) PK samples (at least 1 or 2 per occasion) are available, (iv) doses taken are of the stated magnitude, and (v) the drug has a short half-life. Simulations reveal that especially when more than one PK sample is available per study occasion, the methodology chooses a set of PK observations that should perform better in subsequent explanatory analyses, or as a basis for estimating individual PK parameters, than do other simpler methods.
Starting Page	343
Ending Page	362
Page Count	20
File Format	PDF
ISSN	1567567X
Journal	Journal of Pharmacokinetics and Pharmacodynamics
Volume Number	28
Issue Number	4
e-ISSN	15738744
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2001-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Pharmacology/Toxicology Pharmacy Veterinary Medicine Biochemistry Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Pharmacology

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