A Simulation-based comparison between parametric and nonparametric estimation methods in PBPK Models

H. T. Banks 1 , Y. Ma 1  and L. K. Potter 2
  • 1 Center for Research in Scientific Computation, North Carolina State University, Raleigh, N.C. 27695-8205. E-mails: htbanks@ncsu.edu, yma@ncsu.edu
  • 2 Scientific Computing and Mathematical Modeling, GlaxoSmithKline, Research Triangle Park, NC 27709. E-mail: laura.k.potter@gsk.com

We compare parametric and nonparametric estimation methods in the context of PBPK modeling using simulation studies. We implement a Monte Carlo Markov Chain simulation technique in the parametric method, and a functional analytical approach to estimate the probability distribution function directly in the non-parametric method. The simulation results suggest an advantage for the parametric method when the underlying model can capture the true population distribution. On the other hand, our calculations demonstrate some advantages for a nonparametric approach since it is a more cautious (and hence safer) way to assess the distribution when one does not have sufficient knowledge to assume a population distribution form or parametrization. The parametric approach has obvious advantages when one has significant a priori information on the distributions sought, although when used in the nonparametric method, prior information can also significantly facilitate estimation.

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This journal presents original articles on the theory, numerics and applications of inverse and ill-posed problems. These inverse and ill-posed problems arise in mathematical physics and mathematical analysis, geophysics, acoustics, electrodynamics, tomography, medicine, ecology, financial mathematics etc. Articles on the construction and justification of new numerical algorithms of inverse problem solutions are also published.

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