Role of Statistical Random-Effects Linear Models in Personalized Medicine

• Activity Number: 470
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Francisco J. Diaz*+
• Companies: University of Kansas Medical Center
• Keywords: Dosage individualization ; Empirical bayesian feedback ; Pharmacokinetic modeling ; Linear mixed models ; Evidence farms ; Therapeutic drug monitoring

Abstract

Empirical studies and developments in pharmacokinetic theory suggest that random effects linear models are valuable tools for describing simultaneously patient populations as a whole and patients as individuals. Thus, these models may be useful in the development of personalized medicine. Random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. Individualized dosages computed with these models and an empirical Bayesian approach may produce better results than dosages computed with traditional methods of therapeutic drug monitoring (TDM). These models provide accurate representations of phase III and IV steady-state pharmacokinetic data. Some applications are: drug dosage individualization in TDM; computation of dose correction factors; measure of the clinical importance of clinical, demographic, environmental or genetic covariates; study of drug-drug interactions in clinical settings; computational tools for web-site-based evidence farming; pharmacogenomic studies; development of pharmacological theory; and bioequivalence studies.