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; Thompson, Paul M; Stein, Dan J; van den Heuvel, Odile A; Twisk, Jos W R     «

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...     »

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.      «

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...     »