Basics of meta-analysis: I2 is not an absolute measure of heterogeneity
Basics of meta-analysis: I2 is not an absolute measure of heterogeneity
复制标题
DOI：
10.1002/jrsm.1230
复制
发表时间：
2017-03-01
影响因子：
9.8
通讯作者：
Rothstein, Hannah R.
中科院分区：
文献类型：
--
作者：
Borenstein, Michael;Higgins, Julian P. T.;Rothstein, Hannah R.
当我们在荟萃分析中谈论异质性时，我们的目的通常是为了理解异质性的实质性含义。如果一项干预措施产生的平均效应大小为 50 分，我们想知道不同人群的效应大小是否在 40 到 60 之间，或从 10 到 90 之间变化，因为这说明了干预措施的潜在效用。虽然人们普遍认为 I-2 统计数据提供了此信息，但实际上并没有。在此示例中，如果我们被告知 I-2 为 50%，我们无法知道效果的范围是从 40 到 60，还是从 10 到 90，还是跨越某个其他范围。相反，如果我们想传达预测的影响范围，那么我们应该简单地报告这个范围。这为读者提供了他们认为 I-2 正在捕获的信息，并且以简洁且明确的方式获取信息。版权所有 (C) 2017 约翰·威利父子有限公司
When we speak about heterogeneity in a meta-analysis, our intent is usually to understand the substantive implications of the heterogeneity. If an intervention yields a mean effect size of 50 points, we want to know if the effect size in different populations varies from 40 to 60, or from 10 to 90, because this speaks to the potential utility of the intervention. While there is a common belief that the I-2 statistic provides this information, it actually does not. In this example, if we are told that I-2 is 50%, we have no way of knowing if the effects range from 40 to 60, or from 10 to 90, or across some other range. Rather, if we want to communicate the predicted range of effects, then we should simply report this range. This gives readers the information they think is being captured by I-2 and does so in a way that is concise and unambiguous. Copyright (C) 2017 John Wiley & Sons, Ltd.