Improving Automated Database Studies

Is the age of maturity for automated databases (predicted for decades) finally arriving? Database use has increased dramatically, particularly for the study of medications and other therapeutic interventions. This trend is fueled by the growing recognition that randomized controlled trials, while essential, cannot answer all of the important questions related to the efficacy and safety of therapeutics, ever-improving informatics technology, and advances in epidemiologic methods. Indeed, the recent debut of the US multimilliondollar FDA Sentinel Initiative testifies to the high expectations for automated databases. A session at the 2010 meeting of the Society for Epidemiologic Research sounded an important note of caution. It is undisputed that automated databases have the potential to enable very large retrospective studies of both relative safety and efficacy that would be infeasible with traditional methods. However, the symposium presenters cautioned that this potential must be balanced against the nontrivial limitations of these data systems. The underlying premise of database studies is deceptively simple—use computerized information for very large populations to rapidly define cohorts and compare end point occurrence in exposed versus unexposed groups. But the reality of these studies is far more complex, and there are many pitfalls that await the unwary (Table).