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

There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.

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Drug-Drug Interaction Signal Detection from Drug Safety Reports

2018 IEEE MIT Undergraduate Research Technology Conference (URTC) ◽

10.1109/urtc45901.2018.9244814 ◽

2018 ◽

Author(s):

Brian Zylich ◽

Brian McCarthy ◽

Andrew Schade ◽

Huy Tran ◽

Xiao Qin ◽

...

Keyword(s):

Drug Interaction ◽

Signal Detection ◽

Drug Safety ◽

Drug Drug Interaction

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A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions

Pharmacoepidemiology and Drug Safety ◽

10.1002/pds.668 ◽

2002 ◽

Vol 11 (1) ◽

pp. 3-10 ◽

Cited By ~ 427

Author(s):

Eug�ne P. van Puijenbroek ◽

Andrew Bate ◽

Hubert G. M. Leufkens ◽

Marie Lindquist ◽

Roland Orre ◽

...

Keyword(s):

Signal Detection ◽

Adverse Drug Reactions ◽

Spontaneous Reporting ◽

Drug Reactions ◽

Reporting Systems

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Exploration of the Association Rules Mining Technique for the Signal Detection of Adverse Drug Events in Spontaneous Reporting Systems

PLoS ONE ◽

10.1371/journal.pone.0040561 ◽

2012 ◽

Vol 7 (7) ◽

pp. e40561 ◽

Cited By ~ 18

Author(s):

Chao Wang ◽

Xiao-Jing Guo ◽

Jin-Fang Xu ◽

Cheng Wu ◽

Ya-Lin Sun ◽

...

Keyword(s):

Signal Detection ◽

Association Rules ◽

Spontaneous Reporting ◽

Adverse Drug Events ◽

Association Rules Mining ◽

Mining Technique ◽

Reporting Systems

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Simulating adverse event spontaneous reporting systems as preferential attachment networks

Applied Clinical Informatics ◽

10.4338/aci-2013-11-ra-0097 ◽

2014 ◽

Vol 05 (01) ◽

pp. 206-218 ◽

Cited By ~ 8

Author(s):

T. Botsis ◽

R. Ball ◽

J. Scott

Keyword(s):

Data Mining ◽

Adverse Event ◽

Signal Detection ◽

Spontaneous Reporting ◽

Reporting System ◽

Preferential Attachment ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Reporting Systems ◽

Simulation Strategy

SummaryBackground: Spontaneous Reporting Systems [SRS] are critical tools in the post-licensure evaluation of medical product safety. Regulatory authorities use a variety of data mining techniques to detect potential safety signals in SRS databases. Assessing the performance of such signal detection procedures requires simulated SRS databases, but simulation strategies proposed to date each have limitations.Objective: We sought to develop a novel SRS simulation strategy based on plausible mechanisms for the growth of databases over time.Methods: We developed a simulation strategy based on the network principle of preferential attachment. We demonstrated how this strategy can be used to create simulations based on specific databases of interest, and provided an example of using such simulations to compare signal detection thresholds for a popular data mining algorithm.Results: The preferential attachment simulations were generally structurally similar to our targeted SRS database, although they had fewer nodes of very high degree. The approach was able to generate signal-free SRS simulations, as well as mimicking specific known true signals. Explorations of different reporting thresholds for the FDA Vaccine Adverse Event Reporting System suggested that using proportional reporting ratio [PRR] > 3.0 may yield better signal detection operating characteristics than the more commonly used PRR > 2.0 threshold.Discussion: The network analytic approach to SRS simulation based on the principle of preferential attachment provides an attractive framework for exploring the performance of safety signal detection algorithms. This approach is potentially more principled and versatile than existing simulation approaches.Conclusion: The utility of network-based SRS simulations needs to be further explored by evaluating other types of simulated signals with a broader range of data mining approaches, and comparing network-based simulations with other simulation strategies where applicable.Citation: Scott J, Botsis T, Ball R. Simulating adverse event spontaneous reporting systems as preferential attachment networks: Application to the Vaccine Adverse Event Reporting System. Appl Clin Inf 2014; 5: 206–218 http://dx.doi.org/10.4338/ACI-2013-11-RA-0097

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