It is unclear the extent to which best practices for phenotyping disease states from electronic medical records (EMRs) translate to phenotyping adverse drug events. Here we use statin-induced myotoxicity as a case study to identify best practices in this area. We compared multiple phenotyping algorithms using administrative codes, laboratory measurements, and full-text keyword matching to identify statin-related myopathy from EMRs. Manual review of 300 deidentified EMRs with exposure to at least one statin, created a gold standard set of 124 cases and 176 controls. We tested algorithms using ICD-9 billing codes, laboratory measurements of creatine kinase (CK) and keyword searches of clinical notes and allergy lists. The combined keyword algorithms produced were the most accurate (PPV=86%, NPV=91%). Unlike in most disease phenotyping algorithms, addition of ICD9 codes or laboratory data did not appreciably increase algorithm accuracy. We conclude that phenotype algorithms for adverse drug events should consider text based approaches.