Valentine Jeffrey C, Wilson Sandra Jo, Rindskopf David, Lau Timothy S, Tanner-Smith Emily E, Yeide Martha, LaSota Robin, Foster Lisa
1 University of Louisville, Louisville, KY, USA.
2 Vanderbilt University, Nashville, USA.
Eval Rev. 2017 Feb;41(1):3-26. doi: 10.1177/0193841X16674421. Epub 2016 Oct 25.
For a variety of reasons, researchers and evidence-based clearinghouses synthesizing the results of multiple studies often have very few studies that are eligible for any given research question. This situation is less than optimal for meta-analysis as it is usually practiced, that is, by employing inverse variance weights, which allows more informative studies to contribute relatively more to the analysis. This article outlines the choices available for synthesis when there are few studies to synthesize. As background, we review the synthesis practices used in several projects done at the behest of governmental agencies and private foundations. We then discuss the strengths and limitations of different approaches to meta-analysis in a limited information environment. Using examples from the U.S. Department of Education's What Works Clearinghouse as case studies, we conclude with a discussion of Bayesian meta-analysis as a potential solution to the challenges encountered when attempting to draw inferences about the effectiveness of interventions from a small number of studies.
由于多种原因,综合多项研究结果的研究人员和循证信息中心通常只有极少的研究符合任何特定的研究问题。这种情况对于通常所进行的荟萃分析而言并非最佳,也就是说,采用逆方差权重时,信息量更大的研究在分析中所占比重相对更大。本文概述了在可供综合的研究较少时可采用的选择。作为背景,我们回顾了应政府机构和私人基金会要求开展的几个项目中所采用的综合方法。然后我们讨论在信息有限的环境中不同荟萃分析方法的优缺点。以美国教育部“有效信息中心”的案例研究为例,我们最后讨论贝叶斯荟萃分析,它可能是在试图从少量研究中推断干预措施有效性时应对所遇到挑战的一种解决方案。