An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group Academic Article uri icon

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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.

authors

  • Boedhoe, Premika SW
  • Heymans, Martijn W
  • Schmaal, Lianne
  • Abe, Yoshinari
  • Alonso, Pino
  • Ameis, Stephanie H
  • Anticevic, Alan
  • Arnold, Paul D
  • Batistuzzo, Marcelo C
  • Benedetti, Francesco
  • Beucke, Jan C
  • Bollettini, Irene
  • Bose, Anushree
  • Brem, Silvia
  • Calvo, Anna
  • Calvo, Rosa
  • Cheng, Yuqi
  • Cho, Kang Ik K
  • Ciullo, Valentina
  • Dallaspezia, Sara
  • Denys, Damiaan
  • Feusner, Jamie D
  • Fitzgerald, Kate D
  • Fouche, Jean-Paul
  • Fridgeirsson, Egill A
  • Gruner, Patricia
  • Hanna, Gregory L
  • Hibar, Derrek P
  • Hoexter, Marcelo Q
  • Hu, Hao
  • Huyser, Chaim
  • Jahanshad, Neda
  • James, Anthony
  • Kathmann, Norbert
  • Kaufmann, Christian
  • Koch, Kathrin
  • Kwon, Jun Soo
  • Lazaro, Luisa
  • Lochner, Christine
  • Marsh, Rachel
  • Martínez-Zalacaín, Ignacio
  • Mataix-Cols, David
  • Menchón, José M
  • Minuzzi, Luciano
  • Morer, Astrid
  • Nakamae, Takashi
  • Nakao, Tomohiro
  • Narayanaswamy, Janardhanan C
  • Nishida, Seiji
  • Nurmi, Erika L
  • O'Neill, Joseph
  • Piacentini, John
  • Piras, Fabrizio
  • Piras, Federica
  • Reddy, YC Janardhan
  • Reess, Tim J
  • Sakai, Yuki
  • Sato, Joao R
  • Simpson, H Blair
  • Soreni, Noam
  • Soriano-Mas, Carles
  • Spalletta, Gianfranco
  • Stevens, Michael C
  • Szeszko, Philip R
  • Tolin, David F
  • van Wingen, Guido A
  • Venkatasubramanian, Ganesan
  • Walitza, Susanne
  • Wang, Zhen
  • Yun, Je-Yeon
  • Thompson, Paul M
  • Stein, Dan J
  • van den Heuvel, Odile A
  • Twisk, Jos WR

publication date

  • 2018