An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Premika S. W. Boedhoe, Martijn W. Heymans, Lianne Schmaal, Yoshinari Abe, Pino Alonso, Stephanie H. Ameis, Alan Anticevic, Paul D. Arnold, Marcelo C. Batistuzzo, Francesco Benedetti, Jan C. Beucke, Irene Bollettini, Anushree Bose, Silvia Brem, Anna Calvo, Rosa Calvo, Yuqi Cheng, Kang Ik K. Cho, Valentina Ciullo, Sara DallaspeziaDamiaan Denys, Jamie D. Feusner, Kate D. Fitzgerald, Jean-Paul Fouche, Egill A. Fridgeirsson, Patricia Gruner, Gregory L. Hanna, Derrek P. Hibar, Marcelo Q. Hoexter, Hao Hu, Chaim Huyser, Neda Jahanshad, Anthony James, Norbert Kathmann, Christian Kaufmann, Kathrin Koch, Jun Soo Kwon, Luisa Lazaro, Christine Lochner, Rachel Marsh, Ignacio Martínez-Zalacaín, David Mataix-Cols, José M. Menchón, Luciano Minuzzi, Astrid Morer, Takashi Nakamae, Tomohiro Nakao, Janardhanan C. Narayanaswamy, Seiji Nishida, Erika L. Nurmi, Joseph O'Neill, John Piacentini, Fabrizio Piras, Federica Piras, Y. C. Janardhan Reddy, Tim J. Reess, Yuki Sakai, Joao R. Sato, H. Blair Simpson, Noam Soreni, Carles Soriano-Mas, Gianfranco Spalletta, Michael C. Stevens, Philip R. Szeszko, David F. Tolin, Guido A. van Wingen, Ganesan Venkatasubramanian, Susanne Walitza, Zhen Wang, Je-Yeon Yun, ENIGMA-OCD Working-Group, Paul M. Thompson, Dan J. Stein, Odile A. van den Heuvel, Jos W. R. Twisk

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
JournalFrontiers in Neuroinformatics
Publication statusPublished - 2019

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