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dc.contributor.authorBrown, Jeffrey S.en_US
dc.contributor.authorPetronis, Kenneth R.en_US
dc.contributor.authorBate, Andrewen_US
dc.contributor.authorZhang, Fangen_US
dc.contributor.authorDashevsky, Innaen_US
dc.contributor.authorKulldorff, Martinen_US
dc.contributor.authorAvery, Taliser R.en_US
dc.contributor.authorDavis, Robert L.en_US
dc.contributor.authorChan, K. Arnolden_US
dc.contributor.authorAndrade, Susan E.en_US
dc.contributor.authorBoudreau, Deniseen_US
dc.contributor.authorGunter, Margaret J.en_US
dc.contributor.authorHerrinton, Lisaen_US
dc.contributor.authorPawloski, Pamala A.en_US
dc.contributor.authorRaebel, Marsha A.en_US
dc.contributor.authorRoblin, Douglasen_US
dc.contributor.authorSmith, Daviden_US
dc.contributor.authorReynolds, Roberten_US
dc.date.accessioned2014-03-11T02:49:22Z
dc.date.issued2013en_US
dc.identifier.citationBrown, J. S., K. R. Petronis, A. Bate, F. Zhang, I. Dashevsky, M. Kulldorff, T. R. Avery, et al. 2013. “Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic.” Pharmaceutics 5 (1): 179-200. doi:10.3390/pharmaceutics5010179. http://dx.doi.org/10.3390/pharmaceutics5010179.en
dc.identifier.issn1999-4923en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11879121
dc.description.abstractBackground: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method—the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds.en
dc.language.isoen_USen
dc.publisherMDPIen
dc.relation.isversionofdoi:10.3390/pharmaceutics5010179en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3834945/pdf/en
dash.licenseLAAen_US
dc.subjectpharmacovigilanceen
dc.subjectdrug safety surveillanceen
dc.subjectadverse events data miningen
dc.subjectgamma Poisson shrinkageen
dc.subjecttree-based scan statisticen
dc.titleDrug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statisticen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalPharmaceuticsen
dash.depositing.authorBrown, Jeffrey S.en_US
dc.date.available2014-03-11T02:49:22Z
dc.identifier.doi10.3390/pharmaceutics5010179*
dash.authorsorderedfalse
dash.contributor.affiliatedBrown, Jeffrey
dash.contributor.affiliatedAvery, Taliser
dash.contributor.affiliatedDashevsky, Inna
dash.contributor.affiliatedKulldorff, Martin
dash.contributor.affiliatedZhang, Fang
dc.identifier.orcid0000-0002-5284-2993


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