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Performance comparison of various maximum likelihood nonlinear mixed-effects estimation methods for dose-response

Elodie L Plan1, Alan Maloney, France Mentré

  • 1Department of Pharmaceutical Biosciences, Uppsala University, Sweden. elodie.plan@farmbio.uu.se

The AAPS Journal
|April 25, 2012
PubMed
Summary
This summary is machine-generated.

Comparing nonlinear mixed-effects modeling estimation methods for dose-response models revealed performance differences. Adaptive Gaussian Quadrature (AGQ) and tuned SAEM methods showed superior accuracy, especially with altered initial estimates, balancing runtime and precision.

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Area of Science:

  • Pharmacometrics
  • Statistical Modeling
  • Computational Biology

Background:

  • Nonlinear mixed-effects (NLME) modeling is crucial for analyzing complex biological data, particularly dose-response relationships.
  • Numerous estimation algorithms exist, but their comparative performance in specific applications like dose-response modeling remains underexplored.

Purpose of the Study:

  • To compare the performance of nine parametric maximum likelihood estimation methods for NLME dose-response models.
  • To evaluate method performance under varying model complexities (sigmoidicity, residual error) and data richness (rich vs. sparse designs).
  • To assess the impact of initial parameter estimates on estimation accuracy and runtime.

Main Methods:

  • Simulated 100 datasets for eight scenarios using a sigmoid Emax model with varied parameters.
  • Evaluated nine estimation methods: FOCE, LAPLACE, AGQ, and SAEM across NONMEM, SAS, and MONOLIX software.
  • Assessed performance using relative root mean squared error (RRMSE) and runtime, with both true and altered initial estimates.

Main Results:

  • All methods generally performed well with true initial estimates, except FOCE in R.
  • AGQ, FOCE (NONMEM), LAPLACE (SAS), and tuned SAEM (NONMEM, MONOLIX) showed superior RRMSE with altered initial estimates.
  • Runtime varied, with FOCE and LAPLACE being fastest and AGQ slowest.

Conclusions:

  • Significant differences exist in the performance of NLME estimation methods for dose-response models.
  • Method selection should consider the trade-off between accuracy (especially with challenging initial estimates) and computational runtime.
  • AGQ and tuned SAEM are recommended for improved accuracy in complex dose-response analyses.