Bayesian analysis of minimal model under the insulin-modified IVGTT

Yi Wang, Kent M. Eskridge, Andrzej T. Galecki

University of Michigan.
University of Nebraska-Lincoln.

DOI: 10.4236/health.2010.23027                

Abstract

A Bayesian analysis of the minimal model was proposed where both glucose and insulin were analyzed simultaneously under the insulin-modified intravenous glucose tolerance test (IVGTT). The resulting model was implemented with a nonlinear mixed-effects modeling setup using ordinary differential equations (ODEs), which leads to precise estimation of population parameters by separating the inter- and intra-individual variability. The results indicated that the Bayesian method applied to the glucose-insulin minimal model provided a satisfactory solution with accurate parameter estimates which were numerically stable since the Bayesian method did not require approximation by linearization.

Keywords

Minimal Model; Bayesian Analysis; IVGTT; Nonlinear Mixed-Effects Modeling; ODE

Conflicts of Interest

The authors declare no conflicts of interest.

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