Objectives: Quantitative data mining methods can be used to identify potential signals of unexpected relationships between drug and adverse event (AE). This study aims to compare and explore the use of three data mining methods in our small spontaneous AE database.
Methods: We consider reporting odds ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN) and Gamma Poisson Shrinker (GPS) assuming two different sets of criteria: (1) ROR-1.96SE>1, IC-1.96SD>0, EB05>1 (2) ROR-1.96SE>2, IC-1.96SD>1, EB05 >2. Count of drug-AE pairs ≥3 was considered for ROR and GPS.
Results: The Health Sciences Authority, Singapore received 151,180 AE reports between 1993 and 2013. ROR, BCPNN and GPS identified 2,835, 2,311 and 2,374 significant drug-AE pairs using Criterion 1, and 1,899, 1,101 and 1,358 respectively using Criterion 2. The performance of the three methods with respect to specificity, positive predictive value and negative predictive value were similar, although ROR yielded a higher sensitivity and larger area under the receiver operating characteristic curve. ROR and GPS picked up some potential signals which BCPNN missed.
Conclusions: The defined threshold used for ROR (Criterion 1) is a useful screening tool for our small database. It may be used in conjunction with GPS to avoid missed signals.
Keywords: Adverse drug event; data mining; disproportionality analysis; drug safety; pharmacovigilance; signal detection.