A Bayesian approach for the estimation of patient compliance based on the last sampling information
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Poor adherence to a drug prescription significantly impacts on the efficacy and safety of a planned therapy. The relationship between drug intake and pharmacokinetics (PK) is only partially known. In this work, we focus on the so-called “inverse problem”, concerned with the issue of retracing the patient compliance scenario using limited clinical knowledge. Using a reported Pop-PK model of imatinib, and accounting for the variability around its PK parameters, we were able to simulate a whole range of drug concentration values at a specific sampling point for a population of patients with all possible drug compliance profiles. Using a Bayesian decision rule, we developed a methodology for the determination of the associated compliance profile prior to a given sampling value. The adopted approach allows, for the first time, to quantitatively acquire knowledge about the compliance patterns having a causal effect on a given PK. Moreover, using a simulation approach, we were able to evaluate the evolution of success rate of the retracing process in terms of the considered time period before sampling as well as the model-inherited variability. In conclusion, this work allows, from a probability viewpoint, to propose a solution for this inverse problem of compliance determination.
KeywordsBayesian decision theory Compliance Pharmacokinetics Limited clinical information
This work has been supported by FQRNT, NSERC and MITACS. The Centre de Recherches Mathématiques of Université de Montréal is also acknowledged for its support.