1. An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group
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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 Dallaspezia, Damiaan 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, Anatomy and neurosciences, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Psychiatry, Epidemiology and Data Science, APH - Personalized Medicine, APH - Methodology, APH - Health Behaviors & Chronic Diseases, ACS - Atherosclerosis & ischemic syndromes, Netherlands Institute for Neuroscience (NIN), 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, Bose, Anushree, Brem, Silvia, Calvo, Anna, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Gruner, Patricia, Hanna, Gregory L, Hibar, Derrek P, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kwon, Jun Soo, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Morer, Astrid, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nishida, Seiji, Nurmi, Erika L, O'Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, Y C Janardhan, Reess, Tim J, Sakai, Yuki, Sato, Joao R, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carle, Spalletta, Gianfranco, Stevens, Michael C, Szeszko, Philip R, Tolin, David F, van Wingen, Guido A, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Yun, Je-Yeon, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, Twisk, Jos W R, Adult Psychiatry, Graduate School, and Child Psychiatry
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mega-analysis ,Mega-analysis ,Biomedical Engineering ,Neuroscience (miscellaneous) ,Neuroimaging ,Linear mixed-effect models ,050105 experimental psychology ,lcsh:RC321-571 ,03 medical and health sciences ,0302 clinical medicine ,Obsessive compulsive ,Statistics ,Linear regression ,IPD meta-analysi ,0501 psychology and cognitive sciences ,ddc:610 ,IPD meta-analysis ,Association (psychology) ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,mega-analysi ,Mathematics ,Original Research ,linear mixed-effect model ,neuroimaging ,05 social sciences ,Variance (accounting) ,Confidence interval ,Computer Science Applications ,Standard error ,linear mixed-effect models ,Mega analysis ,610 Medizin und Gesundheit ,030217 neurology & neurosurgery ,Neuroscience ,MRI - Abstract
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.
- Published
- 2019
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