Estimation of population pharmacokinetics using the Gibbs sampler
Identifieur interne : 003431 ( Istex/Corpus ); précédent : 003430; suivant : 003432Estimation of population pharmacokinetics using the Gibbs sampler
Auteurs : G. Best ; C. Tan ; R. Gilks ; J. SpiegelhalterSource :
- Journal of Pharmacokinetics and Biopharmaceutics [ 0090-466X ] ; 1995-08-01.
Abstract
Abstract: Quantification of the average and interindividual variation in pharmacokinetic behavior within the patient population is an important aspect of drug development. Population pharmacokinetic models typically involve large numbers of parameters related nonlinearly to sparse, observational data, which creates difficulties for conventional methods of analysis. The nonlinear mixed-effects method implemented in the computer program NONMEM is a widely used approach to the estimation of population parameters. However, the method relies on somewhat restrictive modeling assumptions to enable efficient parameter estimation. In this paper we describe a Bayesian approach to population pharmacokinetic analysis which used a technique known as Gibbs sampling to simulate values for each model parameter. We provide details of how to implement the method in the context of population pharmacokinetic analysis, and illustrate this via an application to gentamicin population pharmacokinetics in neonates.
Url:
DOI: 10.1007/BF02353641
Links to Exploration step
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DisplayOrder="Western"><GivenName>David</GivenName><GivenName>J.</GivenName><FamilyName>Spiegelhalter</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgDivision>Medical Research Council, Biostatistics Unit</OrgDivision><OrgName>Institute of Public Health</OrgName><OrgAddress><Street>Robinson Way</Street><City>Cambridge</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgDivision>Clinical Pharmacology Unit</OrgDivision><OrgName>University of Cambridge Clinical School</OrgName><OrgAddress><Street>Addenbrooke's Hospital</Street><City>Cambridge</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>Pfizer Central Research</OrgName><OrgAddress><City>Sandwich</City><State>Kent</State><Country>UK</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Quantification of the average and interindividual variation in pharmacokinetic behavior within the patient population is an important aspect of drug development. Population pharmacokinetic models typically involve large numbers of parameters related nonlinearly to sparse, observational data, which creates difficulties for conventional methods of analysis. The nonlinear mixed-effects method implemented in the computer program NONMEM is a widely used approach to the estimation of population parameters. However, the method relies on somewhat restrictive modeling assumptions to enable efficient parameter estimation. In this paper we describe a Bayesian approach to population pharmacokinetic analysis which used a technique known as Gibbs sampling to simulate values for each model parameter. We provide details of how to implement the method in the context of population pharmacokinetic analysis, and illustrate this via an application to gentamicin population pharmacokinetics in neonates.</Para></Abstract><KeywordGroup Language="En"><Heading>Key Words</Heading><Keyword>Gibbs sampling</Keyword><Keyword>population pharmacokinetics</Keyword><Keyword>gentamicin</Keyword></KeywordGroup><ArticleNote Type="Misc"><SimplePara>A grant from the British Heart Foundation supported Nicola G. Best.</SimplePara></ArticleNote></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Estimation of population pharmacokinetics using the Gibbs sampler</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Estimation of population pharmacokinetics using the Gibbs sampler</title></titleInfo><name type="personal" displayLabel="corresp"><namePart type="given">Nicola</namePart><namePart type="given">G.</namePart><namePart type="family">Best</namePart><affiliation>Medical Research Council, Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, UK</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Keith</namePart><namePart type="given">K.</namePart><namePart type="given">C.</namePart><namePart type="family">Tan</namePart><affiliation>Clinical Pharmacology Unit, University of Cambridge Clinical School, Addenbrooke's Hospital, Cambridge, UK</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Wally</namePart><namePart type="given">R.</namePart><namePart type="family">Gilks</namePart><affiliation>Medical Research Council, Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, UK</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">David</namePart><namePart type="given">J.</namePart><namePart type="family">Spiegelhalter</namePart><affiliation>Medical Research Council, Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, UK</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper"></genre><originInfo><publisher>Kluwer Academic Publishers-Plenum Publishers</publisher><place><placeTerm type="text">New York</placeTerm></place><dateCreated encoding="w3cdtf">1994-01-10</dateCreated><dateIssued encoding="w3cdtf">1995-08-01</dateIssued><copyrightDate encoding="w3cdtf">1995</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Quantification of the average and interindividual variation in pharmacokinetic behavior within the patient population is an important aspect of drug development. 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