A bubble plot in meta-analysis is a variation of a scatter plot that visually represents the relationship between study effect sizes and a moderator variable, while also incorporating study weight or sample size into the visualization.

Structure of a Bubble Plot:

• X-axis: A continuous moderator variable (e.g., study year, dose, or mean age of participants).
• Y-axis: Effect size (e.g., odds ratio, risk ratio, mean difference).
• Bubble size: Represents study weight, often based on inverse variance or sample size.
• Bubble color: Can indicate an additional categorical or continuous variable.

Use in Meta-Analysis:

1. Exploring Heterogeneity: Helps identify whether a continuous moderator influences effect sizes.
2. Assessing Publication Trends: For example, plotting effect sizes against publication year.
3. Visualizing Study Weight: Larger studies have more precise estimates and should have more significant influence.
4. Understanding Subgroup Effects: If different studies cluster in different plot regions, this suggests variation due to study characteristics.