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An Evaluation of Computer‐Aided Disproportionality Analysis for Post‐Marketing Signal Detection
Author(s) -
Lehman H P,
Chen J,
Gould A L,
Kassekert R,
Beninger P R,
Carney R,
Goldberg M,
Goss M A,
Kidos K,
Sharrar R G,
Shields K,
Sweet A,
Wiholm B E,
Honig P K
Publication year - 2007
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/sj.clpt.6100233
Subject(s) - pharmacovigilance , bayes' theorem , detection theory , sensitivity (control systems) , statistics , computer science , signal (programming language) , predictive value , medicine , econometrics , data mining , artificial intelligence , mathematics , adverse effect , bayesian probability , engineering , telecommunications , electronic engineering , detector , programming language
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≥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. Clinical Pharmacology & Therapeutics (2007) 82 173–180. doi: 10.1038/sj.clpt.6100233 ; published online 16 May 2007