@article{
 title = {An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group},
 type = {article},
 year = {2019},
 identifiers = {[object Object]},
 keywords = {IPD meta-analysis,MRI,Neuroimaging,linear mixed-effect models,mega-analysis},
 volume = {12},
 websites = {https://www.frontiersin.org/articles/10.3389/fninf.2018.00102/full,http://files/353/Boedhoe et al. - 2019 - An Empirical Comparison of Meta- and Mega-Analysis.pdf},
 month = {8},
 day = {25},
 id = {10256cc1-2257-374b-975a-069401f61783},
 created = {2020-09-17T09:27:49.672Z},
 file_attached = {false},
 profile_id = {20f87055-ac78-3c65-9cf5-216a3558d16a},
 group_id = {14ca8526-77d5-34fd-89de-e48cae5e6ee2},
 last_modified = {2020-09-17T09:27:49.672Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 language = {English},
 private_publication = {false},
 abstract = {Abstract Objective: Brain imaging communities focusing on different diseases increasingly start collaborating and pooling data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data 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, but also with a linear mixed–effects random-intercept mega-analysis model, using data from 38 cohorts including 3665 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 best approach to investigate structural neuroimaging data.},
 bibtype = {article},
 author = {Boedhoe, Premika S W and Heymans, Martijn W and Schmaal, Lianne and Abe, Yoshinari and Alonso, Pino and Ameis, Stephanie H and Anticevic, Alan and Arnold, Paul D and Batistuzzo, Marcelo C and Benedetti, Francesco and Beucke, Jan C and Bollettini, Irene and Bose, Anushree and Brem, Silvia and Calvo, Anna and Calvo, Rosa and Cheng, Yuqi and Cho, Kang Ik K and Ciullo, Valentina and Dallaspezia, Sara and Denys, Damiaan and Feusner, Jamie D and Fitzgerald, Kate D and Fouche, Jean-Paul and Fridgeirsson, Egill A and Gruner, Patricia and Hanna, Gregory L and Hibar, Derrek P and Hoexter, Marcelo Q and Hu, Hao and Huyser, Chaim and Jahanshad, Neda and James, Anthony and Kathmann, Norbert and Kaufmann, Christian and Koch, Kathrin and Kwon, Jun Soo and Lazaro, Luisa and Lochner, Christine and Marsh, Rachel and Martínez-Zalacaín, Ignacio and Mataix-Cols, David and Menchón, José M and Minuzzi, Luciano and Morer, Astrid and Nakamae, Takashi and Nakao, Tomohiro and Narayanaswamy, Janardhanan C and Nishida, Seiji and Nurmi, Erika L and O'Neill, Joseph and Piacentini, John and Piras, Fabrizio and Piras, Federica and Reddy, Y C Janardhan and Reess, Tim J and Sakai, Yuki and Sato, Joao R and Simpson, H Blair and Soreni, Noam and Soriano-Mas, Carles and Spalletta, Gianfranco and Stevens, Michael C and Szeszko, Philip R and Tolin, David F and van Wingen, Guido A and Venkatasubramanian, Ganesan and Walitza, Susanne and Wang, Zhen and Yun, Je-Yeon and Working-Group, Enigma-Ocd and Thompson, Paul M and Stein, Dan J and van den Heuvel, Odile A and Twisk, Jos W R},
 journal = {Frontiers in Neuroinformatics}
}

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Some methodologists claim that a one-stage individual-participant data 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, but also with a linear mixed–effects random-intercept mega-analysis model, using data from 38 cohorts including 3665 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 best approach to investigate structural neuroimaging data.","bibtype":"article","author":"Boedhoe, Premika S W and Heymans, Martijn W and Schmaal, Lianne and Abe, Yoshinari and Alonso, Pino and Ameis, Stephanie H and Anticevic, Alan and Arnold, Paul D and Batistuzzo, Marcelo C and Benedetti, Francesco and Beucke, Jan C and Bollettini, Irene and Bose, Anushree and Brem, Silvia and Calvo, Anna and Calvo, Rosa and Cheng, Yuqi and Cho, Kang Ik K and Ciullo, Valentina and Dallaspezia, Sara and Denys, Damiaan and Feusner, Jamie D and Fitzgerald, Kate D and Fouche, Jean-Paul and Fridgeirsson, Egill A and Gruner, Patricia and Hanna, Gregory L and Hibar, Derrek P and Hoexter, Marcelo Q and Hu, Hao and Huyser, Chaim and Jahanshad, Neda and James, Anthony and Kathmann, Norbert and Kaufmann, Christian and Koch, Kathrin and Kwon, Jun Soo and Lazaro, Luisa and Lochner, Christine and Marsh, Rachel and Martínez-Zalacaín, Ignacio and Mataix-Cols, David and Menchón, José M and Minuzzi, Luciano and Morer, Astrid and Nakamae, Takashi and Nakao, Tomohiro and Narayanaswamy, Janardhanan C and Nishida, Seiji and Nurmi, Erika L and O'Neill, Joseph and Piacentini, John and Piras, Fabrizio and Piras, Federica and Reddy, Y C Janardhan and Reess, Tim J and Sakai, Yuki and Sato, Joao R and Simpson, H Blair and Soreni, Noam and Soriano-Mas, Carles and Spalletta, Gianfranco and Stevens, Michael C and Szeszko, Philip R and Tolin, David F and van Wingen, Guido A and Venkatasubramanian, Ganesan and Walitza, Susanne and Wang, Zhen and Yun, Je-Yeon and Working-Group, Enigma-Ocd and Thompson, Paul M and Stein, Dan J and van den Heuvel, Odile A and Twisk, Jos W R","journal":"Frontiers in Neuroinformatics","bibtex":"@article{\n title = {An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group},\n type = {article},\n year = {2019},\n identifiers = {[object Object]},\n keywords = {IPD meta-analysis,MRI,Neuroimaging,linear mixed-effect models,mega-analysis},\n volume = {12},\n websites = {https://www.frontiersin.org/articles/10.3389/fninf.2018.00102/full,http://files/353/Boedhoe et al. - 2019 - An Empirical Comparison of Meta- and Mega-Analysis.pdf},\n month = {8},\n day = {25},\n id = {10256cc1-2257-374b-975a-069401f61783},\n created = {2020-09-17T09:27:49.672Z},\n file_attached = {false},\n profile_id = {20f87055-ac78-3c65-9cf5-216a3558d16a},\n group_id = {14ca8526-77d5-34fd-89de-e48cae5e6ee2},\n last_modified = {2020-09-17T09:27:49.672Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n language = {English},\n private_publication = {false},\n abstract = {Abstract Objective: Brain imaging communities focusing on different diseases increasingly start collaborating and pooling data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data 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, but also with a linear mixed–effects random-intercept mega-analysis model, using data from 38 cohorts including 3665 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. 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