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2019-01-08Zeitschriftenartikel DOI: 10.3389/fninf.2018.00102
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
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 cc
Kaufmann, Christian cc
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
Thompson, Paul M.
Stein, Dan J.
van den Heuvel, Odile A.
Twisk, Jos W. R.
Lebenswissenschaftliche Fakultät
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.
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DOI
10.3389/fninf.2018.00102
Permanent URL
https://doi.org/10.3389/fninf.2018.00102
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<a href="https://doi.org/10.3389/fninf.2018.00102">https://doi.org/10.3389/fninf.2018.00102</a>