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The Bayesian modeling of covariates for population pharmacokinetic models. (English) Zbl 0882.62104

Summary: 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. 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 covariate selection we extend the approach of E. I. George and R. E. McCulloch [in W. R. Gilks et al. (eds.), Markov Chain Monte Carlo in Practice, 203-214 (1996; Zbl 0844.62051)]. 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.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62F15 Bayesian inference
92C45 Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.)

Citations:

Zbl 0844.62051
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