Density-based Monte Carlo filter and its applications in estimation of unobservable variables and pharmacokinetic parameters

TL;DR

This paper introduces a density-based Monte Carlo filter (DMF) for estimating unobservable variables in nonlinear PK/PD models, demonstrating improved accuracy over traditional EKF methods through simulation comparisons.

Contribution

The paper proposes a novel DMF method for unobservable variable estimation and a simulation-based approach with genetic algorithms for pharmacokinetic parameter estimation, outperforming EKF.

Findings

DMF provides more accurate estimates than EKF.

Simulation results show reduced mean absolute error with DMF.

The approach improves nonlinear PK/PD data analysis.

Abstract

Nonlinear stochastic differential equation models with unobservable variables are now widely used in the analysis of PK/PD data. The unobservable variables are often estimated with extended Kalman filter (EKF), and the unknown pharmacokinetic parameters are usually estimated by maximum likelihood estimator. However, EKF is inadequate for nonlinear PK/PD models, and MLE is known to be biased downwards. A density-based Monte Carlo filter (DMF) is proposed to estimate the unobservable variables, and a simulation-based procedure is proposed to estimate the unknown parameters in this paper, where a genetic algorithm is designed to search the optimal values of pharmacokinetic parameters. The performances of EKF and DMF are compared through simulations, and it is found that the results based on DMF are more accurate than those given by EKF with respect to mean absolute error.