MDDC: An R and Python Package for Adverse Event Identification in Pharmacovigilance Data

TL;DR

This paper introduces MDDC, an R and Python package that employs a novel pattern discovery method for identifying adverse events in pharmacovigilance data, enhancing postmarketing surveillance capabilities.

Contribution

The paper presents a new package implementing the MDDC method, including data generation and utility functions, with real dataset analysis from FAERS.

Findings

Effective identification of adverse event signals in pharmacovigilance data

Integration of pattern discovery approach improves signal detection

Package availability facilitates broader adoption in safety monitoring

Abstract

The safety of medical products continues to be a significant health concern worldwide. Spontaneous reporting systems (SRS) and pharmacovigilance databases are essential tools for postmarketing surveillance of medical products. Various SRS are employed globally, such as the Food and Drug Administration Adverse Event Reporting System (FAERS), EudraVigilance, and VigiBase. In the pharmacovigilance literature, numerous methods have been proposed to assess product - adverse event pairs for potential signals. In this paper, we introduce an R and Python package that implements a novel pattern discovery method for postmarketing adverse event identification, named Modified Detecting Deviating Cells (MDDC). The package also includes a data generation function that considers adverse events as groups, as well as additional utility functions. We illustrate the usage of the package through the analysis of real datasets derived from the FAERS database.