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Professional-grade open-source meta-analysis for advanced evidence-based synthesis.

OpenMetaAnalyst is an open-source, cross-platform software application developed by the Center for Evidence Synthesis in Health at Brown University. Built upon a sophisticated R-driven statistical engine, it provides a graphical user interface (GUI) designed specifically for researchers to conduct complex meta-analyses without requiring deep programming knowledge. As of 2026, it remains a foundational tool in the academic and clinical sectors for its ability to handle binary, continuous, and diagnostic data types within a unified framework. Technically, the software leverages the 'metafor' and 'lme4' R packages under the hood, allowing for both frequentist and Bayesian approaches to evidence synthesis. Its architecture is optimized for transparency and reproducibility, ensuring that every statistical model—from random-effects models to complex meta-regressions—is fully verifiable. While newer AI-driven 'automated' tools have emerged, OpenMetaAnalyst maintains its market position as the gold standard for 'high-trust' manual validation, serving as the primary validation engine for systematic reviews and meta-analyses published in high-impact journals. It supports sophisticated workflows including subgroup analyses, cumulative meta-analyses, and leave-one-out sensitivity testing, all within a locally-hosted environment that ensures 100% data privacy and compliance.
OpenMetaAnalyst is an open-source, cross-platform software application developed by the Center for Evidence Synthesis in Health at Brown University.
Explore all tools that specialize in perform meta-analysis. This domain focus ensures OpenMetaAnalyst delivers optimized results for this specific requirement.
Explore all tools that specialize in forest plot generation. This domain focus ensures OpenMetaAnalyst delivers optimized results for this specific requirement.
Ability to analyze studies with more than two treatment groups while accounting for the correlation between effect sizes.
Allows users to specify prior distributions and run MCMC simulations for evidence synthesis.
Automated iterative analysis that removes one study at a time to check for outliers or influential studies.
Supports continuous and categorical covariates to explore sources of heterogeneity across studies.
Handles sensitivity and specificity data simultaneously using bivariate models.
Generates vector-quality Forest, Funnel, and Galbraith plots with customizable weights and labels.
Performs sequential meta-analysis by adding studies chronologically to identify the point of statistical significance over time.
Download the binary installer for Windows, macOS, or Linux from the official Brown University CESH portal.
Ensure an updated R environment is installed (optional, as some versions bundle the runtime).
Prepare your data in a long or wide format, ensuring outcome variables (e.g., event counts, means) are clearly defined.
Launch the application and select 'New Dataset' to import your CSV or Excel file.
Define the Data Type (Binary, Continuous, or Diagnostic) and the corresponding effect size metric (e.g., Odds Ratio, Risk Ratio, Mean Difference).
Select the statistical model (Fixed-Effect or Random-Effects) based on assumed heterogeneity.
Execute the analysis to generate summary statistics and the initial Forest Plot.
Perform subgroup analysis or meta-regression by selecting covariates from the data panel.
Run leave-one-out sensitivity analyses to verify the robustness of the primary findings.
Export high-resolution publication-ready plots and the complete statistical report for peer-review submission.
All Set
Ready to go
Verified feedback from other users.
"Highly praised for its ease of use compared to raw R coding, though some users find the UI dated."
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