Journal
FRONTIERS IN NEUROINFORMATICS
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fninf.2018.00102
Keywords
neuroimaging; MRI; IPD meta-analysis; mega-analysis; linear mixed-effect models
Funding
- Biocodex
- Lundbeck
- Sun
- NIH BD2K award [U54 EB020403]
- Neuroscience Amsterdam, IPB-grant
- Hartmann Muller Foundation [1460]
- International Obsessive-Compulsive Disorder Foundation (IOCDF) Research Award
- Dutch Organization for Scientific Research (NWO) [912-02-050, 907-00-012, 940-37-018, 916.86.038]
- Netherlands Society for Scientific Research (NWO-ZonMw VENI grant) [916.86.036]
- Netherlands Society for Scientific Research (NWO-ZonMw AGIKO stipend) [920-03-542]
- NARSAD Young Investigator Award
- Netherlands Brain Foundation [2010(1)-50]
- Oxfordshire Health Services Research Committee (OHSRC)
- Deutsche Forschungsgemeinschaft (DFG) [KO 3744/2-1]
- Marato TV3 Foundation grants [01/2010, 091710]
- Wellcome Trust
- South London and Maudsley Trust, London, UK [064846]
- Japanese Ministry of Education, Culture, Sports, Science, and Technology (MEXT KAKENHI) [16K19778, 18K07608]
- International OCD Foundation Research Award [20153694]
- UCLA Clinical and Translational Science Institute Award
- National Institutes of Mental Health [R01MH081864, R01MH085900]
- Government of India [SR/S0/HS/0016/2011]
- Department of Science and Technology [IFA12-LSBM-26]
- Wellcome-DBT India Alliance grant [500236/Z/11/Z]
- Carlos III Health Institute [CP10/00604, PI13/00918, PI13/01958, PI14/00413/PI040829, PI16/00889]
- FEDER funds/European Regional Development Fund (ERDF), AGAUR [2017 SGR 1247, 2014 SGR 489]
- Miguel Servet contract from the Carlos III Health Institute [CPII16/00048]
- Italian Ministry of Health [RC10-11-12-13-14-15A]
- Swiss National Science Foundation [320030_130237]
- Netherlands Organization for Scientific Research [NWO VIDI 917-15-318]
- Grants-in-Aid for Scientific Research [16K19778, 18K07608] Funding Source: KAKEN
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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.
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