Whichever is the case, before journalists publicize any more results from this group, they should know that the confidence intervals reported in both studies have no known statistical provenance as of now. The calculations are not questionable; they are either wrong or unknown.
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What should we believe while we wait for a defensible analysis from the authors? In my opinion, the analysis suggested by
@graduatedescent using Fisher's exact test should be treated as authoritative until the authors are ready to give a competing account.https://bit.ly/2XR0pdL3 replies 76 retweets 434 likesShow this thread -
@graduatedescent shows the SCC data are too noisy even to rule out the possibility that all the positives are false positives. Simply put, the difference between 50 heads in 3330 flips (SCC residents) and 2 heads in 371 flips (negative controls*) isn't statistically significant.7 replies 60 retweets 445 likesShow this thread -
The authors have demographic information they have not yet shared, so it's conceivable a more refined analysis will pin down the prevalence more precisely. My point is that right now, as far as I know, no such analysis exists.
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Note that beyond the formal statistical analysis there are other good reasons to be skeptical of the study, which have been pointed out publicly by
@graduatedescent,@nataliexdean,@StatModeling, and many others.5 replies 32 retweets 259 likesShow this thread -
Will Fithian Retweeted John Cherian
Thanks also to
@jjcherian for his perceptive tweets about the paper that piqued my interest in the first place.https://twitter.com/jjcherian/status/1251272333177880576?s=20 …Will Fithian added,
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*The "negative controls" are blood samples from people who were known not to have been infected with COVID-19. The supplement I refer to can be found at https://www.medrxiv.org/content/medrxiv/suppl/2020/04/17/2020.04.14.20062463.DC1/2020.04.14.20062463-1.pdf …
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Replying to @wfithian
Can you explain this in layman’s terms what is wrong with their data?
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Replying to @notalemming70 @wfithian
everything depends on the unproven specificity of their test characteristics. Only 1.5% positive tests in their sample. Unless specificity >>> 98.5% then it's within the realm of statistical possibility that many/all of the positives were actually false positives
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thanks- in my heart im praying the authors are correct in their projections but it seems irresponsible that they’ve been making the media circuit before even basic peer review
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