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dc.contributor.authorHunanyan, Sona
dc.contributor.authorRue, Haavard
dc.contributor.authorPlummer, Martyn
dc.contributor.authorRoos, Małgorzata
dc.date.accessioned2021-09-29T06:33:12Z
dc.date.available2021-09-29T06:33:12Z
dc.date.issued2021-09-24
dc.identifier.urihttp://hdl.handle.net/10754/672029
dc.description.abstractThe popular Bayesian meta-analysis expressed by Bayesian normal-normal hierarchical model (NNHM) synthesizes knowledge from several studies and is highly relevant in practice. Moreover, NNHM is the simplest Bayesian hierarchical model (BHM), which illustrates problems typical in more complex BHMs. Until now, it has been unclear to what extent the data determines the marginal posterior distributions of the parameters in NNHM. To address this issue we computed the second derivative of the Bhattacharyya coefficient with respect to the weighted likelihood, defined the total empirical determinacy (TED), the proportion of the empirical determinacy of location to TED (pEDL), and the proportion of the empirical determinacy of spread to TED (pEDS). We implemented this method in the R package \texttt{ed4bhm} and considered two case studies and one simulation study. We quantified TED, pEDL and pEDS under different modeling conditions such as model parametrization, the primary outcome, and the prior. This clarified to what extent the location and spread of the marginal posterior distributions of the parameters are determined by the data. Although these investigations focused on Bayesian NNHM, the method proposed is applicable more generally to complex BHMs.
dc.description.sponsorshipSupport by the Swiss National Science Foundation (no. 175933) granted to Ma lgorzata Roos is gratefully acknowledged
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2109.11870.pdf
dc.rightsArchived with thanks to arXiv
dc.subjectEmpirical determinacy
dc.subjectlikelihood weighting
dc.subjectBayesian meta-analysis
dc.subjectBayesian hierarchical models
dc.subjectidentification
dc.titleQuantification of empirical determinacy: the impact of likelihood weighting on posterior location and spread in Bayesian meta-analysis estimated with JAGS and INLA
dc.typePreprint
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Biostatistics at the Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland
dc.contributor.institutionDepartment of Statistics, University of Warwick, United Kingdom
dc.identifier.arxivid2109.11870
kaust.personRue, Haavard
refterms.dateFOA2021-09-29T06:35:45Z


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