Safety of medical products presents a serious concern worldwide. Surveillance systems of post-market medical products, have been established for continual monitoring of adverse events in many countries, and the proliferation of electronic health record systems (EHR) further facilitates continual monitoring for adverse events. In this study, we review existing statistical methods for signal detection that are mostly in use in post-marketing safety surveillance of spontaneously reported adverse events. We use three different methods to generate data (adverse event based, drug based and a modification of the Ahmed et al. (2009, 2010) method) to study the performance of the methods. Performance metrics include type I error, power, number of correctly identified signals, false discovery rate and sensitivity among others. The results show superior performance of the Likelihood Ratio test in all simulation studies. A critical discussion and recommendations for choosing from these methods are presented. An application to the FAERS database is illustrated using the Rhabdomyolysis-related adverse events reported to FDA during 3rd quarter of 2014 to the 1st quarter of 2017 for statin drugs.