A Comparative Study of Data Mining Algorithms used for Signal Detection in FDA AERS Database

Objective: Signal detection is a technique in pharmacovigilance for the early detection of new, rare reactions (desired or undesired) of a drug. This study aims to compare and appraise the performance of data mining algorithms used in signal detection. Method: Most commonly used three data mining algorithms (DMAs) (Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR) and Information Component (IC)) were selected and applied retrospectively in USFDA Adverse Event Reporting System database to detect five confirmed Drug Event Combinations. They were selected in such a way that the drug is withdrawn from the market or label change between 2006-2015. A value of ROR-1.96SE>1, PRR≥2, χ2>4 or IC- 2SD>0 were considered as the positive signal. The data mining algorithms were compared for their sensitivity and early detection. Result: Among the three data mining algorithms, Information Component was found to have a maximum sensitivity (100%) followed by Reporting Odds Ratio (60%) and Proportional Reporting Ratio (40%). Sensitivity associated with the number of reports per drug event combination and early signal detection suggested that information component needs comparatively fewer reports to show positive signal than the other two data mining algorithms. ROR and PRR showed comparable results. Conclusion: Early detection of a reaction is possible using signal detection technique. Information component was found to be sensitive method compared with other two data mining algorithms in FDA Adverse Event Reporting System database. As the number of reports of drug event combination increased, the sensitivity and comparability of data mining algorithm also increased. Key words: Signal Detection, Data mining algorithms, FDA AERS Database, Disproportionality Analysis, Pharmacovigilance.

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