Learn More
PURPOSE To compare the results of drug safety data mining with three different algorithms, when adverse events are identified using MedDRA Preferred Terms (PT) vs. High Level Terms (HLT) vs. Standardised MedDRA Queries (SMQ). METHODS For a representative set of 26 drugs, data from the FDA Adverse Event Reporting System (AERS) database from 2001 through(More)
BACKGROUND Computer-assisted data mining algorithms (DMAs) are being studied to screen spontaneous reporting databases for signals of novel adverse events. The performance characteristics and optimum deployment of these techniques remain to be established. OBJECTIVE To explore issues in the practical evaluation and deployment of DMAs by comparing findings(More)
A principle concern of pharmacovigilance is the timely detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of this process is the scientific acumen of the pharmacovigilance domain expert. There is understandably an interest in developing database screening tools to assist human(More)
In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine(More)
PURPOSE To provide commentary and points of caution to consider before incorporating data mining as a routine component of any Pharmacovigilance program, and to stimulate further research aimed at better defining the predictive value of these new tools as well as their incremental value as an adjunct to traditional methods of post-marketing surveillance. (More)
BACKGROUND Statistical techniques have traditionally been underused in spontaneous reporting systems used for postmarketing surveillance of adverse drug events. Regulatory agencies, pharmaceutical companies, and drug monitoring centers have recently devoted considerable efforts to develop and implement computer-assisted automated signal detection(More)
Several data mining algorithms (DMAs) are being studied in hopes of enhancing screening of large post-marketing safety databases for signals of novel adverse events (AEs). The objective of this study was to apply two DMAs to the United States FDA Adverse Event Reporting System (AERS) database to see whether signals of potentially fatal AEs with cancer drugs(More)
Pharmacovigilance serves to detect previously unrecognised adverse events associated with the use of medicines. The simplest method for detecting signals of such events is crude inspection of lists of spontaneously reported drug-event combinations. Quantitative and automated numerator-based methods such as Bayesian data mining can supplement or supplant(More)
BACKGROUND Pharmacovigilance data-mining algorithms (DMAs) are known to generate significant numbers of false-positive signals of disproportionate reporting (SDRs), using various standards to define the terms 'true positive' and 'false positive'. OBJECTIVE To construct a highly inclusive reference event database of reported adverse events for a limited(More)
Drug-induced immune thrombocytopenia (DITP) is often suspected in patients with acute thrombocytopenia unexplained by other causes, but documenting that a drug is the cause of thrombocytopenia can be challenging. To provide a resource for diagnosis of DITP and for drug safety surveillance, we analyzed 3 distinct methods for identifying drugs that may cause(More)