How to understand and report heterogeneity in a meta-analysis: The difference between I-squared and prediction...

Area of Science:

• Biostatistics
• Clinical Research Methodology

Background:

• Meta-analyses require reporting effect size variation across studies for accurate interpretation.
• The I-squared index is commonly used but inaccurately quantifies heterogeneity.
• Understanding effect size variability is crucial for clinical decision-making.

Purpose of the Study:

• To highlight the limitations of the I-squared index in meta-analysis.
• To advocate for the use of prediction intervals to quantify heterogeneity.
• To explain how prediction intervals provide clinically relevant information about effect size variation.

Main Methods:

• Critically evaluating the I-squared index for quantifying heterogeneity.
• Introducing prediction intervals as a superior method for assessing effect size variability.
• Illustrating the application of prediction intervals with an example of treatment effect distribution.

Main Results:

• The I-squared index fails to accurately represent the distribution of effect sizes across studies.
• Prediction intervals effectively quantify the range and distribution of effect sizes.
• Prediction intervals can describe the proportion of studies with trivial, moderate, or large effects.

Conclusions:

• The I-squared index is an inappropriate measure for quantifying heterogeneity in meta-analyses.
• Prediction intervals offer a more informative approach to understanding effect size variation.
• Accurate quantification of heterogeneity using prediction intervals enhances clinical interpretation of meta-analytic findings.