Premium
Signal detection in FDA AERS database using Dirichlet process
Author(s) -
Hu Na,
Huang Lan,
Tiwari Ram C.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6510
Subject(s) - frequentist inference , computer science , dirichlet process , bayesian probability , nonparametric statistics , poisson distribution , data mining , statistics , dirichlet distribution , bayesian inference , mathematics , artificial intelligence , mathematical analysis , boundary value problem
In the recent two decades, data mining methods for signal detection have been developed for drug safety surveillance, using large post‐market safety data. Several of these methods assume that the number of reports for each drug–adverse event combination is a Poisson random variable with mean proportional to the unknown reporting rate of the drug–adverse event pair. Here, a Bayesian method based on the Poisson–Dirichlet process (DP) model is proposed for signal detection from large databases, such as the Food and Drug Administration's Adverse Event Reporting System (AERS) database. Instead of using a parametric distribution as a common prior for the reporting rates, as is the case with existing Bayesian or empirical Bayesian methods, a nonparametric prior, namely, the DP, is used. The precision parameter and the baseline distribution of the DP, which characterize the process, are modeled hierarchically. The performance of the Poisson–DP model is compared with some other models, through an intensive simulation study using a Bayesian model selection and frequentist performance characteristics such as type‐I error, false discovery rate, sensitivity, and power. For illustration, the proposed model and its extension to address a large amount of zero counts are used to analyze statin drugs for signals using the 2006–2011 AERS data. Copyright © 2015 John Wiley & Sons, Ltd.