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The paper by de Vries and colleagues examined different observational study designs used to assess the effects of statin use and demonstrates three key points. First, the need for accurate matching on key prognostic variables—in this case age. Second, the need to consider the underlying biology when designing epidemiological studies. Third, they suggest that a case-control study nested within a selected group of people may be more prone to bias than a population-based study. However there may be a bigger issue: both the observational designs found a substantial protective effect, while robust data from randomized trials suggests there is no effect. Does this mean we should do away with observational studies of drugs? Clinically important but unexpected adverse effects of drugs are often too rare to be detected in randomized trials. Spontaneous reports of possible adverse effects are often sufficient to lead to withdrawal of a drug from the market.1 High quality adequately powered observational studies would provide a much better evidence base for such decisions. Indeed, observational studies to assess possible adverse events of currently used drugs are now required under guidance issued by the European Medicines Agency.2 The importance of adverse effects is clear. However, unexpected beneficial effects (such as statins preventing fractures) could also be of great potential benefit to patients. Most drugs affect many different cells tissues and organs, but during drug development interest is largely focused on a therapeutic effect in a single organ system. Studies encompassing all the likely beneficial and adverse effects of a drug can help inform prescribing decisions. Computerized clinical data offer an attractive way to study unexpected drug effects in a large, representative population. However, the paper by de Vries demonstrates some of the possible problems that can arise when using such data.

Commentary: We still need observational studies of drugs—they just need to be better