{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Roos M"],"funding":["Swiss National Science Foundation"],"pagination":["1555-1574"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9292837"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["63(8)"],"pubmed_abstract":["In recent years, Bayesian meta-analysis expressed by a normal-normal hierarchical model (NNHM) has been widely used for combining evidence from multiple studies. Data provided for the NNHM are frequently based on a small number of studies and on uncertain within-study standard deviation values. Despite the widespread use of Bayesian NNHM, it has always been unclear to what extent the posterior inference is impacted by the heterogeneity prior (sensitivity S ) and by the uncertainty in the within-study standard deviation values (identification I ). Thus, to answer this question, we developed a unified method to simultaneously quantify both sensitivity and identification ( S - I ) for all model parameters in a Bayesian NNHM, based on derivatives of the Bhattacharyya coefficient with respect t"],"journal":["Biometrical journal. Biometrische Zeitschrift"],"pubmed_title":["Sensitivity and identification quantification by a relative latent model complexity perturbation in Bayesian meta-analysis."],"pmcid":["PMC9292837"],"funding_grant_id":["175933"],"pubmed_authors":["Rue H","Roos M","Hunanyan S","Bakka H"],"additional_accession":[]},"is_claimable":false,"name":"Sensitivity and identification quantification by a relative latent model complexity perturbation in Bayesian meta-analysis.","description":"In recent years, Bayesian meta-analysis expressed by a normal-normal hierarchical model (NNHM) has been widely used for combining evidence from multiple studies. Data provided for the NNHM are frequently based on a small number of studies and on uncertain within-study standard deviation values. Despite the widespread use of Bayesian NNHM, it has always been unclear to what extent the posterior inference is impacted by the heterogeneity prior (sensitivity S ) and by the uncertainty in the within-study standard deviation values (identification I ). Thus, to answer this question, we developed a unified method to simultaneously quantify both sensitivity and identification ( S - I ) for all model parameters in a Bayesian NNHM, based on derivatives of the Bhattacharyya coefficient with respect t","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Dec","modification":"2025-04-25T21:51:01.259Z","creation":"2025-02-19T04:57:15.977Z"},"accession":"S-EPMC9292837","cross_references":{"pubmed":["34378223"],"doi":["10.1002/bimj.202000193"]}}