Can someone clarify which type of test they analyzed? 80% seems way too high. I would expect something closer to 10% at most (which would still mean the probability of a true positive might be very low per Bayes' theorem)
Potential false-positive rate among the 'asymptomatic infected individuals'
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Re: Potential false-positive rate among the 'asymptomatic infected individuals'
#32Earlier quoted context omitted.
I don’t follow but am interested in your point. Please elaborate.
Suppose a test has a 1/100 false positive rate, and the true incidence of the disease over the population that you're testing is 1/1000 (that is, you expect a true positive every 1/1000 times). In this case, every positive test you observe is around 10x more likely to be a false positive than a true positive.
Got it. Thanks! I have a masters in public health and still need to get schooled by hacker news! Haha
Re: Potential false-positive rate among the 'asymptomatic infected individuals'
#33One fallacy that seems universal with healthcare folks is they think the false positive rate is the chance that a given positive result is erroneous. If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. This is why prior probabilities need to be taken into account in making decisions.
This is just a form of the base rate fallacy, correct?
Re: Potential false-positive rate among the 'asymptomatic infected individuals'
#34Can someone clarify which type of test they analyzed? 80% seems way too high. I would expect something closer to 10% at most (which would still mean the probability of a true positive might be very low per Bayes' theorem)
Are you confused or am I misreading your comment? The result is that 80% of positives are false positives, not that 80% of all tests are false positives. (IMO it is still fishy.)
Re: Potential false-positive rate among the 'asymptomatic infected individuals'
#35Re: Potential false-positive rate among the 'asymptomatic infected individuals'
#36One fallacy that seems universal with healthcare folks is they think the false positive rate is the chance that a given positive result is erroneous. If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. This is why prior probabilities need to be taken into account in making decisions.
I appreciate all the answers that point out Bayes's theorem. One thing to add is that in contrast to the usual classroom Bayes the "occurrence in the general population" is very much a variable rather than a constant. For example, Germany's low death rate can be a side effect of the false positive issue in the paper.
In fact, the stark difference between Italy and Germany would provide support to the paper's conclusion.
Re: Potential false-positive rate among the 'asymptomatic infected individuals'
#37Earlier quoted context omitted.
Or think about it this way, this means as a percentage of real people infected, the mortality rate of COVID-19 is higher than thought. This false-positive rate means that the number of real cases is lower than reported and therefore mortality rate is higher. In other words, COVID-19 is more dangerous than thought if you catch it. Then again, I'm sure the actual cases dwarfs those reported since not everyone has been…
Mortality is almost always much lower than the initially reported and observed numbers. There could be many more people that never even get to the testing phase.
The same would be true of things like the Flu then too. Which means the true mortality rate of Flu is much lower, so even if COVID-19 is lower than current estimates for the same reason, it is still an order of magnitude higher than something like the Flu.
Re: Potential false-positive rate among the 'asymptomatic infected individuals'
#38Earlier quoted context omitted.
>If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. Can you elaborate on this a little more...?
If a given test has a 1% chance of returning true, even when the actual result is false, then from a sample of say 1000 tests we would expect at least 10 trues, in addition to any actual true results. If the chance of having the disease in the general population is low (say 1 in a thousand for this example) then we would expect 11 true results in our thousand samples. Of which 91% are incorrect results - false positi…
So then you'd want to know if the cause of a false positive is random, or specific to the individual, right? If it's random, then how much would a retest change your certainty that an initial positive was a true positive?
ie, if someone was a false positive the first time would they still have a 1% chance of getting another false positive, or is it possible there's something about that individual that will always give them a positive result?
Re: Potential false-positive rate among the 'asymptomatic infected individuals'
#39Earlier quoted context omitted.
> I can't imagine an asymptomatic person would put stress on the hospital system? If s/he is in the hospital in quarantine you must give blankets and food, probably a nurse to check the temperature and symptoms two or three times per day, a medical doctor one a day just to be sure. Perhaps a blood analysis from time to time? Luckily you don't have to handle visitors because they are in quarantine. (Or there are some…
why would an asymptomatic person be in an hospital? Even people with minor symptoms are asked to self quarantine at home pretty much everywhere.
In China, asymptomatic with a positive test result still put you in their make-shift hospital facilities.
Re: Potential false-positive rate among the 'asymptomatic infected individuals'
#40One fallacy that seems universal with healthcare folks is they think the false positive rate is the chance that a given positive result is erroneous. If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. This is why prior probabilities need to be taken into account in making decisions.
This is just a form of the base rate fallacy, correct? https://en.wikipedia.org/wiki/Base_rate_fallacy
Yes, it’s a special case of Bayes’ Theorem.