{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:55:06Z","timestamp":1786978506418,"version":"build-2736575974"},"reference-count":47,"publisher":"Wiley","issue":"8-9","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Spontaneous reporting system databases are key resources for post\u2010marketing surveillance, providing real\u2010world evidence (RWE) on the adverse events (AEs) of regulated drugs or other medical products. Various statistical methods have been proposed for AE signal detection in these databases, flagging drug\u2010specific AEs with disproportionately high observed counts compared to expected counts under independence. However, signal detection remains challenging for rare AEs or newer drugs, which receive small observed and expected counts and thus suffer from reduced statistical power. Principled information sharing on signal strengths across drugs\/AEs is crucial in such cases to enhance signal detection. However, existing methods typically ignore complex between\u2010drug associations on AE signal strengths, limiting their ability to detect signals. We propose novel local\u2013global mixture Dirichlet process (DP) prior\u2010based nonparametric Bayesian models to capture these associations, enabling principled information sharing between drugs while balancing flexibility and shrinkage for each drug, thereby enhancing statistical power. We develop efficient Markov chain Monte Carlo algorithms for implementation and employ a false discovery rate (FDR)\u2010controlled, false negative rate (FNR)\u2010optimized hypothesis testing framework for AE signal detection. Extensive simulations demonstrate our methods' superior sensitivity\u2014often surpassing existing approaches by a twofold or greater margin\u2014while strictly controlling the FDR. An application to FDA FAERS data on statin drugs further highlights our methods' effectiveness in real\u2010world AE signal detection. Software implementing our methods is provided as supplementary material.<\/jats:p>","DOI":"10.1002\/sim.70523","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:01:56Z","timestamp":1775559716000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Nonparametric Bayesian Local\u2013Global Model for Enhanced Adverse Event Signal Detection in Spontaneous Reporting System Data"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4238-3081","authenticated-orcid":false,"given":"Xin\u2010Wei","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Biostatistics State University of New York at Buffalo  Buffalo New York 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