{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:47:46Z","timestamp":1792601266336,"version":"4.5.0"},"reference-count":63,"publisher":"Wiley","issue":"23-24","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"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":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>We present a Bayesian dynamic borrowing approach to enhance quantitative identification of adverse events in spontaneous reporting systems. The method embeds a robust meta\u2010analytic predictive prior and incorporates semantic similarity measures (SSMs) to enable weighted information sharing from clinically similar MedDRA preferred terms (PTs) to the target PT. This continuous similarity\u2010based borrowing overcomes limitations of rigid hierarchical grouping in disproportionality analysis (DPA). Using data from the FDA Adverse Event Reporting System and a time\u2010stamped reference set, we evaluate our approach\u2014termed IC SSM\u2014against the current standard approach of \u201ctraditional\u201d information component (IC) analysis and borrowing at the MedDRA HLGT level (IC HLGT). The primary IC SSM approach at the standard threshold (2.5% posterior quantile ) detected true signals over five months earlier than traditional IC. It showed higher sensitivity (57.0%) but lower precision (14.5%) than traditional IC (50.1%, 16.1%), while outperforming IC HLGT (55.7%, 13.9%). In subsequent analyses, even when tightening the alerting quantile of IC SSM to 1% to improve precision (16.4%), superior sensitivity (51.4%) was retained compared to traditional IC. Similarly, relaxing traditional IC's quantile to 5% yielded only 55.6% sensitivity with 14.2% precision, failing to match IC SSM's performance or timeliness and confirming its inherent advantage. IC SSM's superior timeliness and sensitivity stem from integrating clinically meaningful information, making it a scalable and context\u2010aware enhancement to traditional DPA. The method has potential for exploratory analysis in sparse datasets, and refinements to the borrowing strategies and similarity metrics could yield further performance improvements.<\/jats:p>","DOI":"10.1002\/sim.70757","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T05:46:58Z","timestamp":1792129618000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantic Similarity\u2010Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4989-3738","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"Haguinet","sequence":"first","affiliation":[{"name":"GSK  Wavre Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9651-9904","authenticated-orcid":false,"given":"Jeffery 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