As to residual confounding - there are numerous potential differences between people who got packs and those who didn't. For one thing, I don't see any discussion of SES in the paper, which is a pretty big potential confounder
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Replying to @GidMK @EdoajoEric
Again, you ground your mistrust in tiny impactless items. You have an enormous improvement in hospitalization (76%) and your arguments absolutely can't explain even 10% of the impact. Do you think your points would turn the results insignificant?
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Replying to @jjchamie @EdoajoEric
Of course - residual confounding can entirely reverse the effects of an analysis. That's a very well-demonstrated epidemiological fact https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704-ep713_confounding-em/BS704-EP713_Confounding-EM4.html …
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Replying to @GidMK @EdoajoEric
They controlled in the analysis by age and comorbidities, and looked at more than 230,000 people. The results was 76% less hospitalizations in the group with ivermectin. After the study they rolled our IVM in the country and every COVID metric improved. CFR dropped 80%
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Replying to @jjchamie @EdoajoEric
So firstly, same issue as in your analysis - it's almost certain people in Mexico were taking the drug well before these packs were handed out. Age and comorbidities were controlled for in a fairly crude way, and there's a large potential for residual confounding
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Replying to @GidMK @EdoajoEric
Agreed, it's almost certain people in Mexico were taking the drug. This factor potentially reduces the study positive result because some in the control group are treated too Your recurrent card is "potential for residual confounding" This unknown can't explain big differences
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Replying to @jjchamie @EdoajoEric
Of course it can! This is a pretty fundamental part of epidemiology
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Replying to @GidMK @EdoajoEric
A 76% reduction in hospitalization in the ivermectin group. Same region, same season, same variant, controlled for age and comorbidities based on 230,000 people. Your theory that it's something else is an isoteric-sounding explanation. You aren't defending the truth.
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Replying to @jjchamie @EdoajoEric
Same income? Same HbA1c? Same access to hospitals? Same index of homelessness? Etc etc etc. As the authors say, you can only control for the factors you measure, and even with comorbidities they only had data on a small number it seems
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Replying to @GidMK @EdoajoEric
Well, it's 230,000 people with Covid. Everyone with a positive covid test in Mexico city in a period of two months. C'mon you can't have a better matching groups. And 76% reduction in hospitalizations. 76% Your theory is an impossibility
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It is not. Large numbers have no bearing whatsoever on residual confounding
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Replying to @GidMK @EdoajoEric
You're wrong and you know it. The residual confounding have to be enormous to explain the difference, so big that it must be evident and it isn't. Same in Peru, same in Uttar Pradesh India, same in other places. Residual confoundings do explain Together trial result indeed.
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Replying to @jjchamie @EdoajoEric
You appear to genuinely have no idea what confounding is. This is a reasonable primer if you're interested in knowing why it's an issue for this analysis but not the Together trial: https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC11.pdf …
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