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Application 1: Simulation and estimation in the simulated stochastic one-compartmental pharmacokinetic model Application 1: Simulation and estimation in the simulated stochastic one-compartmental pharmacokinetic model As a first application of stochastic mixed effects modeling, we used a stochastic one-compartmental pharmacokinetic model for a simulation study.
The above simulation and estimation procedure was repeated 100 times, generating 100 sets of estimates.
When simulation and estimation models were analogous, the Bayesian analysis was able to provide suitable estimates of the variance components.
All simulation and estimation procedures were performed in the program R [ 30]; restricted maximum likelihood estimates of the variance components were obtained by the function lmer4.
Many of these applications require accurate mathematical representations for control design, simulation and estimation.
A main contribution of this paper is to connect those theories and thus making the tools from simulation and estimation literature available for model based diagnosis design.
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The calculated absolute risks of vascular complications can be used in risk classification for individual patients, health economic simulations, and estimation of the burden of the disease.
The simulation of this model and estimation of the generalized logistic functiońs parameters were performed using COPASI – software and the simulated annealing algorithm for parameter estimation.
Six methods for handling the missing covariate were compared in a stochastic simulations and estimations study where 200 data sets were simulated.
AK implemented the methods, carried out simulations and estimations and wrote the manuscript.
Important decisions in study design can be supported by stochastic simulations and estimations from a previously developed model.
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