Comparative analysis of Hamiltonian monte Carlo and maximum a posteriori inference for personalized dosing strategies in Bayesian pharmacokinetic modeling

In this study, I explore the impact of different Bayesian inference techniques—Hamiltonian Monte Carlo (HMC) and Maximum A Posteriori (MAP)—on personalized dosing strategies within pharmacokinetic modeling for apixaban. I present a Bayesian model that incorporates informative priors to derive personalized dosing recommendations based on desired trough concentrations following an initial dose. Through extensive simulation, I demonstrate significant discrepancies between dosing strategies derived from HMC and MAP, particularly in terms of predicted concentration uncertainties. While MAP provides a point estimate that is computationally efficient and familiar, it leads to different decision-making outcomes compared to HMC, which accounts for uncertainty more comprehensively. My findings reveal that MAP and HMC can lead to markedly different dosing recommendations, highlighting the importance of considering inference methods beyond point predictions in Bayesian pharmacokinetic modeling. I recommend practitioners use HMC alongside MAP to validate and compare results, thereby enhancing the transparency and reproducibility of personalized dosing strategies.