Abstract
Concomitant use of multiple drugs for therapeutic purposes is known as “polypharmacy situations,” which has been recognized as an important social problem recently. In polypharmacy situations, each drug not only induces adverse events (AEs) but also increases the risk of AEs due to drug–drug interactions (DDIs). The proportion of AEs caused by DDIs is estimated to be around 30% of unexpected AEs. The randomized clinical trials in pre-marketing typically focus emphasis on the verification of single drug safety and efficacy rather than the surveys of DDI, and therefore, patients on multiple drugs are usually excluded. However, unlike pre-marketing randomized clinical trials, in clinical practice (= post marketing), many patients use multiple drugs. The spontaneous reporting system is one of the significant sources drug safety surveillance in post-marketing. Commonly, signals of potential drug-induced AEs detected from this source are validated in real-world settings. Recently, not only methodological studies on signal detection of “single” drug, but also on several methodological studies on signal detection of DDIs have been conducted. On the other hand, there are few articles that systematically summarize the statistical methodology for signal detection of DDIs. Therefore, this article reviews the studies on the latest statistical methodologies from classical methodologies for signal detection of DDIs using spontaneous reporting system. This article describes how to calculate for each detection method and the major findings from the published literatures about DDIs. Finally, this article presented several limitations related to the currently used methodologies for signal detection of DDIs and suggestions for further studies.
Highlights
For safety surveillance of a drug, several data-mining algorithms are used to detect quantitative signals from spontaneous reporting systems
The randomized clinical trials in pre-marketing typically focus emphasis on the verification of single drug safety and efficacy rather than the surveys of drug–drug interactions (DDIs), and patients on multiple drugs are usually excluded from the clinical trial
The reporting odds ratio (ROR) is a statistical model similar to odds ratio, and using the logistic regression model shown in Eq 1, the ROR adjusted for age, gender, and concomitant drugs is used as the adjusted ROR
Summary
For safety surveillance of a drug, several data-mining algorithms are used to detect quantitative signals from spontaneous reporting systems. The recent extension of the IC and the GPS can accommodate signals of high-order interactions (Almenoff et al, 2003; Yang and Fram, 2004; Norén et al, 2006; DuMouchel and Harpaz, 2012), generally, the PRR and the ROR are exploited for early signal detection of unknown “single” drug-induced adverse events (AEs). These detection models might detect potential drug-induced AEs that could not be found clinical trials of premarketing using spontaneous reporting systems including postmarketing data. We review studies on the statistical methodologies for signal detection of DDIs using spontaneous reporting systems
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