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Modeling attainment of steady state of drug concentration in plasma by means of a Bayesian approach using MCMC methods
Author(s) -
Jordan Paul,
Brunschwig Hadassa,
Luedin Eric
Publication year - 2008
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.263
Subject(s) - markov chain monte carlo , gibbs sampling , bayesian probability , markov chain , population , computer science , statistics , monte carlo method , econometrics , mathematics , medicine , environmental health
The approach of Bayesian mixed effects modeling is an appropriate method for estimating both population‐specific as well as subject‐specific times to steady state. In addition to pure estimation, the approach allows to determine the time until a certain fraction of individuals of a population has reached steady state with a pre‐specified certainty. In this paper a mixed effects model for the parameters of a nonlinear pharmacokinetic model is used within a Bayesian framework. Model fitting by means of Markov Chain Monte Carlo methods as implemented in the Gibbs sampler as well as the extraction of estimates and probability statements of interest are described. Finally, the proposed approach is illustrated by application to trough data from a multiple dose clinical trial. Copyright © 2007 John Wiley & Sons, Ltd.