Vaccine-adverse event association analysis on the VAERS database

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Vaccine-adverse event association analysis on the VAERS database

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dc.contributor.advisor Powers, Daniel A.
dc.creator Ye, Na, 1983-
dc.date.accessioned 2011-08-05T18:47:36Z
dc.date.available 2011-08-05T18:47:36Z
dc.date.created 2011-05
dc.date.issued 2011-08-05
dc.date.submitted May 2011
dc.identifier.uri http://hdl.handle.net/2152/ETD-UT-2011-05-3293
dc.description.abstract The Vaccine Adverse Event Reporting System (VAERS) received thousands of reports of adverse events that occurred after vaccine administrations from the post-marketing vaccine safety surveillance. However, the causality between vaccines and reported adverse events cannot be taken for granted. In this report several data mining methods were applied to VAERS database that is coded in MedDRA terms to discover possible associations between vaccines and adverse events. Efforts were devoted to identify events that are reported more frequently after administering one vaccine than other vaccines using the following data mining techniques: relative ratio (RR), statistical significance (LogP), proportional reporting ratio (PRR), and screened PRR (SPRR). The vaccine-event combinations that ranked top in each method varied substantially among the methods. RR and PRR gave excessive weight to small counts of vaccine-event pairs, but SPRR was able to correct this weakness. There are only 33 vaccine-event pairs that were shared among the top 1,000 ranked in each method. Evaluating the properties of these data mining methods and exploring other methods will help improve vaccine safety surveillance.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subject Vaccine
dc.subject Adverse event
dc.subject Data mining
dc.subject Relative risk
dc.subject Proportional relative risk
dc.subject Screened proportional reporting ratio
dc.title Vaccine-adverse event association analysis on the VAERS database
dc.date.updated 2011-08-05T18:47:40Z
dc.identifier.slug 2152/ETD-UT-2011-05-3293
dc.contributor.committeeMember Saar-Tsechansky, Maytal
dc.description.department Statistics
dc.type.genre thesis
dc.type.material text
thesis.degree.department Statistics
thesis.degree.discipline Statistics
thesis.degree.grantor University of Texas at Austin
thesis.degree.level Masters
thesis.degree.name Master of Science in Statistics

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