Dr. Todd Odgen | Columbia University

Abstract: Compartment modeling describes the movement of substances or individuals among different states and has applications in epidemiology, pharmacokinetics, ecology, and many other areas.  Fitting such a model to data 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 under investigation.  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 other 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.