4 ms·

It doesn't matter if you eliminate 10 million confounds. No matter how good your correlation study is, it cannot establish a causation. To do that you need rand

It doesn't matter if you eliminate 10 million confounds. No matter how good your correlation study is, it cannot establish a causation. To do that you need random assignment at the very least.

ncmncm 6y ago
... and even that gives you the wrong answer if your diagnostic specificity is faulty. This last is why we keep seeing muddled reports that "antidepressants don't work". Diagnosis of "depression" is a snakepit; the only known way to discern which among at least six conditions you might have, all labeled "depression", is to see what medications work, if any. Imagine trying to conduct a plausible RCT for that. "Gold standard"? Ha.
- oehtXRwMkIs 6y ago
  Did you reply to the wrong comment?
  - ncmncm 6y ago
    I replied to "...it cannot establish a causation. To do that you need random assignment at the very least."
    - oehtXRwMkIs 6y ago
      Oh, your quoting of "gold standard" really threw me off.
0d9eooo 6y ago
10 million confounds is a lot to eliminate. Randomization only works because it decreases the probability of a spurious association existing. At some point the number of confounds eliminated would probably be more worthwhile. I'd definitely pay more attention to a study that eliminated 10 million confounds than most randomized controlled trials. I agree a randomized controlled trial would be nice now. But even that is potentially fraught. For example, someone else mentioned the possibility that glucosamine reduces joint pain, which increases mobility, which increases longevity. Randomizing wouldn't really control for that sort of scenario. And that's not even getting into preregistration, meta-analysis etc.