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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Related Experiment Video

Updated: May 12, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Cross-validation for nonlinear mixed effects models.

Emily Colby1, Eric Bair

  • 1Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA. ecanimation@hotmail.com

Journal of Pharmacokinetics and Pharmacodynamics
|March 28, 2013
PubMed
Summary
This summary is machine-generated.

We developed two new cross-validation methods for nonlinear mixed effects (NLME) models, enabling accurate model selection. These techniques address challenges with out-of-sample predictions in models with random effects.

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05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Statistics
  • Pharmacometrics
  • Computational Biology

Background:

  • Cross-validation is standard for model selection but challenging for mixed effects models.
  • Random effects in nonlinear mixed effects (NLME) models complicate out-of-sample prediction, a key component of cross-validation.

Purpose of the Study:

  • To introduce novel cross-validation techniques applicable to NLME models.
  • To enable robust model and covariate selection for NLME models.

Main Methods:

  • Developed two variants of cross-validation for NLME models.
  • One method uses post hoc random effect estimates for structural model selection.
  • A second method minimizes estimated random effects for covariate selection.

Main Results:

  • Both novel cross-validation methods demonstrated accurate performance on simulated datasets.
  • The methods were successfully applied to real-world population pharmacokinetic data.

Conclusions:

  • The proposed cross-validation approaches effectively address limitations in applying cross-validation to NLME models.
  • These methods facilitate accurate structural and covariate selection in NLME analyses.