Frontiers in Neuroinformatics (Jan 2019)

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

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

DOI
https://doi.org/10.3389/fninf.2018.00102
Journal volume & issue
Vol. 12

Abstract

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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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