Background: Drug safety monitoring relies primarily on spontaneous reporting, but electronic health care record databases offer a possible alternative for the detection of adverse drug reactions (ADRs). Objectives: To evaluate the relative performance of different statistical methods for detecting drug-adverse event associations in electronic health care record data representing potential ADRs. Research Design: Data from 7 databases across 3 countries in Europe comprising over 20 million subjects were used to compute the relative risk estimates for drug-event pairs using 10 different methods, including those developed for spontaneous reporting systems, cohort methods such as the longitudinal gamma poisson shrinker, and case-based methods such as case-control. The newly developed method "longitudinal evaluation of observational profiles of adverse events related to drugs" (LEOPARD) was Measures: The area under the curve of the receiver operator characteristic curve was calculated for each method, both with and without LEOPARD filtering. Results: The highest area under the curve (0.83) was achieved by the combination of either longitudinal gamma poisson shrinker or case-control with LEOPARD filtering, but the performance between methods differed little. LEOPARD increased the overall performance, but flagged several known ADRs as caused by protopathic bias. Conclusions: Combinations of methods demonstrate good performance in distinguishing known ADRs from negative controls, and we assume that these could also be used to detect new drug safety signals.