Improved Apriori Method for Safety Signal Detection Using Post-Marketing Clinical Data

Abstract

Safety signal detection is an integral component of Pharmacovigilance (PhV), which is defined by the World Health Organization as “science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other possible drug related problems”. The purpose of safety signal detection is to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. While post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals, there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS) demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data.