Linked Life Data

7581424

Source:http://linkedlifedata.com/resource/pubmed/id/7581424

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1995-12-12
pubmed:abstractText
This article proposes an EM-like algorithm for estimating, by maximum likelihood, the population parameters of a nonlinear mixed-effect model given sparse individual data. The first step involves Bayesian estimation of the individual parameters. During the second step, population parameters are estimated using a linearization about those Bayesian estimates. This algorithm (implemented in P-PHARM) is evaluated on simulated data, mimicking pharmacokinetic analyses and compared to the First-Order method and the First-Order Conditional Estimates method (both implemented in NONMEM). The accuracy of the results, within few iterations, shows the estimation capabilities of the proposed approach.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1054-3406
pubmed:author
pubmed:issnType
Print
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
141-58
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
1995
pubmed:articleTitle
A two-step iterative algorithm for estimation in nonlinear mixed-effect models with an evaluation in population pharmacokinetics.
pubmed:affiliation
INSERM U194, Service de Biostatistique et Informatique Médicale, CHU Pitié-Salpêtrière, Paris, France.
pubmed:publicationType
Journal Article