FDA drug reviewers are immune from legal liability, but they may be reproached and humiliated by congressional hearings, television exposés, and newspaper condemnations. Why don’t congressional representatives, television reporters, and newspaper columnists bring the deadly consequences of FDA delay to light? Answer: When the side effects of a new drug cause little Tommy of 236 Elm Street, Saginaw, Michigan, to become gravely ill, television reporters show the poor lad languishing in a hospital bed, and viewers respond emotionally. When little Tommy dies, reporters interview the grieving parents. Blame falls on the drug company, on the FDA officials who approved an unsafe drug, and maybe on the doctor. FDA reviewers are anxious to avoid such censure, which might damage their careers and reputations.
The consequences of error in the other direction, however, are not symmetric. If little Tommy suffered from a disease that would be cured by a drug not yet allowed by the FDA, it is unlikely that Tommy’s parents or doctors would even be aware of that fact. If they heard about the not-yet-allowed drug and inquired into its availability, the FDA may simply say that it “must hold the unproved drug until safety questions and risks to the public health are resolved.” No one who could counter such claims would be in a position to do so. Thus, the bad consequences of disallowing the drug would not be identifiable and would not revisit the FDA. In consequence, FDA officials are much less concerned about such consequences. Only in rare cases in which suffering patients have been well organized and vocal, in particular in AIDS cases, have FDA officials taken much heat for withholding approval. Even then the heat is not extreme because the FDA officials can always claim that they are simply doing their job in delaying the approval of experimental drugs. Researchers have long noted that in anxiously seeking to avoid risk of approving an unsafe drug, FDA officials often fall deeply into the inverse error: disallowing valuable drugs. Figure 3 shows the two types of error and indicates the reason for systematic bias toward type 2 errors.