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  • 标题:Bayesian analysis of minimal model under the insulin-modified IVGTT
  • 本地全文:下载
  • 作者:Yi Wang ; Kent M. Eskridge ; Andrzej T. Galecki
  • 期刊名称:Health
  • 印刷版ISSN:1949-4998
  • 电子版ISSN:1949-5005
  • 出版年度:2010
  • 卷号:2
  • 期号:3
  • 页码:188-194
  • DOI:10.4236/health.2010.23027
  • 出版社:Scientific Research Publishing
  • 摘要: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.
  • 关键词:Minimal Model; Bayesian Analysis; IVGTT; Nonlinear Mixed-Effects Modeling; ODE
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