A bayesian perspective on estimation of variability and uncertainty in mechanism-based models.

Abstract

Mechanism-based pharmacokinetic/pharmacodynamic models have a fundamental basis in biology and pharmacology and, thus, are useful for hypothesis generation and extrapolation beyond the conditions of the original analysis data. The complexity of these models necessitates the incorporation of prior knowledge to inform many of the model parameters. Markov chain Monte Carlo Bayesian estimation offers a robust and statistically rigorous approach for incorporation of prior information into mechanism-based models. This article provides a perspective on the utility of this approach.