title: Drug dosage individualization based on a random-effects linear model.
abstract: This article investigates drug dosage individualization when the patient population can be described with a random-effects linear model of a continuous pharmacokinetic or pharmacodynamic response. Specifically, we show through both decision-theoretic arguments and simulations that a published clinical algorithm may produce better individualized dosages than some traditional methods of therapeutic drug monitoring. Since empirical evidence suggests that the linear model may adequately describe drugs and patient populations, and linear models are easier to handle than the nonlinear models traditionally used in population pharmacokinetics, our results highlight the potential applicability of linear mixed models to dosage computations and personalized medicine.
authors.affiliation: Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160, USA. fdiaz@kumc.edu
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Diaz
authors.forename: Myladis R
authors.initials: MR
authors.lastname: Cogollo
authors.forename: Edoardo
authors.initials: E
authors.lastname: Spina
authors.forename: Vincenza
authors.initials: V
authors.lastname: Santoro
authors.forename: Diego M
authors.initials: DM
authors.lastname: Rendon
authors.forename: Jose
authors.initials: J
authors.lastname: de Leon
mesh_terms: D004305:Dose-Response Relationship, Drug; D006801:Humans; D016014:Linear Models; D004364:Pharmaceutical Preparations / Q000008:administration & dosage* / Q000097:blood*; D057285:Precision Medicine / Q000379:methods / Q000706:statistics & numerical data*; D011897:Random Allocation
chemical_list: D004364:Pharmaceutical Preparations