Statistical and graphical approaches for disproportionality analysis of spontaneously-reported adverse events in pharmacovigilance

Abstract

AimCombine disproportionality analysis with dynamically interactive graphics to understand spontaneously-reported adverse events in pharmacovigilance. MethodsFour statistical methods, including Reporting Odds Ratio, Proportional Reporting Ratio, Multi-Item Gamma Poisson Shrinker and Bayesian Confidence Propagation Neural Network that are used for computing disproportionality are described. Tree maps and other graphical techniques are used to display the disproportionality results. ResultsSpontaneously-reported adverse events in pharmacovigilance are collected from physicians, patients, or the medical literature by regulatory agencies, pharmaceutical companies and device manufacturers to monitor the safety of a product once it reaches the market. In order to identify potential safety-signals, disproportionality analysis methods compare the rate at which a particular event of interest co-occurs with a given drug with the rate this event occurs without the drug in the event database. Tree maps are employed to interactively display the adverse events for particular drugs and compare the adverse events among the drugs. ConclusionInteractive graphical displays of disproportionality allow the analyst to quickly identify safety signals and perform additional follow-up analyses. Combining statistical methods with dynamically interactive graphics affords insights into the data inaccessible by traditional analysis methods.