[burger]

Presumably this would be great if true, as it would imply a much lower IFR. A few things give me pause:

1. They estimate that their test has a sensitivity of 80.3% and specificity of 99.5%. I'm not an expert, but doesn't the claimed specificity seem really high for basically ANY test? And doesn't a test that has a 99.5% specificity and an 80.3% sensitivity seem kind of wonky?

2. The 95% Confidence Intervals for the sensitivity was 72.1-87%, for specificity 98.3-99.9%. Aren't those fairly high to be extrapolating so much from this data? 

3. For whatever it's worth, John Ioannidis is identified as one of the co-authors.

The claimed specificity is not off base for a good antibody test, and in fact is basically what we need if we want to use these tests the way government leaders and ths media talk about these tests. If they really have a 99.5% specific test, that is fantastic and means we could actually use this for population studies for a low prevalence disease like this without having too many false positives. It would not be a surprise for sensitivity to be 80% either considering the different immune responses this virus seems to generate in people. If these numbers are accurate, that is great news, though it does mean missing out on 20% of patients who had the disease and don't know.

The URL is not loading for me though.

Got it - so just so I understand, if they are presuming ~80% sensitivity, for every 5 negative results they get, they code one as a false negative and therefore treat it as a presumed positive, is that right?

More or less.  It's more complicated than that because they have to account for specificity (false positives), too, though that rate was low.  They used Bayes rule to come up with a formula that included both.  Details here: https://www.medrxiv.org/content/medrxiv/...2463-1.pdf

I still think the sampling issue makes this pretty much useless. It would be interesting (and, imo, required if I was a reviewer on this paper) to divide the sample into those with and without prior symptoms and see how the % positive differs.

[burger]

Presumably this would be great if true, as it would imply a much lower IFR. A few things give me pause:

1. They estimate that their test has a sensitivity of 80.3% and specificity of 99.5%. I'm not an expert, but doesn't the claimed specificity seem really high for basically ANY test? And doesn't a test that has a 99.5% specificity and an 80.3% sensitivity seem kind of wonky?

2. The 95% Confidence Intervals for the sensitivity was 72.1-87%, for specificity 98.3-99.9%. Aren't those fairly high to be extrapolating so much from this data? 

3. For whatever it's worth, John Ioannidis is identified as one of the co-authors.

The claimed specificity is not off base for a good antibody test, and in fact is basically what we need if we want to use these tests the way government leaders and ths media talk about these tests. If they really have a 99.5% specific test, that is fantastic and means we could actually use this for population studies for a low prevalence disease like this without having too many false positives. It would not be a surprise for sensitivity to be 80% either considering the different immune responses this virus seems to generate in people. If these numbers are accurate, that is great news, though it does mean missing out on 20% of patients who had the disease and don't know.

The URL is not loading for me though.

Got it - so just so I understand, if they are presuming ~80% sensitivity, for every 5 negative results they get, they code one as a false negative and therefore treat it as a presumed positive, is that right?

More or less.  It's more complicated than that because they have to account for specificity (false positives), too, though that rate was low.  They used Bayes rule to come up with a formula that included both.  Details here: https://www.medrxiv.org/content/medrxiv/...2463-1.pdf

I still think the sampling issue makes this pretty much useless. It would be interesting (and, imo, required if I was a reviewer on this paper) to divide the sample into those with and without prior symptoms and see how the % positive differs.