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Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics.
T H T Nguyen1, M-S Mouksassi2, N Holford3
1INSERM, IAME, UMR 1137, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
This tutorial introduces graphical tools for evaluating nonlinear mixed-effects models (NLMEMs). It covers visual assessment methods for continuous data, highlighting correct and misspecified models.
Area of Science:
- Pharmacometrics
- Statistical Modeling
Background:
- Nonlinear mixed-effects models (NLMEMs) are widely used in pharmacometrics.
- Model evaluation is crucial for ensuring the reliability of NLMEMs.
- A variety of tools exist for NLMEM evaluation, with a focus on visual assessment.
Purpose of the Study:
- To present graphical evaluation tools for NLMEMs with continuous data.
- To illustrate the application of these tools in identifying correct or misspecified models.
- To discuss the advantages and disadvantages of different graphical methods.
Main Methods:
- Focus on visual assessment techniques.
- Illustration of graphical tools for NLMEMs.
- Review of metrics used in model evaluation.
Main Results:
- Demonstration of graphs indicating correct model specification.
- Presentation of graphs highlighting potential model misspecification.
- Discussion of the utility and limitations of various visual evaluation methods.
Conclusions:
- Graphical evaluation is a fundamental component of NLMEM assessment.
- Visual tools aid in identifying model adequacy and potential issues.
- Understanding these methods is essential for robust NLMEM analysis.
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