Assessing robustness against potential publication bias in Activation Likelihood Estimation (ALE) meta-analyses for fMRI
Assessing robustness against potential publication bias in Activation Likelihood Estimation (ALE) meta-analyses for fMRI
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DOI：
10.1371/journal.pone.0208177
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发表时间：
2018-11-30
期刊：
影响因子：
3.7
通讯作者：
Moerkerke, Beatrijs
中科院分区：
文献类型：
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作者：
Acar, Freya;Seurinck, Ruth;Moerkerke, Beatrijs
整合研究结果的重要性是无可争议的，基于坐标的meta分析（CBMA）程序，如激活似然估计（ALE）已经成为一种流行的方法，当只报告激活峰时，将fMRI研究结果结合起来。由于元分析的发现有助于积累知识并指导未来的研究，因此不仅分析的质量，得出结论的方式也极其重要。与传统的荟萃分析一样，基于坐标的荟萃分析也可能受到不同形式的发表偏倚的影响，这可能会影响结果并使发现无效。“文件抽屉问题”指的是由于研究没有获得预期结果（如缺乏统计显著性）而无法发表的问题。为了评估meta分析结果的稳定性，并确定其对潜在的文件抽屉问题的稳健性，我们提出了一种算法，以确定在特定区域的研究报告的激活峰的空间收敛不再具有统计学意义之前，可以添加到现有ALE fMRI meta分析中的噪声研究的数量。虽然其他基于坐标的元分析工具箱（如用于多级核密度分析（MKDA）的Galbraith图和用于基于种子的d映射的漏斗图和egger检验）也存在深入了解结果有效性和局限性的方法，但本程序是首次评估ALE算法对潜在发表偏倚的稳健性。该方法通过观察在未报告的信息（可能与包含的信息有系统差异）存在的情况下，结果如何保持稳定，有助于以适当的谨慎态度解释元分析结果。同时，该程序提供了对驱动meta分析结果的研究数量的进一步了解。我们通过一个例子说明了这个过程，并通过大量的仿真测试了几个参数的效果。生成噪声研究的代码是免费提供的，这使得用户在解释他们的结果时可以很容易地使用算法。
The importance of integrating research findings is incontrovertible and procedures for coordinate-based meta-analysis (CBMA) such as Activation Likelihood Estimation (ALE) have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. As meta-analytical findings help building cumulative knowledge and guide future research, not only the quality of such analyses but also the way conclusions are drawn is extremely important. Like classical meta-analyses, coordinate-based meta-analyses can be subject to different forms of publication bias which may impact results and invalidate findings. The file drawer problem refers to the problem where studies fail to get published because they do not obtain anticipated results (e.g. due to lack of statistical significance). To enable assessing the stability of meta-analytical results and determine their robustness against the potential presence of the file drawer problem, we present an algorithm to determine the number of noise studies that can be added to an existing ALE fMRI meta-analysis before spatial convergence of reported activation peaks over studies in specific regions is no longer statistically significant. While methods to gain insight into the validity and limitations of results exist for other coordinate-based meta-analysis toolboxes, such as Galbraith plots for Multilevel Kernel Density Analysis (MKDA) and funnel plots and egger tests for seed-based d mapping, this procedure is the first to assess robustness against potential publication bias for the ALE algorithm. The method assists in interpreting meta-analytical results with the appropriate caution by looking how stable results remain in the presence of unreported information that may differ systematically from the information that is included. At the same time, the procedure provides further insight into the number of studies that drive the meta-analytical results. We illustrate the procedure through an example and test the effect of several parameters through extensive simulations. Code to generate noise studies is made freely available which enables users to easily use the algorithm when interpreting their results.