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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 Aug 1

      5/n Most of these dotpoints are wrong in some way (heterogeneity causing an underestimate is particularly hilarious) but this statement about CoIs is wild considering that there are several potentially fraudulent studies in the IVM literaturepic.twitter.com/pWS2gaywrF

      8 replies 30 retweets 379 likes
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    2. Health Nerd‏Verified account @GidMK Aug 1

      6/n Going back to the heterogeneity point, this is the explanation from the authors about why heterogeneity is not a problem in their analysis. They appear to have entirely misunderstood what heterogeneity is (hint: this is more about BIAS than heterogeneity)pic.twitter.com/m2YLGSFfKy

      2 replies 19 retweets 327 likes
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    3. Health Nerd‏Verified account @GidMK Aug 1

      7/n Also worth noting, I've previously shown the heterogeneity is high in meta-analysis of IVM for COVID-19 mortality, and that's almost entirely because there are 2 studies that show a massive benefit and a bunch of studies that show no benefit at all

      5 replies 25 retweets 346 likes
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    4. Health Nerd‏Verified account @GidMK Aug 1

      8/n Anyway, back to the website - the authors then present this forest plot of effect estimates Each dot is a point estimate, and the lines around the dots represent confidence intervalspic.twitter.com/bQhmFfGU1L

      5 replies 17 retweets 245 likes
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    5. Health Nerd‏Verified account @GidMK Aug 1

      9/n Now, any data thug will immediately notice something wildly improbable about this forest plot (H/T @jamesheathers) Can you see the issue? 👀👀👀👀

      4 replies 16 retweets 247 likes
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    6. Health Nerd‏Verified account @GidMK Aug 1

      10/n While you have a think, here's a graph I made replicating these results. Not very pretty, but the final result is the same (with some minor rounding differences)pic.twitter.com/gDheBO5em6

      2 replies 19 retweets 236 likes
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    7. Health Nerd‏Verified account @GidMK Aug 1

      11/n Ok, so back to the question - why does this look problematic? It comes down to confidence intervals. When you've got a bunch of very wide confidence intervals from different studies, you expect the point estimates to move around inside them quite a bit

      2 replies 17 retweets 261 likes
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    8. Health Nerd‏Verified account @GidMK Aug 1

      12/n Instead, look at those point-estimates! Even though they've all got MASSIVE intervals, virtually all the PEs are within 0.05-0.1 either side of 0.15pic.twitter.com/2f7HQYpoCW

      3 replies 18 retweets 253 likes
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    9. Health Nerd‏Verified account @GidMK Aug 1

      13/n We can actually graph this. In Stata, I made what's called a funnel plot, which basically plots each point estimate against its standard error, with a line at the overall estimate from the meta-analysis modelpic.twitter.com/tF3U3ToFGv

      4 replies 19 retweets 253 likes
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    10. Bryan Adams, PhD‏ @lotusctr Aug 2
      Replying to @GidMK

      Oh come on! This is heterogenous data, funnel plots are not accurate to locate publication bias with such data or every journal publishing a MA would require them. They don't. Let's see your point estimates of the data and stop BSing people🤔

      1 reply 0 retweets 1 like
      Health Nerd‏Verified account @GidMK Aug 2
      Replying to @lotusctr

      Total nonsense. This has nothing to do with heterogeneity, it is the statistical breakdown of the data itself. All MAs with more than a couple of studies should have funnel plots or some other similar assessment it is standard practice

      1:17 AM - 2 Aug 2021
      • 1 Retweet
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      • kfunk937 literally lizard, seekmaking truthsenser Emma ní Dhúnlainge 🇵🇸 Sueytonius Arthur Foxache Swaledale Mutton Co.
      3 replies 1 retweet 6 likes

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