CMA markets itself as a user-friendly, powerful meta-analysis solution, but beneath the polished GUI lies a tool riddled with critical shortcomings.

• The interface, while approachable, encourages black-box usage—users often apply complex statistical models without understanding assumptions or limitations.
• Default settings and automated procedures can mislead novices into inappropriate analyses.
• It lacks transparency in many calculations, offering limited insight into the underlying algorithms.

• CMA supports common meta-analytic models but lags behind open-source tools in cutting-edge methods like network meta-analysis, multivariate meta-analysis, or Bayesian approaches.
• It does not support advanced bias modeling or complex meta-regressions adequately.
• The software offers minimal diagnostic tools to detect publication bias, heterogeneity, or influential studies beyond standard plots.

• CMA is a standalone desktop application with no integration for automated literature screening, data extraction, or risk of bias assessment.
• Manual data entry is required, increasing chances of human error and inefficiency.
• Lack of API or cloud support limits collaboration and workflow automation.

• CMA projects are stored in proprietary file formats, complicating reproducibility.
• Version control is rudimentary or non-existent.
• Reporting templates are rigid, limiting customization of outputs for diverse publication requirements.

• CMA is commercial software with significant licensing costs, limiting access for researchers in low-resource settings.
• Its proprietary nature locks users into its ecosystem, hindering data portability.

CMA offers a visually friendly entry point into meta-analysis but fails to provide the transparency, flexibility, and methodological depth required for rigorous evidence synthesis. Its closed, manual, and costly nature makes it unsuitable for modern, collaborative, and reproducible research environments.

Recommendation: Use CMA cautiously and always supplement with open, transparent, and flexible tools like R packages or advanced platforms that support automated workflows and collaboration.

• ✅ Supports wide range of meta-analytic models including network and Bayesian methods
• ✅ Fully scriptable for reproducibility and customization
• ✅ Integrates with literate programming tools (R Markdown, Docker)
• ➕ Why better than CMA:

Most flexible, transparent, and extensible platform for meta-analysis

• ✅ Free, open-source GUI-based statistical software
• ✅ Supports frequentist and Bayesian meta-analysis methods
• ✅ Easier learning curve than R with reproducible output
• ➕ Why better than CMA:

Combines ease of use with advanced statistical features

• ✅ Automate literature screening, data extraction, and risk of bias assessment
• ✅ Reduce manual workload and errors
• ➕ Why better than CMA:

Automate tedious upstream steps, complement statistical analysis

• ✅ Manage entire systematic review lifecycle (screening, extraction, bias assessment)
• ✅ Support collaboration, version control, and audit trails
• ➕ Why better than CMA:

Covers complete review workflow, not just meta-analysis

• Use R packages for comprehensive, advanced, and reproducible meta-analyses.
• Use JASP or Jamovi for GUI-based advanced analysis with less coding.
• Use Elicit and RobotReviewer to automate evidence extraction and bias assessment.
• Use Covidence or DistillerSR to manage the full systematic review process.
• Use CMA mainly for simple, standalone GUI needs without cutting-edge features.