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
Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him
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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
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
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
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
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
9/n Now, any data thug will immediately notice something wildly improbable about this forest plot (H/T @jamesheathers)
Can you see the issue? 



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
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
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
How much of that is because the likelihood distribution is asymmetric? The right hand tail of the distribution is large, and this pulls the upper limit up a lot. log(effect) might look better
The funnel plot for ln(effect) looks pretty much identical (slightly worse tbh)pic.twitter.com/jrgAt343dr
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