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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Nonlinear mixed effects to improve glucose minimal model parameter estimation: a simulation study in intensive and

Paolo Denti1, Alessandra Bertoldo, Paolo Vicini

  • 1Department of Information Engineering of University of Padova, Padova 35129, Italy. paolo.denti@dei.unipd.it

IEEE Transactions on Bio-Medical Engineering
|April 22, 2009
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Summary

Population modeling improves parameter estimation for the intravenous glucose tolerance test (IVGTT), especially with sparse data. Nonlinear mixed-effects models, particularly FOCE, offer robust and reliable results compared to traditional methods.

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Area of Science:

  • Pharmacokinetics and Pharmacodynamics
  • Mathematical Modeling
  • Biostatistics

Background:

  • Individual parameter estimation for IVGTT minimal models often uses weighted least squares (WLS).
  • Sparse data can lead to unsatisfactory individual parameter estimates.
  • Population approaches offer a solution by leveraging data across subjects.

Purpose of the Study:

  • To compare different estimation methods for IVGTT minimal model parameters.
  • To assess the robustness of these methods under data scarcity.
  • To identify the most reliable estimation approach for sparse IVGTT data.

Main Methods:

  • Simulated datasets were used to evaluate weighted least squares (WLS).
  • Iterative methods (iterative two-stage (ITS) and global two-stage (GTS)) were applied.
  • Nonlinear mixed-effects models (NLMEMs) using first-order (FO), FO conditional estimation (FOCE), and Laplace (LAP) approximations were assessed.

Main Results:

  • Population approaches yielded more reliable parameter estimates than WLS, even with dense sampling.
  • Parameter estimates were significantly more robust with scarce data using population methods.
  • NLMEMs (excluding FO), particularly FOCE, demonstrated greater versatility and robustness with sparse data compared to ITS and GTS.

Conclusions:

  • Nonlinear mixed-effects models, especially FOCE, are superior for estimating IVGTT minimal model parameters, particularly in scenarios with limited data.
  • Population-based estimation strategies enhance the reliability and robustness of parameter identification.
  • FOCE emerges as the most effective method for handling sparse sampling in IVGTT studies.