title: A new approach to modeling covariate effects and individualization in population pharmacokinetics-pharmacodynamics.
abstract: By combining Laplace's approximation and Monte Carlo methods to evaluate multiple integrals, this paper develops a new approach to estimation in nonlinear mixed effects models that are widely used in population pharmacokinetics and pharmacodynamics. Estimation here involves not only estimating the model parameters from Phase I and II studies but also using the fitted model to estimate the concentration versus time curve or the drug effects of a subject who has covariate information but sparse measurements. Because of its computational tractability, the proposed approach can model the covariate effects nonparametrically by using (i) regression splines or neural networks as basis functions and (ii) AIC or BIC for model selection. Its computational and statistical advantages are illustrated in simulation studies and in Phase I trials.
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305, USA. lait@stat.stanford.edu
authors.forename: Tze Leung
authors.initials: TL
authors.lastname: Lai
authors.forename: Mei-Chiung
authors.initials: MC
authors.lastname: Shih
authors.forename: Samuel P
authors.initials: SP
authors.lastname: Wong
mesh_terms: D000328:Adult; D018906:Antineoplastic Agents, Alkylating / Q000493:pharmacokinetics; D003606:Dacarbazine / Q000031:analogs & derivatives / Q000493:pharmacokinetics; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D010599:Pharmacokinetics; D010600:Pharmacology; D012044:Regression Analysis; D000077204:Temozolomide
chemical_list: D018906:Antineoplastic Agents, Alkylating; D003606:Dacarbazine; D000077204:Temozolomide