J. Wakefield et J. Bennett, THE BAYESIAN MODELING OF COVARIATES FOR POPULATION PHARMACOKINETIC MODELS, Journal of the American Statistical Association, 91(435), 1996, pp. 917-927
Pharmacokinetic (PK) models describe how the concentrations of a drug
and its metabolite vary with time. Population PK models identify and q
uantify sources of between-individual variability in observed concentr
ations. 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 ca
n be carried out for population PK models. We argue that the importanc
e of a particular covariate can be discussed only with reference to th
e specific use for which the model is intended. Covariate modeling is
important in population PK studies as it aids in determining dosage re
commendations 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 cruci
al to incorporate relevant prior information. For covariate selection
we extend the approach of George and McCulloch. The approaches utilize
Markov chain Monte Carlo techniques. The methods are illustrated usin
g population PK data from a study of the antibiotic vancomycin in babi
es. These data are sparse, with just 180 concentrations from 37 babies
. Eight covariates are available, from which we construct a covariate
model.