MDDC

MDDC implements a novel pattern discovery method for postmarketing adverse event identification in pharmacovigilance data.

Key Features:

• Pattern Discovery Method: Implements the Modified Detecting Deviating Cells algorithm for identifying adverse event signals in pharmacovigilance datasets.
• Data Generation Function: Generates data that considers adverse events as groups.
• Utility Functions: Provides additional utility functions for analysis.
• Multi-language Implementation: Available as both an R and Python package.

Scientific Applications:

• Pharmacovigilance Signal Detection: Analyzes product-adverse event pairs from spontaneous reporting systems such as FAERS, EudraVigilance, and VigiBase for potential safety signals.
• Postmarketing Surveillance: Supports postmarketing surveillance of medical products using real-world adverse event reporting data.

Methodology:

The Modified Detecting Deviating Cells algorithm identifies deviating patterns in pharmacovigilance data for adverse event detection.