Signal detection in FDA AERS database using Dirichlet process.
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.
Department of Statistics, University of Missouri, MO 65201, Columbia, U.S.A.
Na
N
Hu
Office of Biostatistics, CDER, FDA, Silver Spring, 20993, MD, U.S.A.
Lan
L
Huang
Office of Biostatistics, CDER, FDA, Silver Spring, 20993, MD, U.S.A.
Ram C
RC
Tiwari
D016907:Adverse Drug Reaction Reporting Systems / Q000458:organization & administration / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods / Q000706:statistics & numerical data*; D016208:Databases, Factual; D006801:Humans; D016013:Likelihood Functions; D016017:Odds Ratio; D016012:Poisson Distribution; D018709:Statistics, Nonparametric; D014481:United States; D014486:United States Food and Drug Administration
false discovery rate; information component; pseudo-Bayes factor; pseudo-maximum likelihood; reporting rates; zero-inflated Poisson model