A Hierarchical Modeling Approach to Data Analysis and Study Design in a Multi-Site Experimental fMRI Study

Autor/inn/en	Zhou, Bo; Konstorum, Anna; Duong, Thao; Tieu, Kinh H.; Wells, William M.; Brown, Gregory G.; Stern, Hal S.; Shahbaba, Babak
Titel	A Hierarchical Modeling Approach to Data Analysis and Study Design in a Multi-Site Experimental fMRI Study
Quelle	In: Psychometrika, 78 (2013) 2, S.260-278 (19 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-012-9298-9
Schlagwörter	Brain; Diagnostic Tests; Bayesian Statistics; Hierarchical Linear Modeling; Data Analysis; Research Design; Goodness of Fit; Experiments; Comparative Analysis + Suchen Sie Ihr Suchwort? Gehirn; Diagnostic test; Diagnostischer Test; Auswertung; Forschungsdesign; Erprobung
Abstract	We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model selection based on the deviance information criterion (DIC), we show that our model provides a good fit to the observed data by sharing information across the sites. We also propose a simple approach for evaluating the efficacy of the multi-site experiment by comparing the results to those that would be expected in hypothetical single-site experiments with the same sample size. (As Provided).
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Update	2017/4/10