An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group. Boedhoe, P., S., W., Heymans, M., W., Schmaal, L., Abe, Y., Alonso, P., Ameis, S., H., Anticevic, A., Arnold, P., D., Batistuzzo, M., C., Benedetti, F., Beucke, J., C., Bollettini, I., Bose, A., Brem, S., Calvo, A., Calvo, R., Cheng, Y., Cho, K., I., K., Ciullo, V., Dallaspezia, S., Denys, D., Feusner, J., D., Fitzgerald, K., D., Fouche, J., Fridgeirsson, E., A., Gruner, P., Hanna, G., L., Hibar, D., P., Hoexter, M., Q., Hu, H., Huyser, C., Jahanshad, N., James, A., Kathmann, N., Kaufmann, C., Koch, K., Kwon, J., S., Lazaro, L., Lochner, C., Marsh, R., Martínez-Zalacaín, I., Mataix-Cols, D., Menchón, J., M., Minuzzi, L., Morer, A., Nakamae, T., Nakao, T., Narayanaswamy, J., C., Nishida, S., Nurmi, E., L., O'Neill, J., Piacentini, J., Piras, F., Piras, F., Reddy, Y., C., J., Reess, T., J., Sakai, Y., Sato, J., R., Simpson, H., B., Soreni, N., Soriano-Mas, C., Spalletta, G., Stevens, M., C., Szeszko, P., R., Tolin, D., F., van Wingen, G., A., Venkatasubramanian, G., Walitza, S., Wang, Z., Yun, J., Working-Group, E., Thompson, P., M., Stein, D., J., van den Heuvel, O., A., & Twisk, J., W., R. Frontiers in Neuroinformatics, 8, 2019.
An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group [pdf]Website  abstract   bibtex   
Abstract Objective: Brain imaging communities focusing on different diseases increasingly start collaborating and pooling data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data 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, but also with a linear mixed–effects random-intercept mega-analysis model, using data from 38 cohorts including 3665 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 best approach to investigate structural neuroimaging data.

Downloads: 0