AUTHOR=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 Carles , Spalletta Gianfranco , Stevens Michael C. , Szeszko Philip R. , Tolin David F. , van Wingen Guido A. , Venkatasubramanian Ganesan , Walitza Susanne , Wang Zhen , Yun Je-Yeon ,  ENIGMA-OCD Working-Group , Thompson Paul M. , Stein Dan J. , van den Heuvel Odile A. , Twisk Jos W. R. 

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

JOURNAL=Frontiers in Neuroinformatics

VOLUME=12

YEAR=2019

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2018.00102

DOI=10.3389/fninf.2018.00102

ISSN=1662-5196

ABSTRACT=<p><bold>Objective:</bold> 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.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results:</bold> 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.</p><p><bold>Conclusions:</bold> 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.</p>