An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group

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 agg...

Descripción completa

Detalles Bibliográficos
Autores: Boedhoe, Premika, Heymans, Martijn W., Schmaal, Lianne|||0000-0001-9822-048X, Abe, Yoshinari|||0000-0001-8348-0801, Alonso, Pino|||0000-0002-5779-9111, Ameis, Stephanie H.|||0000-0002-7282-6077, Anticevic, Alan, Arnold, Paul D.|||0000-0003-2496-4624, Batistuzzo, Marcelo C.|||0000-0003-1347-8241, Benedetti, Francesco|||0000-0003-4949-856X, Beucke, Jan C., Bollettini, Irene|||0000-0003-3666-3538, Bose, Anushree|||0000-0002-3394-1646, Brem, Silvia, Calvo, Anna, Calvo-Escalona, Rosa|||0000-0001-9572-4228, Cheng, Yuqi, Cho, Kang Ik K., Ciullo, Valentina|||0000-0002-4095-7806, Dallaspezia, Sara, Denys, Damiaan|||0000-0002-3191-3844, Feusner, Jamie D., Fitzgerald, Kate D., Fouche, Jean-Paul|||0000-0002-0830-2324, Fridgeirsson, Egill A., Gruner, Patricia, Hanna, Gregory L.|||0000-0002-0742-6990, Hibar, Derrek P., Hoexter, Marcelo Q., Hu, Hao, Huyser, Chaim|||0000-0001-8757-3124, Jahanshad, Neda|||0000-0003-4401-8950, James, Anthony|||0000-0002-2742-8328, Kathmann, Norbert|||0000-0002-1348-7060, Kaufmann, Christian, Koch, Kathrin, Kwon, Jun Soo|||0000-0002-1060-1462, Lázaro, Luisa|||0000-0002-8425-5750, Lochner, Christine|||0000-0002-4766-3704, Marsh, Rachel|||0000-0003-2439-6305, Martínez-Zalacaín, Ignacio|||0000-0002-4036-0284, Mataix-Cols, David|||0000-0002-4545-0924, Menchón Magriñá, José Manuel|||0000-0002-6231-6524, Minuzzi, Luciano, Morer, Astrid, Nakamae, Takashi|||0000-0003-4265-198X, Nakao, Tomohiro, Narayanaswamy, Janardhanan C., Nishida, Seiji, Nurmi, Erika L.|||0000-0003-4893-8957, O'Neill, Joseph, Piacentini, John|||0000-0003-4195-7194, Piras, Fabrizio|||0000-0003-3566-5494, Piras, Federica|||0000-0002-9546-7038, Reddy, Y. C. Janardhan, Reess, Tim J., Sakai, Yuki|||0000-0003-2475-8548, Sato, Joao R., Simpson, H. Blair, Soreni, Noam, Soriano-Mas, Carles|||0000-0003-4574-6597, Spalletta, Gianfranco|||0000-0002-7432-4249, Stevens, Michael C.|||0000-0002-3799-5465, Szeszko, Philip R., Tolin, David F., van Wingen, Guido|||0000-0003-3076-5891, Venkatasubramanian, Ganesan|||0000-0002-0949-898X, Walitza, Susanne|||0000-0002-8161-8683, Wang, Zhen|||0000-0003-4319-5314, Yun, Je-Yeon|||0000-0002-5531-2410, Thompson, Paul M.|||0000-0002-4720-8867, Stein, Dan J.|||0000-0001-7218-7810, van den Heuvel, Odile A.|||0000-0002-9804-7653, Twisk, Jos W. R.
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:226321
Acceso en línea:https://ddd.uab.cat/record/226321
https://dx.doi.org/urn:doi:10.3389/fninf.2018.00102
Access Level:acceso abierto
Palabra clave:Neuroimaging
MRI
IPD meta-analysis
Mega-analysis
Linear mixed-effect models
Descripción
Sumario: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

Similares en LA Referencia