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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A Note on Recurring Misconceptions When Fitting Nonlinear Mixed Models.

Jeffrey R Harring1, Shelley A Blozis2

  • 1a University of Maryland , College Park.

Multivariate Behavioral Research
|November 12, 2016
PubMed
Summary
This summary is machine-generated.

Nonlinear mixed-effects (NLME) models require careful interpretation. Estimation methods like first-order linearization (FO) and Gaussian-Hermite quadrature (GHQ) yield distinct population-average versus subject-specific results, impacting model fit and understanding individual change.

Keywords:
First-order linearizationGaussian quadraturenonlinear mixed-effects modelspopulation-averagesubject-specific

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Nonlinear mixed-effects (NLME) models analyze complex individual change in repeated measures data.
  • Misinterpretations of NLME model results are prevalent, necessitating clarification.

Purpose of the Study:

  • To clarify common misconceptions in interpreting NLME model parameters and fitted functions.
  • To highlight how estimation methods influence population-average (PA) and subject-specific (SS) interpretations.

Main Methods:

  • Comparison of first-order linearization (FO) and Gaussian-Hermite quadrature (GHQ) estimation algorithms.
  • Analysis of how these methods affect parameter estimates and fitted trajectories.
  • Examination of the impact on individual lack-of-fit assessments.

Main Results:

  • The choice between FO and GHQ methods dictates whether parameters are interpreted as PA or SS.
  • Estimation approaches influence the typical individual's fitted function.
  • Misinterpretation arises from conflating PA and SS interpretations derived from different algorithms.

Conclusions:

  • Accurate interpretation of NLME models hinges on understanding the chosen estimation method.
  • Researchers must distinguish between population-average and subject-specific inferences.
  • Clearer guidelines are needed to prevent widespread misapplication of NLME analyses.