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Overcoming challenges in prevalence meta-analysis: the case for the Freeman-Tukey transform
Jazeel Abdulmajeed1, Tawanda Chivese2, Suhail A R Doi3
1Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar.
BMC Medical Research Methodology
|April 5, 2025
Summary
The Freeman-Tukey transform offers superior performance for prevalence meta-analysis compared to the logit transformation, especially for extreme proportions. This statistical method ensures better coverage and narrower intervals in prevalence data analysis.
Area of Science:
- Biostatistics
- Epidemiology
- Statistical Modeling
Background:
- Traditional statistical methods often assume normally distributed data, which is unsuitable for analyzing prevalence proportions.
- Variance-stabilizing transformations are necessary for accurate analysis of prevalence data.
Purpose of the Study:
- To empirically evaluate the logit and Freeman-Tukey transformations for analyzing prevalence proportions.
- To determine the optimal transformation for meta-analysis of prevalence data.
Main Methods:
- Monte Carlo simulations were used to create datasets with varying parameters.
- Performance was assessed based on coverage, interval width, and variation over sample size for single proportions.
- Meta-analysis performance was evaluated using absolute mean deviation, coverage, and interval width of pooled proportions.
Main Results:
- The Freeman-Tukey transform showed better coverage and narrower intervals for extreme proportions compared to the logit transform.
- For non-extreme proportions, both transformations performed similarly for single prevalence estimates.
- In meta-analysis, the Freeman-Tukey transform consistently yielded lower deviation, narrower confidence intervals, and improved coverage.
Conclusions:
- The Freeman-Tukey transform is recommended over the logit transformation for meta-analysis of prevalence data.
- This finding aids researchers in selecting appropriate statistical methods for prevalence studies.
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