What question did this study set out to answer?

To evaluate statistical variance-stabilization methods for the meta-analysis of disease prevalence and propose methodological solutions for multiple category prevalence data.

August 20, 2013Journal of Epidemiology & Community HealthOpen Access

Meta-analysis of prevalence

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Authors

JBJan J. Barendregt SDSuhail A.R. Doi YLYong Yi Lee

Discussion

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Overview

Simulation study reveals double arcsine transformation outperforms logit modeling in prevalence meta-analyses, indicating improved accuracy for multi-category disease estimates.

Key Points

To evaluate statistical variance-stabilization methods for the meta-analysis of disease prevalence and propose methodological solutions for multiple category prevalence data.
Assessed logit and double arcsine transformations to stabilize variance when pooling prevalence estimates across studies.
Formulated methodological solutions to resolve estimation challenges specific to multiple category prevalence and implemented them in MetaXL software.
Evaluated performance using statistical simulations and an empirical demonstration using multiple sclerosis data from the Global Burden of Disease 2010 project.
Identified the double arcsine transformation as the preferred variance-stabilization approach over the logit transformation.
Demonstrated that the implementation of multiple category prevalence in MetaXL provides a superior methodological framework for pooling disease frequencies.

Cite This Study

Barendregt et al. (2013) studied this question.

synapsesocial.com/papers/6ab77aca559226bccc976e4e https://doi.org/10.1136/jech-2013-203104

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August 20, 2013Journal of Epidemiology & Community HealthOpen Access

Meta-analysis of prevalence

View Full Paper

Ask AI

Bookmark

Authors

JBJan J. Barendregt SDSuhail A.R. Doi YLYong Yi Lee

Discussion

Member takes

Overview

Simulation study reveals double arcsine transformation outperforms logit modeling in prevalence meta-analyses, indicating improved accuracy for multi-category disease estimates.

Key Points

To evaluate statistical variance-stabilization methods for the meta-analysis of disease prevalence and propose methodological solutions for multiple category prevalence data.
Assessed logit and double arcsine transformations to stabilize variance when pooling prevalence estimates across studies.
Formulated methodological solutions to resolve estimation challenges specific to multiple category prevalence and implemented them in MetaXL software.
Evaluated performance using statistical simulations and an empirical demonstration using multiple sclerosis data from the Global Burden of Disease 2010 project.
Identified the double arcsine transformation as the preferred variance-stabilization approach over the logit transformation.
Demonstrated that the implementation of multiple category prevalence in MetaXL provides a superior methodological framework for pooling disease frequencies.

Cite This Study

Barendregt et al. (2013) studied this question.

synapsesocial.com/papers/6ab77aca559226bccc976e4e https://doi.org/10.1136/jech-2013-203104

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