Three-Component Mixture Model-Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System.
CPT Pharmacometrics Syst Pharmacol
; 7(8): 499-506, 2018 08.
Article
em En
| MEDLINE
| ID: mdl-30091855
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Misturas Complexas
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
Limite:
Humans
País como assunto:
America do norte
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article