Signal detection in FDA AERS database using Dirichlet process.

Abstract:

: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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Hu N,Huang L,Tiwari RC

doi

10.1002/sim.6510

subject

Has Abstract

pub_date

2015-08-30 00:00:00

pages

2725-42

issue

19

eissn

0277-6715

issn

1097-0258

journal_volume

34

pub_type

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