An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group
An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group
复制标题
DOI：
10.3389/fninf.2018.00102
复制
发表时间：
2019-01-08
影响因子：
3.5
通讯作者：
Twisk, Jos W. R.
中科院分区：
文献类型：
--
作者：
Boedhoe, Premika S. W.;Heymans, Martijn W.;Twisk, Jos W. R.
目的：专注于不同疾病的脑成像社区越来越多地开始合作并汇集数据，以进行强大的Meta分析和大型分析。一些方法学家声称，一个阶段的个人参与者数据（IPD）的大分析可以上级两阶段的汇总数据的元分析，因为更详细的计算可以在大分析中进行。在得出任何一种方法的性能的明确结论之前，有必要对Meta分析和巨分析的方法和结果进行批判性评估。方法：在这里，我们比较了逆方差加权随机效应荟萃分析模型与多元线性回归大分析模型，以及线性混合效应随机截距大分析模型，使用来自38个队列的数据，包括ENIGMA-OCD联盟的3，665名参与者。我们评估的效果大小和标准误，和适合的模型，以评估不同的methods.Results的性能：大分析模型显示较低的标准误和较窄的置信区间比荟萃分析。线性回归和线性混合效应随机截距模型的标准误和置信区间相似。此外，线性混合效应的随机截距模型表现出更好的拟合指数相比，线性回归大analytical models.Conclusions：我们的研究结果表明，通过Meta和大分析得到的结果不同，有利于后者。在队列间存在中等变异的多中心研究中，线性混合效应随机截距大型分析框架似乎是研究结构神经影像学数据的更好方法。
Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses.Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods.Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models.Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.