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Related Experiment Videos

Comparing non-hierarchical models: application to non-linear mixed effects modeling

E I Ette1

  • 1Office of Clinical Pharmacology and Biopharmaceutics, Food and Drug Administration, Rockville, MD 20857, USA.

Computers in Biology and Medicine
|November 1, 1996
PubMed
Summary
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A new method using bootstrap confidence intervals on log-likelihood differences (LLDs) allows comparison of non-linear mixed effects models. This approach improves model selection in pharmacokinetic studies.

Area of Science:

  • Pharmacometrics
  • Statistical modeling
  • Pharmacokinetics

Background:

  • Comparing non-hierarchical non-linear mixed effects models lacks a standardized method.
  • Current practice relies on selecting models with lower objective functions, which can be unreliable.
  • Accurate model comparison is crucial for robust pharmacokinetic and pharmacodynamic analyses.

Purpose of the Study:

  • To propose and validate a novel statistical method for comparing the goodness-of-fit of two non-hierarchical non-linear mixed effects models.
  • To introduce bootstrapping of log-likelihood differences (LLDs) as a reliable technique for model comparison.
  • To demonstrate the application of this method in pharmacokinetic studies.

Main Methods:

  • Bootstrapping the log-likelihood differences (LLDs) between two competing non-hierarchical models.

Related Experiment Videos

  • Constructing bootstrap confidence intervals for the LLDs to assess statistical significance.
  • Applying the method to compare different parameterizations of clearance models in a longitudinal pharmacokinetic study.

    • Main Results:

    • The proposed bootstrap method provides a statistically sound basis for comparing model fit.
    • Demonstrated effectiveness in distinguishing between different clearance model structures.
    • Illustrates the comparison of additive and exponential models for creatinine clearance as a predictor.

    Conclusions:

    • Bootstrapping LLDs offers a robust alternative to relying solely on objective function values for model comparison.
    • This method enhances the reliability of model selection in non-linear mixed effects modeling.
    • Facilitates more accurate pharmacokinetic modeling and analysis, particularly in drug development.