caa a22 4500 605534071 CHVBK 20210128100841.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10928-015-9439-8 doi (NATIONALLICENCE)springer-10.1007/s10928-015-9439-8 Bayesian population modeling of drug dosing adherence [Elektronische Daten] [Kelly Fellows, Colin Stoneking, Murali Ramanathan] Adherence is a frequent contributing factor to variations in drug concentrations and efficacy. The purpose of this work was to develop an integrated population model to describe variation in adherence, dose-timing deviations, overdosing and persistence to dosing regimens. The hybrid Markov chain-von Mises method for modeling adherence in individual subjects was extended to the population setting using a Bayesian approach. Four integrated population models for overall adherence, the two-state Markov chain transition parameters, dose-timing deviations, overdosing and persistence were formulated and critically compared. The Markov chain-Monte Carlo algorithm was used for identifying distribution parameters and for simulations. The model was challenged with medication event monitoring system data for 207 hypertension patients. The four Bayesian models demonstrated good mixing and convergence characteristics. The distributions of adherence, dose-timing deviations, overdosing and persistence were markedly non-normal and diverse. The models varied in complexity and the method used to incorporate inter-dependence with the preceding dose in the two-state Markov chain. The model that incorporated a cooperativity term for inter-dependence and a hyperbolic parameterization of the transition matrix probabilities was identified as the preferred model over the alternatives. The simulated probability densities from the model satisfactorily fit the observed probability distributions of adherence, dose-timing deviations, overdosing and persistence parameters in the sample patients. The model also adequately described the median and observed quartiles for these parameters. The Bayesian model for adherence provides a parsimonious, yet integrated, description of adherence in populations. It may find potential applications in clinical trial simulations and pharmacokinetic-pharmacodynamic modeling. Springer Science+Business Media New York, 2015 Adherence nationallicence Compliance nationallicence Dosing patterns nationallicence Runs nationallicence Circular distribution nationallicence Fellows Kelly Department of Pharmaceutical Sciences and Neurology, State University of New York, 355 Kapoor Hall, 14214-8033, Buffalo, NY, USA aut Stoneking Colin Seminar for Statistics, Department of Mathematics, ETH Zurich, Zurich, Switzerland aut Ramanathan Murali Department of Pharmaceutical Sciences and Neurology, State University of New York, 355 Kapoor Hall, 14214-8033, Buffalo, NY, USA aut Journal of Pharmacokinetics and Pharmacodynamics Springer US; http://www.springer-ny.com 42/5(2015-10-01), 515-525 1567-567X 42:5<515 2015 42 10928 https://doi.org/10.1007/s10928-015-9439-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10928-015-9439-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fellows Kelly Department of Pharmaceutical Sciences and Neurology, State University of New York, 355 Kapoor Hall, 14214-8033, Buffalo, NY, USA aut NATIONALLICENCE 700 1- Stoneking Colin Seminar for Statistics, Department of Mathematics, ETH Zurich, Zurich, Switzerland aut NATIONALLICENCE 700 1- Ramanathan Murali Department of Pharmaceutical Sciences and Neurology, State University of New York, 355 Kapoor Hall, 14214-8033, Buffalo, NY, USA aut NATIONALLICENCE 773 0- Journal of Pharmacokinetics and Pharmacodynamics Springer US; http://www.springer-ny.com 42/5(2015-10-01), 515-525 1567-567X 42:5<515 2015 42 10928