{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:40:46Z","timestamp":1776681646183,"version":"3.51.2"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,13]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>We introduce an integrative ranking and thresholding framework for fusing evidence from multiple testing procedures. The key innovation is a method that transforms binary testing decisions into compound $ e $-values, enabling the combination of findings across diverse data sources or studies. We demonstrate that our new framework ensures overall false discovery rate control, provided that the individual studies maintain their respective false discovery rate levels. The proposed approach is highly flexible and offers a powerful method for fusing inferences in meta-analyses where some studies report summary statistics while the rest reveal only the rejections under a prespecified false discovery rate level. Extensions to alternative Type I error control measures are explored.<\/jats:p>","DOI":"10.1093\/biomet\/asaf080","type":"journal-article","created":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T20:35:32Z","timestamp":1762720532000},"source":"Crossref","is-referenced-by-count":2,"title":["Harnessing the collective wisdom: fusion learning using decision sequences from diverse sources"],"prefix":"10.1093","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7280-5559","authenticated-orcid":false,"given":"Trambak","family":"Banerjee","sequence":"first","affiliation":[{"name":"Analytics, Information and Operations, University of Kansas , 1654 Naismith Drive , Lawrence, Kansas 66045, . trambak@ku.edu","place":["U.S.A"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bowen","family":"Gang","sequence":"additional","affiliation":[{"name":"Statistics and Data Science, Fudan University , 220 Handan Road , Shanghai, 200437, \u00a0 bgang@fudan.edu.cn","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianliang","family":"He","sequence":"additional","affiliation":[{"name":"Statistics and Data Science, Yale University , 219 Prospect Street , New Haven, Connecticut 06511, . jianliang.he@yale.edu","place":["U.S.A"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,11,9]]},"reference":[{"key":"2026032607092392900_asaf080-B1","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1214\/15-AOS1337","article-title":"Controlling the false discovery rate via knockoffs","volume":"43","author":"Barber","year":"2015","journal-title":"Ann. 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