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Title: Nonparametric functional data modeling of pharmacokinetic processes with applications in dynamic PET imaging Authors:  Baoyi Shi - Columbia University (United States)
Todd Ogden - Columbia University (United States) [presenting]
Abstract: Modeling a pharmacokinetic process typically involves solving a system of linear differential equations and estimating the parameters upon which the functions depend. In order for this approach to be valid, it is necessary that a number of fairly strong assumptions hold, assumptions involving various aspects of the kinetic behavior of the substance being studied. In many situations, such models are understood to be simplifications of the ``true'' kinetic process. While in some circumstances, such a simplified model may be a useful (and close) approximation to the truth, in some cases, important aspects of the kinetic behavior cannot be represented. We present a nonparametric approach, based on principles of functional data analysis, to modeling of pharmacokinetic data. We illustrate its use through application to data from a dynamic PET imaging study of the human brain.