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The role of data mining in pharmacovigilance (Expert Opin Drug Saf. 2005;4(5):929-48) explains how signal detection algorithms work and addresses questions regarding their validation, comparative performance, limitations and potential for use and misuse in pharmacovigilance.
An empirical evaluation of several disproportionality methods in a number of different spontaneous reporting databases is given in Comparison of statistical detection methods within and across spontaneous reporting databases (Drug Saf. 2015;38(6);577-87).
Performance of pharmacovigilance signal detection algorithms for the FDA adverse event reporting system (Clin Pharmacol Ther. 2013;93(6):539-46) describes the performance of signal-detection algorithms for spontaneous reports in the US FDA adverse event reporting system against a benchmark constructed by the Observational Health Data Sciences and Informatics community. It concludes that logistic regression performs better than traditional disproportionality analysis. Other studies have addressed similar or related questions, for examples Large-scale regression-based pattern discovery: The example of screening the WHO global Drug Safety database (Stat Anal. Data Min. 2010;3(4):197–208), Are all quantitative postmarketing signal detection methods equal? Performance characteristics of logistic regression and Multi-item Gamma Poisson Shrinker (Pharmacoepidemiol Drug Saf. 2012; 21(6):622–30 and Data-driven prediction of drug effects and interactions (Sci Transl Med. 2012;4(125):125ra31).
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