Department of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany.
Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Res Synth Methods. 2018 Sep;9(3):382-392. doi: 10.1002/jrsm.1297. Epub 2018 Apr 6.
In systematic reviews, meta-analyses are routinely applied to summarize the results of the relevant studies for a specific research question. If one can assume that in all studies the same true effect is estimated, the application of a meta-analysis with common effect (commonly referred to as fixed-effect meta-analysis) is adequate. If between-study heterogeneity is expected to be present, the method of choice is a meta-analysis with random effects. The widely used DerSimonian and Laird method for meta-analyses with random effects has been criticized due to its unfavorable statistical properties, especially in the case of very few studies. A working group of the Cochrane Collaboration recommended the use of the Knapp-Hartung method for meta-analyses with random effects. However, as heterogeneity cannot be reliably estimated if only very few studies are available, the Knapp-Hartung method, while correctly accounting for the corresponding uncertainty, has very low power. Our aim is to summarize possible methods to perform meaningful evidence syntheses in the situation with only very few (ie, 2-4) studies. Some general recommendations are provided on which method should be used when. Our recommendations are based on the existing literature on methods for meta-analysis with very few studies and consensus of the authors. The recommendations are illustrated by 2 examples coming from dossier assessments of the Institute for Quality and Efficiency in Health Care.
在系统评价中,通常会应用荟萃分析来总结特定研究问题相关研究的结果。如果可以假设所有研究都估计了相同的真实效应,那么应用具有共同效应(通常称为固定效应荟萃分析)的荟萃分析是足够的。如果预计存在研究间异质性,则选择的方法是具有随机效应的荟萃分析。由于其不利的统计特性,特别是在研究数量非常少的情况下,广泛使用的具有随机效应的 DerSimonian 和 Laird 荟萃分析方法受到了批评。 Cochrane 协作组的一个工作组建议使用具有随机效应的 Knapp-Hartung 方法进行荟萃分析。然而,如果只有极少数研究可用,则无法可靠地估计异质性,虽然 Knapp-Hartung 方法正确考虑了相应的不确定性,但它的功效非常低。我们的目的是总结在仅有极少数(即 2-4 项)研究的情况下进行有意义的证据综合的可能方法。提供了一些一般性建议,说明在何时应使用哪种方法。我们的建议基于关于具有极少数研究的荟萃分析方法的现有文献和作者的共识。通过来自卫生保健质量和效率研究所档案评估的 2 个示例来说明建议。