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Titolo:
Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling
Autore:
Bennett, J; Wakefield, J;
Indirizzi:
Univ London Imperial Coll Sci Technol & Med, Dept Epidemiol & Publ Hlth, London, England Univ London Imperial Coll Sci Technol & Med London England don, England Univ Washington, Dept Stat, Seattle, WA 98195 USA Univ Washington SeattleWA USA 98195 on, Dept Stat, Seattle, WA 98195 USA Univ Washington, Dept Biostat, Seattle, WA 98195 USA Univ Washington Seattle WA USA 98195 Dept Biostat, Seattle, WA 98195 USA
Titolo Testata:
BIOMETRICS
fascicolo: 3, volume: 57, anno: 2001,
pagine: 803 - 812
SICI:
0006-341X(200109)57:3<803:EIJPPM>2.0.ZU;2-A
Fonte:
ISI
Lingua:
ENG
Soggetto:
PHARMACOKINETIC-PHARMACODYNAMIC MODELS; MIXED-EFFECTS MODELS;
Keywords:
errors-in-variables; feedback; hierarchical models; measurement error; predictive distributions; prior information;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Life Sciences
Citazioni:
28
Recensione:
Indirizzi per estratti:
Indirizzo: Bennett, J Univ London Imperial Coll Sci Technol & Med, Dept Epidemiol & Publ Hlth, St Marys Campus, London, England Univ London Imperial Coll Sci Technol & Med St Marys Campus London England
Citazione:
J. Bennett e J. Wakefield, "Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling", BIOMETRICS, 57(3), 2001, pp. 803-812

Abstract

Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood asa function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response. Population PK/PD studies aim to determine the sources of variability in the observed concentrations/responses across groups of individuals. In this article, we consider the joint modeling of PK/PD data. The natural approach is to specify a joint model in which the concentration and response data are simultaneously modeled. Unfortunately, this approach may not Lie optimal if, due to sparsity of concentration data, all overly simple PK model is specified. As all alternative, we propose all errors-in-variables approach in which the observed-concentration data are assumed to be measured with error without reference to a specific PK model. We give all example of all analysis of PK/PD data obtained following administration of all anticoagulant drug. The study was originally carried out in order to make dosage recommendations. The prior for the distribution of the true concentrations, which may incorporate all individual's covariate information, is derived as a predictive distribution from all earlier study. The errors-in-variables approach is compared with the joint modeling approach and more naive methods inwhich the observed concentrations, or the separately modeled concentrations, are substituted into the response model. Throughout, a Bayesian approachis taken with implementation via Markov chain Monte Carlo methods.

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Documento generato il 17/01/21 alle ore 17:36:48