Here’s a quote from the Greek physician Galen (c. 130-210 A.D.)
All who drink of this remedy recover in a short time, except those whom it does not help, who all die. Therefore, it is obvious that it fails only in incurable cases.
Imagine a dialog between Galen and a modern statistician.
Stat: You say your new treatment is better than the previous one?
Stat: But more people died on the new treatment.
Galen: Those patients don’t count because they were incurable. They would have died anyway.
The problem with Galen’s line of reasoning is that it is not falsifiable: no experiment could disprove it. He could call any treatment superior by claiming that evidence against it doesn’t count. Still, Galen might have been right.
Now suppose our statistician has a long talk with Galen and tells him about modern statistical technique.
Galen: Can’t you look back at my notes and see whether there was something different about the patients who didn’t respond to the new treatment? There’s got to be some explanation. Maybe my new treatment isn’t better for everyone, but there must be a group for whom it’s better.
Stat: Well, that’s tricky business. Advocates call that “subset analysis.” Critics call it “data dredging.” The problem is that the more clever you are with generating after-the-fact explanations, the more likely you’ll come up with one that seems true but isn’t.
Galen: I’ll have to think about that one. What do you propose we do?
Stat: We’ll have to do a randomized experiment. When each patient arrives, we’ll flip a coin to decide whether to give them the old or the new treatment. That way we expect about the same number of incurable patients to receive each treatment.
Galen: But the new treatment is better. Why should I give half my patients the worse treatment?
Stat: We don’t really know that the new treatment is better. Maybe it’s not. A randomized experiment will give us more confidence one way or another.
Galen: But couldn’t we be unlucky and assign more incurable patients to the better treatment?
Stat: Yes, that’s possible. But it’s not likely we will assign too many more incurable patients to either treatment. That’s just a chance we’ll have to take.
The issues in these imaginary dialogs come up all the time. There are people who believe their treatment is superior despite evidence to the contrary. But sometimes they’re right. New treatments are often tested on patients with poor prognosis, so the complaints of receiving more incurable patients are justified. And yet until there’s some evidence that a new treatment may be at least as good as standard, it’s unethical to give that treatment to patients with better prognosis. Sometimes post-hoc analysis finds a smoking gun, and sometimes it’s data dredging. Sometimes randomized trials fail to balance on important patient characteristics. There are no simple answers. Context is critical, and dilemmas remain despite our best efforts. That’s what makes biostatistics interesting.
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