Often, perhaps usually, a treatment will make some people better, do nothing for others, and make some people worse.
When a treatment makes about as many people worse as it makes better, then meta-analysts say it doesn’t work.
But often it *does* work - for *some* people. Indeed, it might work very well indeed for them.
The other people - those for whom it makes no difference or makes worse - just need to stop using it - then everyone benefits.
It is just a matter of using patient response as a feedback loop.
Is that really so difficult to understand? - yet I can never get this point across to bio-statisticians/ epidemiologists, meta-analysts and the like…
3 comments:
"Is that really so difficult to understand? - yet I can never get this point across to bio-statisticians/ epidemiologists, meta-analysts and the like…"
But this is not difficult to understand. Indeed, it is just common sense.
Which reminds me; I had a teacher who used to say, "The only trouble with common sense is that it's not the common".
Apropos your post:
http://www.ribbonfarm.com/2010/09/28/learning-from-one-data-point/
In a bureaucratic state, a treatment that will prolong the life and well-being of ten terminal patients is not sufficient if that treatment will kill one terminal patient. And no one will be permitted to choose for himself.
A bureaucrat is not paid to understand. Our original mistake was in permitting him any access to decisions.
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