An evaluation of statistical approaches to postmarketing surveillance

Abstract

Safety of medical products presents a serious concern worldwide. Surveillance systems of postmarket medical products have been established for continual monitoring of adverse events (AEs) in many countries, and the proliferation of electronic health record systems further facilitates continual monitoring for AEs. We review existing statistical methods for signal detection that are mostly in use in postmarketing safety surveillance of spontaneously reported AEs and we study their performance characteristics by simulation. We compare those with the likelihood ratio test (LRT) method (appropriately modified for use in pharmacovigilance) and use three different methods to generate data (AE based, drug based, and a modification of the method of Ahmed et al). Performance metrics include type I error, power, sensitivity, and false discovery rate, among others. The results show superior performance of the LRT method in almost all simulation experiments. An application to the FDA Adverse Event Reporting System database is illustrated using rhabdomyolysis-related preferred terms reported to FDA during the third-quarter of 2014 to the first-quarter of 2017 for statin drugs. We present a critical discussion and recommendations for use of these methods.

Keywords: adverse event identification; likelihood ratio test; pharmacovigilance; postmarketing surveillance; signal detection.