Bayesian estimation in veterinary pharmacology: A conceptual and practical introduction

Area of Science:

• Veterinary Pharmacology
• Statistical Modeling
• Computational Biology

Background:

• Mathematical and computational tools are increasingly vital in veterinary pharmacology.
• Statistical inference for these complex models has been underexplored, with optimization and frequentist methods dominating.
• Bayesian statistics offer potential for enhanced interpretability and practical application in this field.

Purpose of the Study:

• To explore the specification of Bayesian models tailored for veterinary pharmacology.
• To demonstrate the practical implementation of Bayesian analyses, including prior selection.
• To illustrate the generation of useful statistics and uncertainty statements not easily obtainable with other methods.

Main Methods:

• Specification of Bayesian models relevant to veterinary pharmacology.
• Demonstration of prior selection techniques.
• Application of Bayesian multilevel modeling using simulated data for case studies.

Main Results:

• Bayesian models can effectively incorporate prior information from existing knowledge and study design.
• Demonstrated capability to generate practically useful statistics and uncertainty quantification.
• Case studies showcase applications in clinical trials, pharmacodynamics, and pharmacokinetics.

Conclusions:

• Bayesian statistics provide a valuable and interpretable framework for modern veterinary pharmacology research.
• The approach facilitates robust statistical inference and uncertainty estimation in complex models.
• This work serves as a foundational guide for researchers considering Bayesian methods in their applied work.