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

Autor: Zhang P; Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, Ohio, USA., Li M; Biomedical Engineering Institute, College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China.; CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, China., Chiang CW; Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, Ohio, USA., Wang L; Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, Ohio, USA.; Biomedical Engineering Institute, College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China., Xiang Y; Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, Ohio, USA., Cheng L; Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, Ohio, USA., Feng W; Biomedical Engineering Institute, College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China., Schleyer TK; Department of Medicine, Indiana University, Indianapolis, Indiana, USA., Quinney SK; Department of Obstetrics and Gynecology, Indiana University, Indianapolis, Indiana, USA., Wu HY; Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, Ohio, USA., Zeng D; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA., Li L; Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, Ohio, USA.
Jazyk: angličtina
Zdroj: CPT: pharmacometrics & systems pharmacology [CPT Pharmacometrics Syst Pharmacol] 2018 Aug; Vol. 7 (8), pp. 499-506. Date of Electronic Publication: 2018 Aug 09.
DOI: 10.1002/psp4.12294
Abstrakt: 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.
(© 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.)
Databáze: MEDLINE
Externí odkaz: