Here's the result. The association disappears completely It looks like one study is driving all of these resultspic.twitter.com/hcmXJePEol
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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Here's the result. The association disappears completely It looks like one study is driving all of these resultspic.twitter.com/hcmXJePEol
So what is this study? Essentially, a simple observational survey of people in Singaporepic.twitter.com/H2qxbLvGJD
Now, I'm not going to critique this piece of research in-depth, but I think it's worth noting that it only surveyed 300 people, of whom 80 had IBS The other studies looked at a total of ~2,500 people
So what we're seeing in the meta-analysis is basically a series of negative results being totally overset by a single positive result That is not great scientifically!pic.twitter.com/xv5a3lDScx
It's a bit like tossing a coin 5 times, getting 4 tails and 1 heads, and concluding that heads is the right answer
This is especially true when you consider that the p-value is 0.064, which means that these results aren't even ~technically~ significant in any model!
But bringing this back to #scicomm - how is a journalist meant to know this? It's complex stuff. Most scientists I know aren't comfortable re-running a meta-analysis to see what happens when you exclude studies
And the press release, let's remember, is astonishingly positive. No mention of the MASSIVE question mark remaining after this research, just "pet owners more likely to have IBS"
The real finding from this analysis is that there may be a very modest increase in risk of IBS from owning a pet, but this seems unlikely at present based on the totality of the evidence
Who do we blame for the misreporting? I'll leave that to you There are many steps along the way that could've corrected this, but none were taken
SMALL CORRECTION The forest plot I included earlier in the analysis of the random-effects model was from the log-transformed variables (oops) here's the plot once exponentiated:pic.twitter.com/Me4zJfoI6w
Also, the p-value is 0.064 for this model, which is technically not significant. The effect size is also different from that reported in the abstract, however if I run a fixed effects model everything is exactly the same so I suspect that's what was actually done here
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