Johnson K, Guo C, Gosink M, Wang V, Hauben M. Multinomial modeling and an evaluation of common data-mining algorithms for identifying signals of disproportionate reporting in pharmacovigilance databases.
Bioinformatics 2012;
28:3123-30. [PMID:
23064001 DOI:
10.1093/bioinformatics/bts576]
[Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION
A principal objective of pharmacovigilance is to detect adverse drug reactions that are unknown or novel in terms of their clinical severity or frequency. One method is through inspection of spontaneous reporting system databases, which consist of millions of reports of patients experiencing adverse effects while taking one or more drugs. For such large databases, there is an increasing need for quantitative and automated screening tools to assist drug safety professionals in identifying drug-event combinations (DECs) worthy of further investigation. Existing algorithms can effectively identify problematic DECs when the frequencies are high. However these algorithms perform differently for low-frequency DECs.
RESULTS
In this work, we provide a method based on the multinomial distribution that identifies signals of disproportionate reporting, especially for low-frequency combinations. In addition, we comprehensively compare the performance of commonly used algorithms with the new approach. Simulation results demonstrate the advantages of the proposed method, and analysis of the Adverse Event Reporting System data shows that the proposed method can help detect interesting signals. Furthermore, we suggest that these methods be used to identify DECs that occur significantly less frequently than expected, thus identifying potential alternative indications for these drugs. We provide an empirical example that demonstrates the importance of exploring underexpected DECs.
AVAILABILITY
Code to implement the proposed method is available in R on request from the corresponding authors.
CONTACT
kjell@arboranalytics.com or Mark.M.Gosink@Pfizer.com
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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