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...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| 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 |
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oai:ddd.uab.cat:226321 |
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España |
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| dc.title.none.fl_str_mv |
An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group |
| title |
An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group |
| spellingShingle |
An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group Boedhoe, Premika Neuroimaging MRI IPD meta-analysis Mega-analysis Linear mixed-effect models |
| title_short |
An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group |
| title_full |
An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group |
| title_fullStr |
An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group |
| title_full_unstemmed |
An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group |
| title_sort |
An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group |
| dc.creator.none.fl_str_mv |
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. |
| author |
Boedhoe, Premika |
| author_facet |
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. |
| author_role |
author |
| author2 |
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. |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Neuroimaging MRI IPD meta-analysis Mega-analysis Linear mixed-effect models |
| topic |
Neuroimaging MRI IPD meta-analysis Mega-analysis Linear mixed-effect models |
| description |
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 |
| publishDate |
2019 |
| dc.date.none.fl_str_mv |
2 2019-01-01 2019 2019-01-01 |
| dc.type.none.fl_str_mv |
Article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://ddd.uab.cat/record/226321 https://dx.doi.org/urn:doi:10.3389/fninf.2018.00102 |
| url |
https://ddd.uab.cat/record/226321 https://dx.doi.org/urn:doi:10.3389/fninf.2018.00102 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.relation.none.fl_str_mv |
Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2017/SGR-1247 Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2014/SGR-489 |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by/4.0/ |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.source.none.fl_str_mv |
reponame:Dipòsit Digital de Documents de la UAB instname:Universitat Autònoma de Barcelona |
| instname_str |
Universitat Autònoma de Barcelona |
| reponame_str |
Dipòsit Digital de Documents de la UAB |
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Dipòsit Digital de Documents de la UAB |
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1869419894292873216 |
| spelling |
An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working groupBoedhoe, PremikaHeymans, Martijn W.Schmaal, Lianne|||0000-0001-9822-048XAbe, Yoshinari|||0000-0001-8348-0801Alonso, Pino|||0000-0002-5779-9111Ameis, Stephanie H.|||0000-0002-7282-6077Anticevic, AlanArnold, Paul D.|||0000-0003-2496-4624Batistuzzo, Marcelo C.|||0000-0003-1347-8241Benedetti, Francesco|||0000-0003-4949-856XBeucke, Jan C.Bollettini, Irene|||0000-0003-3666-3538Bose, Anushree|||0000-0002-3394-1646Brem, SilviaCalvo, AnnaCalvo-Escalona, Rosa|||0000-0001-9572-4228Cheng, YuqiCho, Kang Ik K.Ciullo, Valentina|||0000-0002-4095-7806Dallaspezia, SaraDenys, Damiaan|||0000-0002-3191-3844Feusner, Jamie D.Fitzgerald, Kate D.Fouche, Jean-Paul|||0000-0002-0830-2324Fridgeirsson, Egill A.Gruner, PatriciaHanna, Gregory L.|||0000-0002-0742-6990Hibar, Derrek P.Hoexter, Marcelo Q.Hu, HaoHuyser, Chaim|||0000-0001-8757-3124Jahanshad, Neda|||0000-0003-4401-8950James, Anthony|||0000-0002-2742-8328Kathmann, Norbert|||0000-0002-1348-7060Kaufmann, ChristianKoch, KathrinKwon, Jun Soo|||0000-0002-1060-1462Lázaro, Luisa|||0000-0002-8425-5750Lochner, Christine|||0000-0002-4766-3704Marsh, Rachel|||0000-0003-2439-6305Martínez-Zalacaín, Ignacio|||0000-0002-4036-0284Mataix-Cols, David|||0000-0002-4545-0924Menchón Magriñá, José Manuel|||0000-0002-6231-6524Minuzzi, LucianoMorer, AstridNakamae, Takashi|||0000-0003-4265-198XNakao, TomohiroNarayanaswamy, Janardhanan C.Nishida, SeijiNurmi, Erika L.|||0000-0003-4893-8957O'Neill, JosephPiacentini, John|||0000-0003-4195-7194Piras, Fabrizio|||0000-0003-3566-5494Piras, Federica|||0000-0002-9546-7038Reddy, Y. C. JanardhanReess, Tim J.Sakai, Yuki|||0000-0003-2475-8548Sato, Joao R.Simpson, H. BlairSoreni, NoamSoriano-Mas, Carles|||0000-0003-4574-6597Spalletta, Gianfranco|||0000-0002-7432-4249Stevens, Michael C.|||0000-0002-3799-5465Szeszko, Philip R.Tolin, David F.van Wingen, Guido|||0000-0003-3076-5891Venkatasubramanian, Ganesan|||0000-0002-0949-898XWalitza, Susanne|||0000-0002-8161-8683Wang, Zhen|||0000-0003-4319-5314Yun, Je-Yeon|||0000-0002-5531-2410Thompson, Paul M.|||0000-0002-4720-8867Stein, Dan J.|||0000-0001-7218-7810van den Heuvel, Odile A.|||0000-0002-9804-7653Twisk, Jos W. R.NeuroimagingMRIIPD meta-analysisMega-analysisLinear mixed-effect modelsObjective: 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 22019-01-0120192019-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/226321https://dx.doi.org/urn:doi:10.3389/fninf.2018.00102reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2017/SGR-1247Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2014/SGR-489open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2263212026-06-06T12:50:31Z |
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15.298079 |