I haven’t looked at the paper itself, just the Age report. But wouldn’t the only way the entire study be contradicted is if the antibody test literally didn’t work?
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Replying to @Vic_Rollison @JamesUVanDyke
The Age report contains all the info needed to conclude that no firm conclusions can be drawn from the study. 41 of 2991 tested +ve to antibodies to the virus. False +ve rate of test = 1.09. Article itself says sample size is too small to make confident estimate; understatement!
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Replying to @orpheuseurydice @JamesUVanDyke
They’ve probably used largest sample they had access to - convenience sample of hospital admissions so not universalisable. I have no idea what sample would be needed to make the results definitive, but I do know opinion polls infer 16 million voters by surveying only 1,200
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Replying to @Vic_Rollison @JamesUVanDyke
Yes. The fact that it is too small a sample size would be a qualification made by the authors of the study themselves. Equally problematic is the false +ve rate for antibodies. 9 in 100, ie nearly 10% are false +ves. This error is amplified if you extrapolate.
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Replying to @orpheuseurydice @JamesUVanDyke
If 10% are false positives, then the study is still 90% accurate so still shows a large amount of undetected spread.
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That is incorrect. With prevalence of 2 in 1,000, 10% false positives means that 998 tests will pick up 99 false positives, 2 tests will be true positive, for a rate of 2% positive predictive value (or 2% 'accurate')
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Dr Coatesworth says the model estimates “somewhere between 0-185,000 cases”. I don’t think I’d base any policy decisions on that!pic.twitter.com/VjoGcttFNY
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Somewhere between 0-185,000, but everyone just hopes it’s zero and moves on. They found undetected spread - double tested positives to make sure. So much wishful thinking going on. 40% asymptomatic. Easy to see how undetected spread happens.
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I'm not sure what you mean by "hope" here. The point others have rightly made is that the confidence interval includes 0, which means the results are not statistically distinguishable from a situation where all of the positives in the study were false positives
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So how do you explain their explanation of their confidence of 0.28% positive based on double testing? They’re the ones who did the study. I’m just reading their analysis of their results.
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I'm not going to speak for the authors, merely pointing out the fact that with a confidence interval including zero it is possible that there were no true positive results in the study. This is the uncertainty that I believe Dr Coatsworth was expressing in the quote above
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