The Bayesian Modeling of Covariates for Population Pharmacokinetic Models
本文讨论如何在群体药代动力学模型中用贝叶斯方法建模协变量，以帮助确定特定人群的用药剂量，并用万古霉素在婴儿中的稀疏数据（37名婴儿、180个浓度、8个协变量）演示了方法。
Abstract 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 covariate selection we 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.