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http://hdl.handle.net/2445/172000
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DC Field | Value | Language |
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dc.contributor.author | Alonso, Pino | - |
dc.contributor.author | Calvo, Ana | - |
dc.contributor.author | Lazaro, Luisa | - |
dc.contributor.author | Martínez Zalacaín, Ignacio | - |
dc.contributor.author | Menchón Magriñá, José Manuel | - |
dc.contributor.author | Morer Liñán, Astrid | - |
dc.contributor.author | Soriano Mas, Carles | - |
dc.contributor.author | ENIGMA-OCD Working-Group | - |
dc.date.accessioned | 2020-11-12T14:29:40Z | - |
dc.date.available | 2020-11-12T14:29:40Z | - |
dc.date.issued | 2019-01 | - |
dc.identifier.issn | 1662-5196 | - |
dc.identifier.uri | http://hdl.handle.net/2445/172000 | - |
dc.description.abstract | 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 | - |
dc.format.extent | 8 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Frontiers Media | - |
dc.relation.isformatof | Reproducció del document publicat a: | - |
dc.relation.ispartof | Frontiers in Neuroinformatics, 2019, vol. 12 | - |
dc.rights | cc-by (c) Alonso, Pino et al., 2019 | - |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es | - |
dc.source | Articles publicats en revistes (Ciències Clíniques) | - |
dc.subject.classification | Neurosi obsessiva | - |
dc.subject.classification | Impulsos (Psicologia) | - |
dc.subject.other | Obsessive-compulsive disorder | - |
dc.subject.other | Impulse | - |
dc.title | An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.idgrec | 687082 | - |
dc.date.updated | 2020-11-12T14:29:40Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
dc.identifier.pmid | 30670959 | - |
Appears in Collections: | Articles publicats en revistes (Medicina) Articles publicats en revistes (Ciències Clíniques) Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer) Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) |
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