title: A hierarchical modeling approach to data analysis and study design in a multi-site experimental fMRI study.
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.
authors.affiliation: University of California, Irvine, Irvine, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhou
authors.forename: Anna
authors.initials: A
authors.lastname: Konstorum
authors.forename: Thao
authors.initials: T
authors.lastname: Duong
authors.forename: Kinh H
authors.initials: KH
authors.lastname: Tieu
authors.forename: William M
authors.initials: WM
authors.lastname: Wells
authors.forename: Gregory G
authors.initials: GG
authors.lastname: Brown
authors.forename: Hal S
authors.initials: HS
authors.lastname: Stern
authors.forename: Babak
authors.initials: B
authors.lastname: Shahbaba
mesh_terms: D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D005260:Female; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000379:methods*
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