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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
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@GidMK

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Health NerdVerified account

@GidMK

Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him

Sydney, New South Wales
theguardian.com/profile/gideon…
Joined November 2015

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    1. J Chamie‏ @jjchamie Aug 12
      Replying to @GidMK @EdoajoEric

      A very big issue with residual confounding? Numeric errors in the tables? Can you be more specific? I think @PPMERlNO would like to check that out.

      1 reply 0 retweets 1 like
    2. Health Nerd‏Verified account @GidMK Aug 12
      Replying to @jjchamie @EdoajoEric

      Sure thing - the numeric errors are pretty simple, it looks like some people were excluded that are not reported as far as I can see in the text. The text says 77,381 and 156,468 but the values in the tables add up to 77,327 and 156,011 respectivelypic.twitter.com/dmXP5gURtW

      1 reply 0 retweets 2 likes
    3. Health Nerd‏Verified account @GidMK Aug 12
      Replying to @GidMK @jjchamie @EdoajoEric

      This also matches the percentages given - 48,511 is 31.09% of 156,011, not 156,468. Not a major issue, but should probably be corrected

      1 reply 0 retweets 2 likes
    4. Health Nerd‏Verified account @GidMK Aug 12
      Replying to @GidMK @jjchamie @EdoajoEric

      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

      2 replies 0 retweets 3 likes
    5. J Chamie‏ @jjchamie Aug 12
      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?

      1 reply 0 retweets 4 likes
    6. Health Nerd‏Verified account @GidMK Aug 12
      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 …

      1 reply 0 retweets 6 likes
    7. J Chamie‏ @jjchamie Aug 12
      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%

      1 reply 0 retweets 3 likes
    8. Health Nerd‏Verified account @GidMK Aug 12
      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

      1 reply 0 retweets 4 likes
    9. J Chamie‏ @jjchamie Aug 12
      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

      1 reply 0 retweets 5 likes
    10. Health Nerd‏Verified account @GidMK Aug 12
      Replying to @jjchamie @EdoajoEric

      Of course it can! This is a pretty fundamental part of epidemiology

      2 replies 0 retweets 4 likes
      Health Nerd‏Verified account @GidMK Aug 12
      Replying to @GidMK @jjchamie @EdoajoEric

      Oh hey, the authors acknowledge exactly this point in the paperpic.twitter.com/dYxfSq8LCm

      6:57 PM - 12 Aug 2021
      • 2 Likes
      • 🇪🇸 Balkrai 🇪🇸 Atomsk's Sanakan
      0 replies 0 retweets 2 likes

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