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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Generalized quasi-linear mixed-effects model.

Yusuke Saigusa1, Shinto Eguchi2, Osamu Komori3

  • 1Department of Biostatistics, School of medicine, 13155Yokohama City University, Japan.

Statistical Methods in Medical Research
|March 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible nonlinear mixed-effects model to improve generalized linear mixed model (GLMM) analysis for complex biological data. The new model better handles nonlinear relationships and captures patient subgroup heterogeneity in treatment effectiveness.

Keywords:
Generalized linear mixed modelmodel complexitymodel misspecificationquasi-linear modelingrobustness

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Biological Sciences

Background:

  • Generalized linear mixed models (GLMMs) are widely used for longitudinal and clustered biological data.
  • GLMMs can suffer from model complexity and misspecification issues.
  • Existing methods may not adequately capture nonlinear relationships in fixed and random effects.

Purpose of the Study:

  • To extend the standard GLMM to a more flexible nonlinear mixed-effects model.
  • To address challenges of model complexity and misspecification in GLMM applications.
  • To provide a robust statistical framework for analyzing complex biological data with potential nonlinearities.

Main Methods:

  • Development of a nonlinear mixed-effects model using quasi-linear modeling.
  • Extension of penalized quasi-likelihood and restricted maximum likelihood for parameter estimation.
  • Formulation of conditional AIC for model selection.
  • Evaluation through simulation studies and analysis of respiratory illness trial data.

Main Results:

  • The proposed model offers a more flexible fit than GLMMs when nonlinear relationships exist.
  • The model reduces to the standard GLMM when relationships are linear.
  • Simulation studies demonstrate the model's performance under misspecification.
  • Analysis revealed the model's ability to identify patient subgroups benefiting from treatment.

Conclusions:

  • The proposed nonlinear mixed-effects model provides a more adaptable and accurate alternative to GLMMs for complex biological data.
  • This approach effectively handles nonlinearities and identifies patient heterogeneity.
  • The model has practical implications for analyzing clinical trial data and understanding treatment efficacy in specific subgroups.