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

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Abstract

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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Abbreviations

AE:

adverse events

CI:

Confidence interval

csv:

comma-separated values

DDI:

drug-drug interaction

FAERS:

Food and Drug Administration Adverse Events Reporting System

IC:

information component

JADER:

Japanese Adverse Drug Event Report database

MedDRA /J:

Medical Dictionary for Regulatory Activities Japanese version

MGPS:

multi gamma–Poisson shrinker

PT:

preferred term

PRR:

proportional reporting ratio

RGPS:

regression-adjusted gamma–Poisson shrinkage

SJS:

Stevens-Johnson syndrome,

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Acknowledgments and Disclosures

Yoshihiro Noguchi, Tomoya Tachi and Hitomi Teramachi have no conflicts of interest that are directly relevant to the content of this study.

Funding

This study was supported by JSPS KAKENHI Grant Number 19 K20731.

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Authors and Affiliations

  1. Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigakunishi, Gifu, 501-1196, Japan

    Yoshihiro Noguchi, Tomoya Tachi & Hitomi Teramachi

  2. Laboratory of Community Healthcare Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigakunishi, Gifu, 501-1196, Japan

    Hitomi Teramachi

Authors
  1. Yoshihiro Noguchi
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  2. Tomoya Tachi
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  3. Hitomi Teramachi
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Correspondence to Yoshihiro Noguchi or Hitomi Teramachi.

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Noguchi, Y., Tachi, T. & Teramachi, H. Comparison of Signal Detection Algorithms Based on Frequency Statistical Model for Drug-Drug Interaction Using Spontaneous Reporting Systems. Pharm Res 37, 86 (2020). https://doi.org/10.1007/s11095-020-02801-3

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  • DOI: https://doi.org/10.1007/s11095-020-02801-3

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