Is the FDA Too Conservative or Too Aggressive?

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 […]

Source: Is the FDA Too Conservative or Too Aggressive?

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.

Using data mining to ban trolls on League of Legends

Something I just found for my Big Data class.

Riot rolls out automated, instant bans for League of Legends trolls

Machine learning system aims to remove problem players “within 15 minutes.”

An interesting thread of player comments has a good discussion of potential problems with automated bans. Only time will tell how well the company develops the system to get around these issues.

This company also took an experimental approach to banning players. And hired 3 PhDs in Cognitive Science to develop it. (Just to be clear, their experiments did not appear to be automated A/B style experiments.) After the jump is a screen shot from that system.

League of Legends screen shot

But, I’m not tempted to play League of Legends to study player behavior and experiment with getting banned! (I don’t think I’ve ever tried an MMO beyond some prototypes 15 years ago.)  If any players want to post your observations here, great.

When the doctor’s away, the patient is more likely to survive | Ars Technica

When the doctor’s away, the patient is more likely to survive | Ars Technica.

Very surprising. When cardiologists are away from the hospital, deaths after heart failure or cardiac arrest declined. I’ll probably use this in my course this Spring. (Or perhaps in both courses: Big Data, and Operations Quality in Healthcare.)

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