The Bayesian Modeling of Covariates for Population Pharmacokinetic Models
作者:
Jon Wakefield,
James Bennett,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 435
页码: 917-927
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476961
出版商: Taylor & Francis Group
关键词: Clinical significance;Covariate selection;Dosage determination;Markov chain Monte Carlo;Prior information
数据来源: Taylor
摘要:
Pharmacokinetic (PK) models describe how the concentrations of a drug and its metabolite vary with time. Population PK models identify and quantify sources of between-individual variability in observed concentrations. Crucial to this aim is the identification of those covariates (i.e., individual-specific characteristics) responsible for explaining the variability. In this article we discuss how covariate modeling can be carried out for population PK models. We argue that the importance of a particular covariate can be discussed only with reference to the specific use for which the model is intended. Covariate modeling is important in population PK studies as it aids in determining dosage recommendations for specific covariate-defined populations. We describe a Bayesian predictive procedure that places covariate modeling in the context of dosage determination. In problems such as these it is crucial to incorporate relevant prior information. For covariateselectionwe extend the approach of George and McCulloch. The approaches utilize Markov chain Monte Carlo techniques. The methods are illustrated using population PK data from a study of the antibiotic vancomycin in babies. These data are sparse, with just 180 concentrations from 37 babies. Eight covariates are available, from which we construct a covariate model.
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