Measuring inconsistency in meta-analyses

Measuring inconsistency in meta-analyses

2003-09-06 | Julian P T Higgins, Simon G Thompson, Jonathan J Deeks, Douglas G Altman
The article discusses the importance of assessing heterogeneity in meta-analyses to determine the consistency of study results and the generalizability of the findings. Traditional statistical tests for heterogeneity, such as Cochran's \(Q\) test, are often inadequate due to their sensitivity to the number of studies included and their poor ability to detect true heterogeneity. To address these issues, the authors introduce a new quantity, \(I^2\), which quantifies the degree of inconsistency in the studies' results. \(I^2\) is calculated as the percentage of total variation across studies that is due to heterogeneity rather than chance, ranging from 0% (no heterogeneity) to 100% ( maximum heterogeneity). The authors provide examples and applications of \(I^2\) to illustrate its utility in meta-analyses, including subgroup analyses and comparisons of different outcome measures. They argue that \(I^2\) is a more reliable and flexible tool for assessing heterogeneity compared to traditional statistical tests, and it helps in making more informed decisions about the interpretation of meta-analysis results.The article discusses the importance of assessing heterogeneity in meta-analyses to determine the consistency of study results and the generalizability of the findings. Traditional statistical tests for heterogeneity, such as Cochran's \(Q\) test, are often inadequate due to their sensitivity to the number of studies included and their poor ability to detect true heterogeneity. To address these issues, the authors introduce a new quantity, \(I^2\), which quantifies the degree of inconsistency in the studies' results. \(I^2\) is calculated as the percentage of total variation across studies that is due to heterogeneity rather than chance, ranging from 0% (no heterogeneity) to 100% ( maximum heterogeneity). The authors provide examples and applications of \(I^2\) to illustrate its utility in meta-analyses, including subgroup analyses and comparisons of different outcome measures. They argue that \(I^2\) is a more reliable and flexible tool for assessing heterogeneity compared to traditional statistical tests, and it helps in making more informed decisions about the interpretation of meta-analysis results.
Reach us at info@study.space
[slides] Measuring inconsistency in meta-analyses | StudySpace