  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.
