Abstract
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.
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Acknowledgments
This paper is a tribute and acknowledgement of the numerous seminal contributions of Dr. Gerhard Levy to the pharmaceutical sciences. My research interests in adherence modeling were sparked by an incidental conversation with Dr. Gerhard Levy in the corridor many years ago. We take this opportunity to congratulate and felicitate Dr. Levy on his 50-year track record of scientific accomplishments. We are grateful to Colin Stoneking and Klaus Oberauer (Department of Cognitive Psychology, University of Zurich, Switzerland) for kindly providing the add-on code for analyzing the VM distribution. This research is not funded. Support from the National Multiple Sclerosis Society (RG4836-A-5) to the Ramanathan laboratory is gratefully acknowledged.
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Fellows, K., Stoneking, C.J. & Ramanathan, M. Bayesian population modeling of drug dosing adherence. J Pharmacokinet Pharmacodyn 42, 515–525 (2015). https://doi.org/10.1007/s10928-015-9439-8
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DOI: https://doi.org/10.1007/s10928-015-9439-8