The FDA employs an average-patient standard when reviewing drugs: it approves a drug only if is safe and effective for the average patient in a clinical trial. It is common, however, for patients to respond differently to a drug. Therefore, the average-patient standard can reject a drug that benefits certain patient subgroups (false negatives) and even approve a drug that harms other patient subgroups (false positives). These errors increase the cost of drug development – and thus health care – by wasting research on unproductive or unapproved drugs. The reason why the FDA sticks with an average patient standard is concern about opportunism by drug companies. With enough data dredging, a drug company can always find some subgroup of patients that appears to benefit from its drug, even if the subgroup truly does not. In this paper we offer alternatives to the average patient standard that reduce the risk of false negatives without increasing false positives from drug company opportunism. These proposals combine changes to institutional design – evaluation of trial data by an independent auditor – with statistical tools to reinforce the new institutional design – specifically, to ensure the auditor is truly independent of drug companies. We illustrate our proposals by applying them to the results of a recent clinical trial of a cancer drug (motexafin gadolinium). Our analysis suggests that the FDA may have made a mistake in rejecting that drug.