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Nonlinear mixed-effects models with misspecified random-effects distribution.

Reza Drikvandi1

  • 1Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK.

Pharmaceutical Statistics
|October 31, 2019
PubMed
Summary
This summary is machine-generated.

Nonlinear mixed-effects models are sensitive to random-effects distribution assumptions. Misspecification impacts variance components and predictions, though fixed effects remain robust. A diagnostic test is proposed for real data analysis.

Keywords:
Gauss-Hermite quadraturediagnostic testlongitudinal datanonlinear mixed-effects modelspredictionrandom-effects distribution

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Area of Science:

  • Statistics
  • Pharmacometrics
  • Biostatistics

Background:

  • Nonlinear mixed-effects models (NLME) are crucial for longitudinal data analysis, particularly in pharmaceutical research.
  • These models rely on unobservable random effects, making their distribution prone to misspecification.
  • Gauss-Hermite quadrature is the standard method for calculating marginal likelihood in mixed models.

Purpose of the Study:

  • To investigate the impact of misspecifying the random-effects distribution in NLME.
  • To develop a formal diagnostic test for assessing the appropriateness of assumed random-effects distributions.
  • To provide guidance for real-world data analysis in pharmaceutical research.

Main Methods:

  • Analysis of consequences of random-effects distribution misspecification in NLME.
  • Focus on Gauss-Hermite quadrature for marginal likelihood calculation.
  • Development and application of a formal diagnostic test for random-effects distributions.

Main Results:

  • Fixed-effects parameter estimates in NLME are generally robust to non-normal random-effects distributions.
  • Variance component estimates are highly sensitive to the assumed random-effects distribution.
  • Misspecified distributions lead to overestimation or underestimation of random-effects predictions.

Conclusions:

  • While fixed effects are robust, accurate random-effects distribution is critical for variance components and predictions in NLME.
  • The proposed diagnostic test is valuable for ensuring model adequacy in practical applications.
  • Findings are illustrated with a pharmacokinetic study, highlighting the importance of distributional assumptions.