{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T09:52:18Z","timestamp":1781517138619,"version":"3.54.1"},"reference-count":41,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["INFORMS Journal on Computing"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>Most drugs are associated with some form of adverse drug reactions (ADRs). Understanding the connection between drugs and ADRs is crucial for minimizing patient harm and reducing public healthcare costs. Consequently, there has been sustained interest in correlation analysis within pharmacovigilance and drug development. In the postmarketing phase, the estimated correlation between drugs and their ADRs is affected by both the correlation degree and variability. Therefore, accounting for variability is particularly important when measuring correlations, particularly in the early stage with fewer data points, where variability is typically higher. In this study, we introduce a framework called error-controlled correlation (ECC), which provides correlation estimates while dynamically adjusting for variability. ECC offers a versatile framework that is applicable to any correlation measure. Using the five most widely used correlation measures, we demonstrate ECC\u2019s efficacy in identifying highly correlated drug-ADR pairs while maintaining a controlled type 1 error rate. Experimental results on both real-world and simulated data show that ECC consistently outperforms benchmark methods. Notably, it achieves comparable performance to existing methods with only 1\/10th of the data, enabling significantly earlier ADR detection.<\/jats:p>\n                  <jats:p>History: Accepted by J. Paul Brooks, Area Editor for Applications in Biology, Medicine, &amp; Healthcare.<\/jats:p>\n                  <jats:p>Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https:\/\/pubsonline.informs.org\/doi\/suppl\/10.1287\/ijoc.2024.0585 ) as well as from the IJOC GitHub software repository ( https:\/\/github.com\/INFORMSJoC\/2024.0585 ). The complete IJOC Software and Data Repository is available at https:\/\/informsjoc.github.io\/ .<\/jats:p>","DOI":"10.1287\/ijoc.2024.0585","type":"journal-article","created":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T16:28:12Z","timestamp":1752078492000},"page":"862-877","source":"Crossref","is-referenced-by-count":0,"title":["Early Detection of Adverse Drug Reactions in Postmarket Monitoring"],"prefix":"10.1287","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0618-8628","authenticated-orcid":false,"given":"Lian","family":"Duan","sequence":"first","affiliation":[{"name":"Department of Information Systems and Business Analytics, Hofstra University, Hempstead, New York 11549"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2346-8151","authenticated-orcid":false,"given":"Wenjun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Business Analytics and Statistics Department, University of Tennessee, Knoxville, Knoxville, Tennessee 37996"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3837-1750","authenticated-orcid":false,"given":"Yong","family":"Hu","sequence":"additional","affiliation":[{"name":"Big Data Decision Institute, Jinan University, Guangzhou 510632, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3263-5217","authenticated-orcid":false,"given":"Lida","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk,  Virginia 23529"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8036-2110","authenticated-orcid":false,"given":"Mei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida 32611"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"109","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1007\/s002280050466"},{"key":"B3","doi-asserted-by":"crossref","unstructured":"Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. Peckman JM, Ram S, Franklin M, eds.\n                      Proc. 1997 ACM SIGMOD Internat. Conf. Management Data\n                      (Association for Computing Machinery, New York), 255\u2013264.","DOI":"10.1145\/253260.253325"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.3198"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2012.2227272"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1145\/2637484"},{"key":"B8","doi-asserted-by":"crossref","unstructured":"Duan L, Zhou W, Hu Y, Xu L, Liu M (2025) Early detection of adverse drug reactions in post-market monitoring. https:\/\/doi.org\/10.1287\/ijoc.2024.0585.cd, https:\/\/github.com\/INFORMSJoC\/2024.0585.","DOI":"10.1287\/ijoc.2024.0585.cd"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.intimp.2019.105866"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1145\/1132960.1132963"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1038\/clpt.2013.24"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1007\/s40264-022-01186-z"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2006.02.003"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1007\/s00228-002-0501-2"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1159\/000339789"},{"key":"B20","unstructured":"Kleinberg S (2013) Causal inference with rare events in large-scale time-series data.\n                      IJCAI\u201913 Proc. 23rd Internat. Joint Conf. Artificial Intelligence\n                      (AAAI Press, Washington, DC), 1444\u20131450."},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-019-0999-1"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2012-001119"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1038\/nature11159"},{"key":"B24","doi-asserted-by":"crossref","unstructured":"Merck C, Kleinberg S (2016) Causal explanation under indeterminism: A sampling approach.\n                      Proc. 30th AAAI Conf. Artificial Intelligence\n                      (AAAI Press, Washington, DC), 1037\u20131043.","DOI":"10.1609\/aaai.v30i1.10088"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.296.6632.1313"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1968.11009219"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.2196\/11264"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1177\/0962280211403604"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3247"},{"key":"B30","volume-title":"Knowledge Discovery in Databases","author":"Piatestky G","year":"1991"},{"key":"B32","unstructured":"Romano Y, Patterson E, Candes E (2019) Conformalized quantile regression.\n                      Proc. 33rd Internat. Conf. Neural Inform. Processing Systems\n                      (Curran Associates, Inc. Red Hook, NY), 3543\u20133553."},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1007\/s40264-020-00957-w"},{"key":"B34","unstructured":"Sarnak DO, Squires D, Kuzmak G, Bishop S (2017) Paying for prescription drugs around the world: Why is the US an outlier? Commonwealth Fund Issue Brief (October), 1\u201314."},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1177\/0962280214527531"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1007\/s40264-015-0388-3"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1080\/14740338.2023.2223949"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1002\/pds.5624"},{"key":"B39","unstructured":"Waitman LR, Warren JJ, Manos EL, Connolly DW (2011) Expressing observations from electronic medical record flowsheets in an i2b2 based clinical data repository to support research and quality improvement.\n                      AMIA Annual Sympos. Proc.\n                      , vol. 2011 (American Medical Informatics Association, Bethesda, MD), 1454\u20131463."},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.2196\/11016"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3029446"},{"key":"B42","unstructured":"World Health Organization (1969) International drug monitoring: The role of the hospital: Report of a WHO meeting (World Health Organization, Geneva)."},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.1599388"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.1080.0265"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1007\/s40264-022-01155-6"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2003.1209011"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2791602"}],"container-title":["INFORMS Journal on Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubsonline.informs.org\/doi\/pdf\/10.1287\/ijoc.2024.0585","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T08:55:27Z","timestamp":1781513727000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubsonline.informs.org\/doi\/10.1287\/ijoc.2024.0585"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1287\/ijoc.2024.0585"],"URL":"https:\/\/doi.org\/10.1287\/ijoc.2024.0585","relation":{},"ISSN":["1091-9856","1526-5528"],"issn-type":[{"value":"1091-9856","type":"print"},{"value":"1526-5528","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]}}}