Welcome to the Meta-Analysis of Proportions App (PERSyst MA version 1.0). The app is based on the R package meta, version 7.0-0. This application is designed to facilitate meta-analysis of proportions through a user-friendly interface, making it accessible regardless of your statistical software proficiency. Here’s a step-by-step guide to help you get started.
1) Data Entry:
- Upon launching the app, you will see two options for data entry: Manual and Upload CSV. Choose the most convenient option for your analysis.
- Manual: Select this option to enter your study data manually. You will be able to input study names, sample sizes, event counts, and subgroup information (if applicable) directly into the interface.
- Upload CSV: Select this option if you have a CSV file containing your study data. The CSV file should have the following columns: study name, sample size, event count, and subgroup (if any). Ensure there is no header in the file, and the columns follow this exact order, with values separated by commas.
- Use the Add Row or Remove Row buttons to adjust the number of studies in your manual entry form if you’re entering data manually.
- Be sure no cell for sample size or events is left blank, and the number of events is not higher than the sample size.
2) Settings:
- Subgroup Analysis: Decide whether you want to perform a subgroup analysis. If “Yes” is selected, ensure your data (either entered manually or uploaded via CSV) includes subgroup information.
- Synthesis Model: Select the appropriate synthesis model for your analysis. You can choose between common (fixed), random, or both (suggested only for educational purposes).
- Method for the Meta-analysis: Select among Inverse of Variance (standard method using the inverse of variance for weighting studies) or Generalized Linear Mixed Model – GLMM (advanced method that uses a random intercept logistic regression model). If GLMM is used, heterogeneity will be quantified using maximum-likelihood as the variance estimator, and the meta-analysis will apply the logit transformation.
- Transformation of Proportions: Select the appropriate transformation.
- Heterogeneity Estimator: Choose the estimator for assessing heterogeneity among studies.
- Prediction Interval: If using a random effects model, decide whether to include a prediction interval in your analysis. A prediction interval is estimated only if you have three or more studies.
3) Running the Analysis and Viewing the Results:
- Click on Run to perform the meta-analysis based on your inputs and settings. If you update any parameter, make sure to click on Run again to update the meta-analysis results.
- To adjust the forest plot, set the axis interval for your forest plot between 0 and 100%.
- The results, including a forest plot and a summary of the meta-analysis findings, will be displayed in the main panel. To enhance reporting on the meta-analysis of proportions, below the forest plot, you will see a suggested text for the methods section.
- Use the Download Forest Plot button to save the forest plot as a PDF vector file.