6/n See, the authors powered their study (i.e. recruited participants) assuming that masks decreased your risk of infection by 50%, which is quite a lot!pic.twitter.com/WcBowCIJ7u
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17/n This sounds like a minor point, but it actually isn't - if only 1.59%/1.95% of people were infected, it means that the study was underpowered for its main analysis, and thus we can't conclude much from the results
18/n Sorry, small correction - I used the final totals of 1.8% and 2.1% not the actual antibody numbers of 1.6% and 1.7% in that calculation. If you apply the correction properly, you get 1.56% masks and 2.09% non-masks
19/n For some context, to find a difference this small, the study would've needed to recruit about 24,000 people, or 12,000 in each group, which is about 4x as big
That's the result for per-protocol analysis where participants with missing baseline antibody tests are included (wrong as study period was towards the end of the first wave). The raw estimates for the sensitivity analysis where those participants were excluded are 1.4%/1.8%.pic.twitter.com/1GPYs34urC
Sure, but that doesn't correct for test characteristics it corrects for the possibility that they included people who they shouldn't have in the study
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]
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
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