Quantifying heterogeneity in a meta-analysis
Quantifying heterogeneity in a meta-analysis
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DOI：
10.1002/sim.1186
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发表时间：
2002-06-15
影响因子：
2
通讯作者：
Thompson, SG
中科院分区：
文献类型：
--
作者：
Higgins, JPT;Thompson, SG
荟萃分析中异质性的程度部分决定了得出总体结论的难度。这种程度可以通过估计研究间方差来测量，但解释则特定于特定的治疗效果指标。存在异质性检验，但取决于荟萃分析中的研究数量。我们从数学标准出发，开发了异质性对荟萃分析影响的测量方法，该方法独立于研究数量和治疗效果度量。我们推导并提出了三个合适的统计量：H是χ（2）异质性统计量除以其自由度的平方根; R是随机效应荟萃分析的基础平均值的标准误与固定效应荟萃分析估计值的标准误的比值，I-2是H的转换，描述了研究估计值中由异质性引起的总变异的比例。我们讨论的解释，区间估计和这些措施的其他属性，并检查他们在五个示例数据集显示不同数量的异质性。我们的结论是，H和I-2，通常可以计算发表的荟萃分析，异质性的影响是特别有用的总结。应在已发表的荟萃分析中列出其中一项或两项，优先于异质性检验。版权所有（C）2002约翰威利父子有限公司
The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi(2) heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I-2 is a transformation of H that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I-2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright (C) 2002 John Wiley Sons, Ltd.