Hierarchical model Bayesian approach

Riccio A. Barone G. Chianese E. and Giunta G. (2006). A hierarchical Bayesian approach to the spatio-temporal modeling of air quality data. Atmospheric Environment, 40(3), 554-566. 

Mu and Ludden (38) described a hierarchical Bayesian model-based approach to incorporate an estimate of compliance into a population PK analysis. With the approach, both compliance and population PK parameters were estimated simultaneously. Rather than emphasize the estimation of compliance, the emphasis of the approach is on improving efficiency in the estimation of population PK parameters. 

Probabilistic models have been developed for characterizing compliance. The most commonly cited probabilistic approach is the hierarchical Markov model. Other more recently developed approaches range from a random sampling probabilistic model approach, to likelihood approaches, Bayesian approaches, and a missing dosing history approach. It is up to the pharmacometrician to choose the method that would best characterize his/her nonadherence data. The application example reinforces the importance of compliance to prescribed drug therapy, and how steady-state pharmacokinetics can be disrupted in the presence of noncompliance. 

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