Principles of Bayesian statistics and its relationship with applied pharmacokinetics

Abstract

If one knows the probability of an event occurring in a population, Bayesian statistics allows modifying its value when there is new individual information available. Although the Bayesian and frequentist (classical) methodologies have identical fields of application, the first one is increasingly applied in scientific research and big data analysis. In modern pharmacotherapy, clinical pharmacokinetics has been used for the expansion of monitoring, facilitated by technical-analytical and mathematical-statistical developments. Population pharmacokinetics has allowed the identification and quantification of pathophysiological and treatment characteristics in a specific patient population, especially in the pediatric and neonatal population and other vulnerable groups, explaining interindividual variability. Likewise, Bayesian estimation is important as a statistical tool applied in pharmacotherapy optimization software when pharmacological monitoring is based on clinical pharmacokinetic interpretation. With its advantages and despite its limitations, pharmacotherapeutic optimization based on Bayesian estimation is increasingly used, becoming the reference method today. This characteristic is particularly convenient for routine clinical practice due to the limited number of samples required from the patient and the flexibility it shows regarding blood sampling times for drug quantification. Therefore, the application of Bayesian principles to the practice of clinical pharmacokinetics has led to the improvement of pharmacotherapeutic care.