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As premarketing clinical trials usually have stringent inclusion\/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score\u2010adjusted three\u2010component mixture model (PS\u20103CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug\u2010drug\u2010ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS\u20103CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS\u20103CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS\u20103CMM prioritizes DDI signals differently. PS\u20103CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS\u20103CMM is a new method that is complement to the existing DDI signal detection methods.<\/jats:p>","DOI":"10.1002\/sim.8457","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T13:51:48Z","timestamp":1577454708000},"page":"996-1010","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Propensity score\u2010adjusted three\u2010component mixture model for drug\u2010drug interaction data mining in FDA Adverse Event Reporting System"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6350-7998","authenticated-orcid":false,"given":"Xueying","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics College of Medicine, The Ohio State University  Columbus Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixing","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3592-9042","authenticated-orcid":false,"given":"Pengyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics College of Medicine, The Ohio State University  Columbus Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,12,27]]},"reference":[{"key":"e_1_2_8_2_1","first-page":"1972","article-title":"Drug interactions among commonly used medications. 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