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Detection of Adverse Drug Reaction Signals in the Thai FDA Database: Comparison between Reporting Odds Ratio and Bayesian Confidence Propagation Neural Network Methods

The study aimed to compare performance between the reporting odds ratio (ROR) and the Bayesian confidence propagation neural network (BCPNN) methods in identifying serious adverse drug reactions (ADRs) using the Thai FDA spontaneous database. The two methods were retrospectively applied to identify new, serious ADRs reported with antiretroviral therapy (ART) drugs using the data set between 1990 and 2006. We plotted the ROR and the information component against time to compare the differential timing of signal detection and the pattern of signaling over time between these methods. The ROR and the BCPNN methods identified the associations between ART drugs and serious ADRs at the same time. Both methods were similar in detecting the first signal of a potential ADR. However, the pattern of signaling seems relatively different with each method. Additional analyses of different drugs, ADRs, and databases will contribute to increase understanding of methods for postmarketing surveillance using spontaneous reporting system.