Performance of Between-study Heterogeneity Measures in the Cochrane Library

Epidemiology
Xiaoyue MaHaitao Chu

Abstract

The growth in comparative effectiveness research and evidence-based medicine has increased attention to systematic reviews and meta-analyses. Meta-analysis synthesizes and contrasts evidence from multiple independent studies to improve statistical efficiency and reduce bias. Assessing heterogeneity is critical for performing a meta-analysis and interpreting results. As a widely used heterogeneity measure, the I statistic quantifies the proportion of total variation across studies that is caused by real differences in effect size. The presence of outlying studies can seriously exaggerate the I statistic. Two alternative heterogeneity measures, the (Equation is included in full-text article.)and (Equation is included in full-text article.)have been recently proposed to reduce the impact of outlying studies. To evaluate these measures' performance empirically, we applied them to 20,599 meta-analyses in the Cochrane Library. We found that the (Equation is included in full-text article.)and (Equation is included in full-text article.)have strong agreement with the I, while they are more robust than the I when outlying studies appear.

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Citations

Jun 25, 2019·Journal of Evaluation in Clinical Practice·Lifeng Lin
Feb 17, 2021·Journal of Clinical Epidemiology·Chang XuSunita Vohra
Apr 5, 2020·Journal of Clinical Epidemiology·Chang XuXin Sun
Nov 20, 2021·Statistics in Medicine·Yipeng WangHaitao Chu

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