{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:00:53Z","timestamp":1792173653957,"version":"4.4.0"},"reference-count":37,"publisher":"Wiley","issue":"19","license":[{"start":{"date-parts":[[2015,4,29]],"date-time":"2015-04-29T00:00:00Z","timestamp":1430265600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2015,9,1]],"date-time":"2015-09-01T00:00:00Z","timestamp":1441065600000},"content-version":"tdm","delay-in-days":125,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2015,8,30]]},"abstract":"<jats:p>In the recent two decades, data mining methods for signal detection have been developed for drug safety surveillance, using large post\u2010market safety data. Several of these methods assume that the number of reports for each drug\u2013adverse event combination is a Poisson random variable with mean proportional to the unknown reporting rate of the drug\u2013adverse event pair. Here, a Bayesian method based on the Poisson\u2013Dirichlet process (DP) model is proposed for signal detection from large databases, such as the Food and Drug Administration's Adverse Event Reporting System (AERS) database. Instead of using a parametric distribution as a common prior for the reporting rates, as is the case with existing Bayesian or empirical Bayesian methods, a nonparametric prior, namely, the DP, is used. The precision parameter and the baseline distribution of the DP, which characterize the process, are modeled hierarchically. The performance of the Poisson\u2013DP model is compared with some other models, through an intensive simulation study using a Bayesian model selection and frequentist performance characteristics such as type\u2010I error, false discovery rate, sensitivity, and power. For illustration, the proposed model and its extension to address a large amount of zero counts are used to analyze statin drugs for signals using the 2006\u20132011 AERS data. Copyright \u00a9 2015\u2009John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/sim.6510","type":"journal-article","created":{"date-parts":[[2015,4,30]],"date-time":"2015-04-30T04:33:06Z","timestamp":1430368386000},"page":"2725-2742","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Signal detection in FDA AERS database using Dirichlet process"],"prefix":"10.1002","volume":"34","author":[{"given":"Na","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Statistics University of Missouri  MO 65201 Columbia U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Huang","sequence":"additional","affiliation":[{"name":"Office of Biostatistics, CDER FDA  Silver Spring 20993 MD U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram C.","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Office of Biostatistics, CDER FDA  Silver Spring 20993 MD U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2015,4,29]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.677"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.1001"},{"key":"e_1_2_11_4_1","first-page":"30","volume-title":"An Introduction to the Theory of Statistics","author":"Yule GU","year":"1957"},{"key":"e_1_2_11_5_1","volume-title":"A Guide to Chi\u2010Squared Testing","author":"Greenwood PE","year":"1996"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/0895-4356(92)90088-5"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2011.ap10243"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6103"},{"issue":"4","key":"e_1_2_11_9_1","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s002280050466","article-title":"A Bayesian neural network method for adverse drug reaction signal generation","volume":"54","author":"Bate A","journal-title":"European Journal of Clinical Pharmacology"},{"issue":"21","key":"e_1_2_11_10_1","doi-asserted-by":"crossref","first-page":"3740","DOI":"10.1002\/sim.2473","article-title":"Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events","volume":"25","author":"Noren GN","journal-title":"Statistics in Medicine"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.2307\/2686093"},{"key":"e_1_2_11_12_1","doi-asserted-by":"crossref","unstructured":"DuMouchelW PregibonD.Empirical Bayes screening for multi\u2010item associations. 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