Evidence-based medicine is an important initiative of the U.S. National Institutes of Health. However, published data are limited. Medical knowledge can be advanced through pharmacoepidemiology and data mining research of very large databases, data warehouses. Before Vioxx™ (rofecoxib) and Celebrex™ (celecoxib) were publicly suspected of being associated with acute myocardial infarction and stroke, etodolac was evaluated for gastrointestinal safety using a data warehouse constructed from several data sources and data mining tools embedded with pharmacoepidemiologic methods. Results were published in a top tier medical journal, and established gastrointestinal safety of a generic COX-2 analgesic medication. This project demonstrates that the knowledge needed for evidence-based medicine can be augmented through pharmacoepidemiology research using data mining and data warehousing.

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Benford's Law was first described by an astronomer in 1881, but physicist Frank Benford lent his name to the property in a mathematical treatise published in 1938. Behaviour of numbers described by the Law defies intuition, demonstrating that one is the most frequent (30.1%), and nine is the least frequent (4.6%). The property holds for a wide variety of numbers, including but not limited to: stock indices, river lengths, road numbers, etc. Departures from the classic Benford distribution are linked to anomalies, specifically in financial data where the property has been successfully employed in financial audits. The limitation of Benford's Law is that it identifies a relatively large pool of "candidate" anomalies that must be manually evaluated. In the present analysis of Medicaid data, multivariate cluster analysis in multiple tandem analyses is used to winnow the number of anomalies to a pool of high probability anomalies for evaluation. This approach makes the application of Benford's Law more practical.