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Titolo:
Selecting reliable pharmacokinetic data for explanatory analyses of clinical trials in the presence of possible noncompliance
Autore:
Lu, JF; Gries, JM; Verotta, D; Sheiner, LB;
Indirizzi:
Univ Calif San Francisco, Sch Pharm, Dept Biopharmaceut Sci, San Francisco, CA 94143 USA Univ Calif San Francisco San Francisco CA USA 94143 ancisco, CA 94143 USA Univ Calif San Francisco, Sch Med, Dept Epidemiol & Biostat, San Francisco, CA 94143 USA Univ Calif San Francisco San Francisco CA USA 94143 ancisco, CA 94143 USA Univ Calif San Francisco, Sch Med, Dept Lab Med, San Francisco, CA 94143 USA Univ Calif San Francisco San Francisco CA USA 94143 ancisco, CA 94143 USA
Titolo Testata:
JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS
fascicolo: 4, volume: 28, anno: 2001,
pagine: 343 - 362
SICI:
1567-567X(200108)28:4<343:SRPDFE>2.0.ZU;2-M
Fonte:
ISI
Lingua:
ENG
Soggetto:
INSTRUMENTAL VARIABLES; IDENTIFICATION; THERAPY;
Keywords:
Bayes estimates; limit of quantification; NONMEM; compliance; measurement error; population PK-PD;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
16
Recensione:
Indirizzi per estratti:
Indirizzo: Sheiner, LB Univ Calif San Francisco, Sch Pharm, Dept Biopharmaceut Sci, Box 0626, SanFrancisco, CA 94143 USA Univ Calif San Francisco Box 0626 San Francisco CA USA 94143 A
Citazione:
J.F. Lu et al., "Selecting reliable pharmacokinetic data for explanatory analyses of clinical trials in the presence of possible noncompliance", J PHARMA PH, 28(4), 2001, pp. 343-362

Abstract

For single-dose concentration-time data collected in clinical trials to beuseful 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) tinder three mutually exclusiveevents: 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 inaddition 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.

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Documento generato il 18/01/20 alle ore 07:37:23