For the epi nerds, when I run it with a fixed-effects model my results are the same as those reported in the paper, but my random-effects model CI crosses 0 
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Replying to @GidMK
what method did the paper use? fixed-effects (common-effects) seems rarely appropriate for most situations
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Replying to @dailyzad
That's the thing - they report using a random effects model, but I can't replicate that in Stata. I'd probably need to see their code to get the precise effect estimate they generatedpic.twitter.com/2fH3xll8Tc
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(I'm also using the DerSimonian and Laird variance estimation, but my guess is that the procedure in Stata produces very slightly different results than other statistical software)
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Replying to @GidMK
Hmm interesting. I don't think they actually used the DL method... I calculated the standard errors by taking the natural logs ln(UL/LL) and tried to reproduce it with *metan logOR logSE, fixed eform* I got much wider interval estimates (as expected with random effects)pic.twitter.com/y7kXtgU7YU
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I got the reported results with *metan logOR logSE, fixed eform*. Double checked on R with metafor and got similar results. Any case... I don't think it actually matters whether the interval includes the null or not. All of included studies are regarding nonrandom exposurespic.twitter.com/Ng2PJYtv9A
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Replying to @dailyzad
Yes I saw exactly the same. I reckon they misreported the model they used in the abstract I do agree about the nonrandom exposures, but I think it's worth noting that, done properly, even their own analysis doesn't show an effect
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Also, it's really not ideal practice to not at least do a sensitivity analysis excluding the single study that's driving the entire effect, but to be fair that's what I'd expect from a conference abstract which is why this shouldn't have been a news story in the first place!
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Just noticed if you exclude that study the I^2 drops from 27% to 0% so I reckon there's definitely a rationale for using the sensitivity analysis as the most robust estimate
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Replying to @GidMK
The contribution it has to the summary effect is interesting, but not sure if the analysis excluding it is more robust. Heterogeneity variance is a part of life and estimators need to account for it, would've been better to use REML or HKSJ rather than DL given the # of studies
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That's fair, it's just a bit of a red flag imo if you've got several studies with much lower prevalence and then one study that's found a base rate of ~20% IBS and contributes quite a bit of heterogeneity to the model as well
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Replying to @GidMK
Indeed, I'm not yet convinced from the abstract provided
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