2/n The semi-anonymous site claims to be a "real-time meta analysis" of all published studies on ivermectin, collating an impressive 60 pieces of research It's flashy, well-designed, and at face value appears very legitimate
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3/n The benefits that this website show for ivermectin are pretty amazing - 96%(!) lower mortality based on 10,797 patients worth of data is quite astonishing. Sounds like we should all be using ivermectin! Except, well, these numbers are totally meaninglesspic.twitter.com/B0rrocOEpS
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4/n Digging into the site, you're immediately hit with this error. That's not how p-values work at all, any stats textbook will show you why this statement is entirely untruepic.twitter.com/Hzb4K1NYaH
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Replying to @GidMK
Hi, Health Nerd. I'm a statistican and textbook author. I computed the p-value and found the exact answer here. There is nothing about the statement I can see that is incorrect. A p-value is the result of the computation testing to see if a result could occur by random chance.
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Replying to @EduEngineer @GidMK
Perhaps you're reading in a meaning the author meant to express but didn't. e.g. perhaps they meant to say "a hypothetical ineffective treatment would generate" rather than "an ineffective treatment generated"
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Replying to @K_Sheldrick @EduEngineer
It's also worth noting that in the context of this meta-analytic model the p-value is entirely the result of the cherry-picking of "positive" values, so the chance of having a low p-value is 100% regardless of whether ivm works or not
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Replying to @GidMK @K_Sheldrick
No, there was no cherry-picking. There was an extremely forgiving set of inclusion-exclusion criteria that let in some positive and negative results, but left out almost nothing.
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Replying to @EduEngineer @K_Sheldrick
Of course there is cherry-picking throughout, it is rather boringly obvious. The anonymous authors of the website simply pick the most convenient values for their analysis so that they can have a better looking model regardless of severity etc
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I identified one example of this in the thread, but it's pretty much ubiquitous throughout the analysis
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Replying to @GidMK @K_Sheldrick
Can you name the author in the example of cherry-picking? I don't see it. Which study was misplaced in the inclusion-exclusion criteria?
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It's not the inclusion criteria, which are basically "chuck all the awful studies into one website". It's just that the authors extract only "positive" results regardless of whether studies actually showed a benefit
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