- Two-Group Meta-Analysis:
- Continuous Outcomes: Analyze continuous outcome data using means, standard deviations, and sample sizes for the experimental and control groups.
- Binary Outcomes: Analyze binary outcome data using event counts and sample sizes for the experimental and control groups.
- Incidence Rate Outcomes: Analyze incidence rate outcome data using event counts and person-time for the experimental and control groups.
- Single-Group Meta-Analysis:
- Single Means: Analyze single-group data using means, standard deviations, and sample sizes.
- Single Proportions: Analyze single-group data using event counts and sample sizes.
- Single Incidence Rates: Analyze single-group data using event counts and person-time.
- Correlations Meta-Analysis: Analyze correlation coefficients with sample sizes.
- Precomputed Effect Sizes Meta-Analysis: Analyze precomputed effect sizes with standard errors or confidence intervals.
- Risk of Bias Plots: Create summary and traffic light plots from risk-of-bias assessments using RoB 2, RoB 2 (cluster), ROBINS-I, ROBINS-E, QUADAS-2, and QUIPS.
- Model Settings: Fit common-effect and random-effects models with various effect measures and heterogeneity estimators.
- Subgroup Analysis: Compare effects and test for differences across subgroups.
- Meta-Regression: Fit meta-regression models to explore the effect of continuous and categorical predictors.
- Leave-One-Out Analysis: Evaluate the influence of individual studies by omitting them one at a time.
- Cumulative Meta-Analysis: Examine how the pooled effect changes as studies are added sequentially in a selected order.
- Publication Bias: Assess publication bias using funnel plots, trim-and-fill analysis, and the Doi plot / LFK index.
This module is actively being developed! If you encounter any issues, have a question, or have ideas for new features:
- 💬 Please open an issue on GitHub
- ✉️ Or send an email to metajamteam@gmail.com
If you find this module helpful for your research or work, please don't forget to cite us and drop a star ⭐️ on our GitHub repository! It helps a lot and motivates further development.