Three-Component Mixture Model-Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System

Pengyue Zhang, Meng Li, Chien Wei Chiang, Lei Wang, Yang Xiang, Lijun Cheng, Weixing Feng, Titus K. Schleyer, Sara K. Quinney, Heng Yi Wu, Donglin Zeng, Lang Li

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is an important source for detecting adverse drug event (ADE) signals. In this article, we propose a three-component mixture model (3CMM) for FAERS signal detection. In 3CMM, a drug-ADE pair is assumed to have either a zero relative risk (RR), or a background RR (mean RR = 1), or an increased RR (mean RR >1). By clearly defining the second component (mean RR = 1) as the null distribution, 3CMM estimates local false discovery rates (FDRs) for ADE signals under the empirical Bayes framework. Compared with existing approaches, the local FDR's top signals have noninferior or better sensitivities to detect true signals in both FAERS analysis and simulation studies. Additionally, we identify that the top signals of different approaches have different patterns, and they are complementary to each other.

Original languageEnglish (US)
Pages (from-to)499-506
Number of pages8
JournalCPT: Pharmacometrics and Systems Pharmacology
Volume7
Issue number8
DOIs
StatePublished - Aug 2018

ASJC Scopus subject areas

  • Modeling and Simulation
  • Pharmacology (medical)

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