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Updated: Dec 15, 2025
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
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Meta-analysis of Proportions Using Generalized Linear Mixed Models.
1From the Department of Statistics, Florida State University, Tallahassee, FL.
Epidemiology (Cambridge, Mass.)
|July 14, 2020
Summary
Generalized linear mixed models (GLMMs) offer a superior one-step approach for meta-analyses of proportions compared to traditional two-step methods. GLMMs demonstrate reduced bias and improved accuracy in synthesizing study data.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Meta-analyses of proportions are crucial in epidemiology.
- Conventional two-step methods (log, logit, arcsine transformations) have limitations, including fixed variance assumptions and issues with zero counts.
Purpose of the Study:
- To summarize and compare various methods for meta-analyses of proportions.
- To evaluate the performance of generalized linear mixed models (GLMMs) against traditional two-step methods.
Main Methods:
- Comparison of conventional two-step meta-analysis techniques.
- Implementation and evaluation of generalized linear mixed models (GLMMs) as a one-step approach.
- Utilizing real and simulated datasets for performance analysis.
Main Results:
- GLMMs exhibited smaller biases and mean squared errors compared to two-step methods.
- GLMMs provided higher coverage probabilities, indicating more reliable estimates.
- Traditional methods face challenges with variance estimation and zero-count data.
Conclusions:
- GLMMs represent a more accurate and robust approach for meta-analyses of proportions in epidemiologic research.
- Despite their advantages, GLMMs are underutilized in practice.
- Accessible software is available for implementing these advanced methods.
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