title: Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling.
abstract: Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood as a function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response. Population PK/PD studies aim to determine the sources of variability in the observed concentrations/responses across groups of individuals. In this article, we consider the joint modeling of PK/PD data. The natural approach is to specify a joint model in which the concentration and response data are simultaneously modeled. Unfortunately, this approach may not be optimal if, due to sparsity of concentration data, an overly simple PK model is specified. As an alternative, we propose an errors-in-variables approach in which the observed-concentration data are assumed to be measured with error without reference to a specific PK model. We give an example of an analysis of PK/PD data obtained following administration of an anticoagulant drug. The study was originally carried out in order to make dosage recommendations. The prior for the distribution of the true concentrations, which may incorporate an individual's covariate information, is derived as a predictive distribution from an earlier study. The errors-in-variables approach is compared with the joint modeling approach and more naive methods in which the observed concentrations, or the separately modeled concentrations, are substituted into the response model. Throughout, a Bayesian approach is taken with implementation via Markov chain Monte Carlo methods.
authors.affiliation: Department of Epidemiology and Public Health, Imperial College School of Medicine, London, UK.
authors.forename: J
authors.initials: J
authors.lastname: Bennett
authors.forename: J
authors.initials: J
authors.lastname: Wakefield
mesh_terms: D000925:Anticoagulants / Q000493:pharmacokinetics / Q000494:pharmacology; D001499:Bayes Theorem; D001699:Biometry; D006629:Hirudins / Q000031:analogs & derivatives* / Q000493:pharmacokinetics / Q000494:pharmacology; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D010314:Partial Thromboplastin Time; D010599:Pharmacokinetics; D010600:Pharmacology / Q000706:statistics & numerical data*; D011994:Recombinant Proteins / Q000493:pharmacokinetics / Q000494:pharmacology
chemical_list: D000925:Anticoagulants; D006629:Hirudins; D011994:Recombinant Proteins; C060225:desirudin