Interpretation of tests of heterogeneity and bias in meta-analysis
Interpretation of tests of heterogeneity and bias in meta-analysis
复制标题
DOI：
10.1111/j.1365-2753.2008.00986.x
复制
发表时间：
2008-10-01
影响因子：
2.4
通讯作者：
Ioannidis, John P. A.
中科院分区：
文献类型：
--
作者：
Ioannidis, John P. A.
异质性和偏倚的统计检验，特别是发表偏倚，在荟萃分析中非常流行。这些测试使用统计方法，其局限性往往没有被认识到。此外，它往往暗示与不适当的信心，这些测试可以提供可靠的答案，在本质上是不属于统计性质的问题。统计异质性只是临床和实用异质性的相关性，有时相关性可能很弱。同样，统计信号可能暗示偏倚，但孤立地看，它们不能完全证明或反驳一般偏倚，更不用说偏倚的具体原因，如特别是出版偏倚。异质性和偏倚的假阳性和假阴性信号可能很常见，基于一些合理的考虑，可以预测其患病率。在这里，我讨论了在荟萃分析中使用和解释异质性和偏倚的统计检验时出现的主要常见挑战和缺陷。我讨论的误解，可能会发生在统计推断，临床/语用推理和具体原因归因的水平。本文就如何避免这些缺陷，正确使用这些测试并从中学习提出了建议。
Statistical tests of heterogeneity and bias, in particular publication bias, are very popular in meta-analyses. These tests use statistical approaches whose limitations are often not recognized. Moreover, it is often implied with inappropriate confidence that these tests can provide reliable answers to questions that in essence are not of statistical nature. Statistical heterogeneity is only a correlate of clinical and pragmatic heterogeneity and the correlation may sometimes be weak. Similarly, statistical signals may hint to bias, but seen in isolation they cannot fully prove or disprove bias in general, let alone specific causes of bias, such as publication bias in particular. Both false-positive and false-negative signals of heterogeneity and bias can be common and their prevalence may be anticipated based on some rational considerations. Here I discuss the major common challenges and flaws that emerge in using and interpreting statistical tests of heterogeneity and bias in meta-analyses. I discuss misinterpretations that can occur at the level of statistical inference, clinical/pragmatic inference and specific cause attribution. Suggestions are made on how to avoid these flaws, use these tests properly and learn from them.