I have long argued that the FDA has an incentive to delay the introduction of new drugs because approving a bad drug (Type I error) has more severe consequences for the FDA than does failing to approve a good drug (Type II […]
My take: this paper by Vahid Montazerhodjat and Andrew Lo is interesting, but it only looks at one issue, and there are many other problems that make overapproval more likely. There are many biases in the drug pipeline and FDA approval process, most of which are heavily in favor of approving drugs that do nothing (and yet, still have side effects). To mention one of many, the population used to test drugs is younger, healthier, more homogeneous, and more compliant than the population that ends up actually taking the drug. A second bias is that the testing process screens out people who have major side effects – they stop taking the drug, and are dropped from the sample (and from the statistical analysis at the end). So we only see the people with moderate or no side effects. Both of these problems lead to biases, which better statistical methods cannot remove.
The paper is interesting, but it is working from an idealized model of the drug research process, and I would not take its quantitative results seriously. The basic logic seems sound, though: there should be different approval standards for different diseases.