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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Real longitudinal data analysis for real people: building a good enough mixed model
Jing Cheng1, Lloyd J Edwards, Mildred M Maldonado-Molina
1Division of Biostatistics, Department of Epidemiology and Health Policy Research, University of Florida College of Medicine, FL, USA. jcheng@biostat.ufl.edu
Statistics in Medicine
|December 17, 2009
Summary
Building effective mixed effects models for longitudinal data is simplified with a new five-step strategy. This approach enhances model convergence, speed, and accuracy for robust statistical inference.
Area of Science:
- Statistics
- Biostatistics
- Longitudinal Data Analysis
Background:
- Mixed effects models are widely used for analyzing longitudinal data.
- Developing appropriate mixed effects models presents significant challenges.
- Existing methods lack a systematic strategy for model building.
Purpose of the Study:
- To propose a systematic strategy for building effective mixed effects models.
- To provide practical advice for improving model fitting and convergence.
- To enhance the credibility of statistical inference from mixed models.
Main Methods:
- A five-step procedure for mixed effects model fitting is introduced.
- Recommendations include centering, scaling, and full-rank coding of predictors.
- Application of univariate linear model diagnostics to mixed models.
Main Results:
- The proposed strategy improves convergence, computing speed, and numerical accuracy.
- Practical advice helps manage the complexity of modeling mean and covariance structures.
- The approach facilitates fitting more general and credible covariance models.
Conclusions:
- The systematic strategy offers a practical solution for building better mixed effects models.
- Improved diagnostics and data preprocessing are crucial for defensible inference.
- Further development of covariance and inference tools for mixed models is needed.
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