Who reads this anyway? Nobody, that’s who. I could write just about anything here, and it wouldn’t make a difference. As a matter of fact, I’m kinda curious to find out how much text can you dump in here. If you’re like really verbose, you could go on and on about any pointless…[no more than this]

  • 1 Post
  • 368 Comments
Joined 1 year ago
cake
Cake day: June 5th, 2023

help-circle




  • The idea of modern medicine is to sell chemical compounds that actually have an effect. It’s a philosophical and ethical thing. All products have a unique psychological effect that gets intertwined with their biochemical effect. If you can’t study them individually, it’s impossible to tell if the biochemical effect even exists at all. If your medicine relies heavily, or even entirely, on the psychological side, it’s no different than homeopathy. The idea of modern medicine is to be better than the old stuff that preceded it.

    I prefer to think of this as an equation like this: Pm+Bm=Pp+Bp

    Pm=psychological effect, medicine

    Bm=biochemical effect, medicine

    Pp=psychological effect, placebo = surprisingly big

    Bp=biochemical effect, placebo = 0

    If these sides are equivalent, the medicine is just as effective as placebo. If the medicine side is bigger, you’ll want to know how much of it comes from the P and B terms. In order to figure that out, you would need to know some values. Normally, you can just assume that Pm=Pp, but if you can’t assume that, it you’re left with two unknowns in that equation. In this case, you really can’t assume them to be equal, which means that your data won’t allow you to figure out how much of the total effect comes from psychological and biochemical effects. It could be 50/50, 10/90, who knows. That sort of uncertainty is a serious problem, because of the philosophical and ethical side of developing medicine.


  • Statistical tests are very picky. They have been designed by mathematicians in a mathematical ideal vacuum void of all reality. The method works in those ideal conditions, but when you take that method and apply it in messy reality where everything is flawed, you may run into some trouble. In simple cases, it’s easy to abide by the assumptions of the statistical test, but as your experiment gets more and more complicated, there are more and more potholes for you to dodge. Best case scenario is, your messy data is just barely clean enough that you can be reasonably sure the statistical test still works well enough and you can sort of trust the result up to a certain point.

    However, when you know for a fact that some of the underlying assumptions of the statistical test are clearly being violated, all bets are off. Sure, you get a result, but who in their right mind would ever trust that result?

    If the test says that the medicine is works, there’s clearly financial incentive to believe it and start selling those pills. If it says that the medicine is no better than placebo, there’s similar incentive to reject the test result and demand more experiments. Most of that debate goes out the window if you can be reasonably sure that the data is good enough and the result of your statistical test is reliable enough.


  • Yeah, that’s the thing with placebo. It’s surprisingly effective, and separating the psychological effect from actual chemistry can be very tricky. If most participants can correctly identify if they’re bing fed the real drug or a placebo, it makes it impossible to figure out how much each effect contributes to the end result. Ideally, you would only use effective medicine that does not need the placebo effect to actually work.

    Imagine, if all medicine had lots of placebo effect in them. How would you treat patients who are in a coma or otherwise unconscious?