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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
Verified account
@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. Health Nerd‏Verified account @GidMK 18 Nov 2020

      14/n Serology tests are used to find antibodies, and they are (as all tests are) imperfect So, usually we correct for the imperfections to get a better estimate of the true number of people with antibodies

      1 reply 3 retweets 45 likes
      Show this thread
    2. Health Nerd‏Verified account @GidMK 18 Nov 2020

      15/n In this case, the study found that 1.8% of people in the mask group had antibodies, compared to 2.1% of people in the non-mask group But those are just the RAW figurespic.twitter.com/NtiZuV9Uml

      1 reply 3 retweets 51 likes
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    3. Health Nerd‏Verified account @GidMK 18 Nov 2020

      16/n If we use the Rogen-Gladen estimator, which is a pretty standard correction for test characteristics, we see instead that 1.59% and 1.95% of people in masks/no masks were probably infected, respectivelypic.twitter.com/d4rPZ5kJVw

      3 replies 4 retweets 59 likes
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    4. Mike Dee‏ @MikeDeeeeeee 18 Nov 2020
      Replying to @GidMK

      I may be getting it wrong but would you mind checking your calculations? I get 1.07% and 1.51% respectively using the formula above. I use 90.2% for sensitivity and 99.2% for specificity as provided in the paper. [I get 0.65% and 1.11% for the sensitivity analysis of the authors]

      1 reply 0 retweets 0 likes
    5. Health Nerd‏Verified account @GidMK 18 Nov 2020
      Replying to @MikeDeeeeeee

      So, I was slightly off in those numbers, it's actually 1.56% vs 2.09% using the main results and the sens/spec from the internal validation study rather than the manufacturer. You have to take into account that some infections were confirmed by PCRpic.twitter.com/5jpxWkUItP

      2 replies 0 retweets 1 like
    6. Health Nerd‏Verified account @GidMK 18 Nov 2020
      Replying to @GidMK @MikeDeeeeeee

      So it's 1.8-0.2 = 1.6% for masks and 2.1-0.4 = 1.7% for non-masks, corrected that becomes 1.34% and 1.46%, add back in the PCR (for whom spec is ~100%) and you get 1.54% and 2.06%

      1 reply 0 retweets 1 like
    7. Health Nerd‏Verified account @GidMK 18 Nov 2020
      Replying to @GidMK @MikeDeeeeeee

      Ugh, just realized it should be 2.1-0.6 for the non-masks, which changes the numbers again to 1.54% vs 1.82%. Same general problem, an estimated 1.68% infected in the study

      1 reply 0 retweets 0 likes
    8. Mike Dee‏ @MikeDeeeeeee 18 Nov 2020
      Replying to @GidMK

      Sorry to be a pain, but can you check your Rogan-Gladen correction and/or the sensitivity/specificity numbers you use? When I use the formula, I get lower true prevalence. In the fully corrected version of yours, I get to 1.04% (masks) and 1.43% (control).

      1 reply 0 retweets 0 likes
    9. Health Nerd‏Verified account @GidMK 18 Nov 2020
      Replying to @MikeDeeeeeee

      I'm still not excluding the pre test people tho, this is just the numbers from the main analysis

      1 reply 0 retweets 0 likes
    10. Mike Dee‏ @MikeDeeeeeee 18 Nov 2020
      Replying to @GidMK

      Me neither in the numbers above. Take mask group. AB confirmed 37 out of 2383 = 1.55%. Rogan-Gladen correction with specificity 99.2% and sensitivity 90.2%: (1.55%+99.2%-1)/(99.2%+90.2%-1) = 0.75%/89.4% = 0.84%

      1 reply 0 retweets 0 likes
      Health Nerd‏Verified account @GidMK 18 Nov 2020
      Replying to @MikeDeeeeeee

      Yep but I'm using the internal validation numbers for sens/spec rather than the manufacturer numbers, so 82.5% and 99.5%

      4:24 PM - 18 Nov 2020
      • 1 Like
      • Mike Dee
      1 reply 0 retweets 1 like
        1. Health Nerd‏Verified account @GidMK 18 Nov 2020
          Replying to @GidMK @MikeDeeeeeee

          I'm generally very mistrustful of manufacturer numbers because they are almost always higher than those found in validation studies by other people 😂

          0 replies 0 retweets 1 like
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