Sequence symmetry analysis (SSA) is a method for detecting adverse drug events by utilizing computerized claims data. The method has been increasingly used to investigate safety concerns of medications and as a pharmacovigilance tool to identify unsuspected side effects. Validation studies have indicated that SSA has moderate sensitivity and high specificity and has robust performance. In this review we present the conceptual framework of SSA and discuss advantages and potential pitfalls of the method in practice. SSA is based on analyzing the sequences of medications; if one medication (drug B) is more often initiated after another medication (drug A) than before, it may be an indication of an adverse effect of drug A. The main advantage of the method is that it requires a minimal dataset and is computationally efficient. By design, SSA controls time-constant confounders. However, the validity of SSA may be affected by time-varying confounders, as well as by time trends in the occurrence of exposure or outcome events. Trend effects may be adjusted by modeling the expected sequence ratio in the absence of a true association. There is a potential for false positive or negative results and careful consideration should be given to potential sources of bias when interpreting the results of SSA studies.