Comparison of Signal Detection Algorithms Based on Frequency Statistical Model for Drug-Drug Interaction Using Spontaneous Reporting Systems

Purpose Adverse events (AEs) caused by polypharmacy have recently become a clinical problem, and it is important to monitor the safety profile of drug-drug interactions (DDIs). Mining signals using the spontaneous reporting systems is a very effective method for single drug-induced AE monitoring as...

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Vydané v:Pharmaceutical research Ročník 37; číslo 5; s. 86
Hlavní autori: Noguchi, Yoshihiro, Tachi, Tomoya, Teramachi, Hitomi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.05.2020
Springer
Springer Nature B.V
Predmet:
Algorithms
Analysis
Biochemistry
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Complications and side effects
Drug interaction
Drug interactions
Drugs
Mathematical models
Medical Law
Medical research
Medicine, Experimental
Pharmacology/Toxicology
Pharmacy
Research Paper
Statistical analysis
Stevens-Johnson syndrome
frequency statistical model
signal detection
data mining algorithms
drug-drug interaction
spontaneous reporting systems
ISSN:0724-8741, 1573-904X, 1573-904X
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Shrnutí:Purpose Adverse events (AEs) caused by polypharmacy have recently become a clinical problem, and it is important to monitor the safety profile of drug-drug interactions (DDIs). Mining signals using the spontaneous reporting systems is a very effective method for single drug-induced AE monitoring as well as early detection of DDIs. The objective of this study was to compare signal detection algorithms for DDIs based on frequency statistical models. Methods Five frequency statistical models: the Ω shrinkage measure , additive (risk difference), multiplicative (risk ratio), combination risk ratio , and chi-square statistics models were compared using the Japanese Adverse Drug Event Report (JADER) database as the spontaneous reporting system in Japan. The drugs targeted for the survey are all registered and classified as “suspect drugs” in JADER, and the AEs targeted for this study were the same as those in a previous study on Stevens-Johnson syndrome (SJS). Results Of 3924 pairs that reported SJS, the number of signals detected by the Ω shrinkage measure , additive , multiplicative , combination risk ratio , and chi-square statistics models was 712, 3298, 2252, 739, and 1289 pairs, respectively. Among the five models, the Ω shrinkage measure model showed the most conservative signal detection tendency. Conclusion Specifically, caution should be exercised when the number of reports is low because results differ depending on the statistical models. This study will contribute to the selection of appropriate statistical models to detect signals of potential DDIs.
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ISSN:0724-8741
1573-904X
1573-904X
DOI:10.1007/s11095-020-02801-3