From the Guidelines
When dealing with an I² meta-analysis that shows heterogeneity, it is crucial to investigate the sources of this heterogeneity rather than simply reporting a pooled estimate, as high heterogeneity can indicate significant differences in study results or methodologies. The I² statistic is a measure used to quantify the amount of variation in results across studies beyond that expected by chance, with values less than 25% often considered low heterogeneity 1. However, when considerable heterogeneity is observed, it is advisable to consider possible reasons, such as differences between subgroups of studies or data extraction errors, especially in the context of continuous outcomes 1. Some key points to consider in the context of I² meta-analysis heterogeneity include:
- I² values of 25%, 50%, and 75% are generally considered to represent low, moderate, and high heterogeneity, respectively.
- Using random-effects models instead of fixed-effects models is recommended when heterogeneity is present, as they account for between-study variation.
- Understanding the causes of heterogeneity is crucial, as it may reflect important clinical or methodological differences between studies that could affect the interpretation and applicability of the meta-analysis results to clinical practice.
- When there are few studies, inferences about heterogeneity should be cautious, and reporting the uncertainty in I², such as the 95% confidence interval, may be helpful 1. In clinical practice, the presence of high heterogeneity in a meta-analysis should prompt a careful evaluation of the studies included and the potential for subgroup differences, rather than relying solely on the pooled estimate.
From the Research
Heterogeneity in Meta-Analysis
- Heterogeneity in meta-analysis refers to the variation in study outcomes between different studies, which can affect the reliability of the summary estimate 2, 3.
- The I2 statistic is commonly used to quantify heterogeneity, but it does not directly inform about the distribution of effects and can be misinterpreted 2, 3.
Interpretation of I2 Statistic
- The I2 statistic represents the proportion of variability in a meta-analysis that is explained by differences between the included trials rather than by sampling error 3, 4.
- A high I2 value does not always indicate high heterogeneity, as it can be influenced by the number of studies and the point estimate 2.
- I2 statistics may not be discriminative and should be interpreted with caution, avoiding arbitrary thresholds 2.
Dealing with Heterogeneity
- To discuss heterogeneity, reviewers should focus on the description of the expected range of estimates, which can be done using prediction intervals and planned sensitivity analysis 2.
- The use of 95% confidence intervals for I2 estimates can provide good coverage over time and reflect the uncertainty associated with estimating I2 5.
- A sufficient number of trials and events are required to ensure stable and reliable I2 estimates, with a median of 467 events and 11 trials needed before the cumulative I2 estimates stay within +/-20% of the final I2 estimate 5.