Model driven method for exploring individual and confounding effects in spontaneous adverse event reporting databases

ABSTRACT

Background

Spontaneous Adverse Event Reporting (SAER) databases play a crucial role in post-marketing drug surveillance. However, the traditional model-free disproportionality analysis has been challenged by the insufficiency in investigating subgroup and confounders. These issues result in significant low-precision and biases in data mining for SAER.

Methods

The Model-Driven Reporting Odds Ratio (MD-ROR) was proposed to bridge the gap between SAER database and explainable models for exploring individual and confounding effects. MD-ROR is grounded in a well-designed model, rather than a 2 × 2cross table, for estimating AE-drug signals. Consequently, individual and confounding effects can be parameterized based on these models. We employed simulation data and the FDA Adverse Event Reporting System (FAERS) database.

Result

The simulated data indicated the subgroup effects estimated by MD-ROR were unbiased and efficient. Moreover, the adjusted-MD-ROR demonstrated greater robustness against confounding biases than the crude ROR. Applying our method to the FAERS database suggested higher occurrences of drug interactions and cardiac adverse events induced by Midazolam in females compared to males.

Conclusion

The study underscored that MD-ROR holds promise as a method for investigating individual and confounding effects in SAER databases.

KEYWORDS:

• FAERS (FDA adverse events Reporting system)
• Model driven
• individual effects
• confounding effects

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Declaration of Interests

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosure

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgements

Authors thank Prof. Yueping Shen and Dr. Na Sun of the Soochow University for helpful comments about this paper.

Author Contributions

Bo Lv: Conceptualization, Methodology, Software, Data analysis, Visualization, Writing - Original Draft; Yuedong Li: Conceptualization, Validation Data Curation and Writing - Review & Editing; Aiming Shi: Supervision, Project administration, Funding acquisition; Jie Pan: Supervision, Project administration, Funding acquisition.

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Additional information

Funding

This manuscript was funded by Pre-research fund of The Second Affiliated Hospital of Soochow University [No. SDFEYBS2211] and Beijing Great Physician Commonweal Foundation [No. YWJKJJHKYJJ-TYU066D].