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.
Document Type
Article
Publication Date
3-14-2012
Digital Object Identifier (DOI)
http://dx.doi.org/10.1080/10543406.2010.547264
Repository Citation
Diaz, Francisco J.; Cogollo, Myladis R.; Spina, Edoardo; Santoro, Vincenza; Rendon, Diego M.; and de Leon, Jose, "Drug Dosage Individualization Based on a Random-Effects Linear Model" (2012). Psychiatry Faculty Publications. 12.
https://uknowledge.uky.edu/psychiatry_facpub/12
Notes/Citation Information
Published in Journal of Biopharmaceutical Statistics, v. 22, issue 3, p. 463-484.
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Biopharmaceutical Statistics on 03/14/2012, available online: http://wwww.tandfonline.com/ 10.1080/10543406.2010.547264