Bayesian population modeling of drug dosing adherence

Journal of Pharmacokinetics and Pharmacodynamics
Kelly FellowsMurali Ramanathan

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...Continue Reading

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Citations

Sep 26, 2015·Journal of Pharmacokinetics and Pharmacodynamics·Ho-Leung Fung, William J Jusko

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