title: An evaluation of computer-aided disproportionality analysis for post-marketing signal detection.
abstract: To understand the value of computer-aided disproportionality analysis (DA) in relation to current pharmacovigilance signal detection methods, four products were retrospectively evaluated by applying an empirical Bayes method to Merck's post-marketing safety database. Findings were compared with the prior detection of labeled post-marketing adverse events. Disproportionality ratios (empirical Bayes geometric mean lower 95% bounds for the posterior distribution (EBGM05)) were generated for product-event pairs. Overall (1993-2004 data, EBGM05> or =2, individual terms) results of signal detection using DA compared to standard methods were sensitivity, 31.1%; specificity, 95.3%; and positive predictive value, 19.9%. Using groupings of synonymous labeled terms, sensitivity improved (40.9%). More of the adverse events detected by both methods were detected earlier using DA and grouped (versus individual) terms. With 1939-2004 data, diagnostic properties were similar to those from 1993 to 2004. DA methods using Merck's safety database demonstrate sufficient sensitivity and specificity to be considered for use as an adjunct to conventional signal detection methods.
authors.affiliation: Merck & Co. Inc., West Point, PA, USA. Heidi_Lehman@Merck.com
authors.forename: H P
authors.initials: HP
authors.lastname: Lehman
authors.forename: J
authors.initials: J
authors.lastname: Chen
authors.forename: A L
authors.initials: AL
authors.lastname: Gould
authors.forename: R
authors.initials: R
authors.lastname: Kassekert
authors.forename: P R
authors.initials: PR
authors.lastname: Beninger
authors.forename: R
authors.initials: R
authors.lastname: Carney
authors.forename: M
authors.initials: M
authors.lastname: Goldberg
authors.forename: M A
authors.initials: MA
authors.lastname: Goss
authors.forename: K
authors.initials: K
authors.lastname: Kidos
authors.forename: R G
authors.initials: RG
authors.lastname: Sharrar
authors.forename: K
authors.initials: K
authors.lastname: Shields
authors.forename: A
authors.initials: A
authors.lastname: Sweet
authors.forename: B E
authors.initials: BE
authors.lastname: Wiholm
authors.forename: P K
authors.initials: PK
authors.lastname: Honig
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data; D017076:Computer-Aided Design / Q000592:standards*; D016208:Databases, Factual / Q000706:statistics & numerical data; D004345:Drug Industry / Q000379:methods / Q000706:statistics & numerical data / Q000639:trends; D064420:Drug-Related Side Effects and Adverse Reactions / Q000139:chemically induced / Q000517:prevention & control; D011358:Product Surveillance, Postmarketing / Q000379:methods* / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012189:Retrospective Studies; D013997:Time Factors; D014612:Vaccines / Q000009:adverse effects
chemical_list: D014612:Vaccines