An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group - Boedhoe, Premika S. W.; Heymans, Martijn W.; Schmaal, Lianne; Abe, Yoshinari; Alonso, Pino; Ameis, Stephanie H.; Anticevic, Alan; Arnold, Paul D.; Batistuzzo, Marcelo C.; Benedetti, Francesco; Beucke, Jan C.; Bollettini, Irene; et al. (Frontiers in Neuroinformatics, 2019)
Abstract Objective: Brain imaging communities focusing on different diseases increasingly start collaborating and pooling data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data 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, but also with a linear mixed–effects random-intercept mega-analysis model, using data from 38 cohorts including 3665 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 best approach to investigate structural neuroimaging data.