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Comparison of four heterogeneity measures for meta-analysis.

Abstract：

RATIONALE, AIMS, AND OBJECTIVES:Heterogeneity is a critical issue in meta-analysis, because it implies the appropriateness of combining the collected studies and impacts the reliability of the synthesized results. The Q test is a traditional method to assess heterogeneity; however, because it does not have an intuitive interpretation for clinicians and often has low statistical power, many meta-analysts alter to use some measures, such as the I2 statistic, to quantify the extent of heterogeneity. This article aims at providing a summary of available tools to assess heterogeneity and comparing their performance. METHODS:We reviewed four heterogeneity measures (I2 , R ̂ I , R ̂ M , and R ̂ b ) and illustrated how they could be treated as test statistics like the Q statistic. These measures were compared with respect to statistical power based on simulations driven by three real-data examples. The pairwise agreement among the four measures was also evaluated using Cohen's κ coefficient. RESULTS:Generally, R ̂ I was slightly more powerful than the Q test, while its type I error rate might be slightly inflated. The power of I2 was fairly close to that of Q. The R ̂ M and R ̂ b statistics might have low powers in some cases. Because the differences between the powers of I2 , R ̂ I , and Q were often tiny, meta-analysts might not expect I2 and R ̂ I to yield significant heterogeneity if the Q test failed to do so. In addition, I2 and R ̂ I had fairly good agreement based on the simulated meta-analyses, but all other pairs of heterogeneity measures generally had poor agreement. CONCLUSION:The I2 and R ̂ I statistics are recommended for measuring heterogeneity. Meta-analysts should use the heterogeneity measures as descriptive statistics which have intuitive interpretations from the clinical perspective, instead of determining the significance of heterogeneity simply based on their magnitudes.

journal_name

J Eval Clin Pract

journal_title

Journal of evaluation in clinical practice

authors

Lin L

doi

10.1111/jep.13159

subject

Has Abstract

pub_date

2020-02-01 00:00:00

pages

376-384

issue

1

eissn

1356-1294

issn

1365-2753

journal_volume

26

pub_type

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