Pros and Cons of Bayesian Pharmacometric Modeling Using BUGS

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Bayesian modelingTwo distinguishing core notionsBasic elements of Baysian inferenceJust as with maximum likelihood methods, we begin with a modeldescribing the relationship between the data y and theunknown-valued parameters θ — a likelihood function p (y|θ).Prior knowledge (or belief) about model parameters θ isquantitatively described in terms of a probability distribution — aprior distribution p (θ).Bayes rule provides a rigorous basis for quantitative statisticalinference that considers both prior knowledge and new data viathe posterior distribution: p (θ|y) ∝ p (y|θ) p (θ)Use the posterior distribution for inferences regarding parametervalues.Use the posterior predictive distribution for inferences regardingfuture observations:∫p (y new |y) = p (y new |θ) p (θ|y) dθc○2009 Metrum Institute Bayesian Modeling Using BUGS AAPS 2009 3 / 26
Bayesian modelingBarriers to Bayesian pharmacometric applicationsBarriers to more widespread use of Bayesianpharmacometric modelingCan be much more computationally intensiveLack of adequate support for PK and PD modelsPKBugs provides built-in models limited to linear compartmentalmodels with 1–3 compartments, but itDoes not appropriately handle time-dependent covariatesOnly allows input into 1 compartment: central or absorptionSupport for ODEs available (WBDIFF, MCSim and OpenBUGS) butsubstantial programming is needed to apply it to complicated eventhistories, e.g., typical multiple dose dataIn short, there is was no real equivalent to PREDPPc○2009 Metrum Institute Bayesian Modeling Using BUGS AAPS 2009 4 / 26
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Pros and Cons of Bayesian Pharmacometric Modeling Using BUGS

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