4 ms·

Anyone worth arguing with acknowledges the limitations of the PCR testing. You are essentially suggesting that we ignore the limitations and biases of these ser

Anyone worth arguing with acknowledges the limitations of the PCR testing. You are essentially suggesting that we ignore the limitations and biases of these serological surveys. If a handful of internet denizens can quickly point out methodological flaws, maybe the researches should try to address or account for those. I fully understand the urgency of the matter, and that urgency can lead to mistakes. But these studies are going to be used in policy making and could potentially impact millions of lives. The difference between 1% and 0.5% is huge, that is over 1.5 million in the US. And powerful people are trying to use these studies to make a case for re-opening the economy. I think it is completely reasonable to push back on these flaws and biases in light of that.

timr 7y ago
"You are essentially suggesting that we ignore the limitations and biases of these serological surveys." I am not. I am suggesting that you are preferring a rate based on an even more biased method, because the method yields a bigger number. "If a handful of internet denizens can quickly point out methodological flaws, maybe the researches should try to address or account for those." If a handful of internet denizens can quickly point out "methodological flaws", it usually means that the "methodological flaws" they have discovered are well-known and accounted for. In this particular case, sample bias is not a new statistical phenomenon, and we've been doing serological surveys for a long time.
- qqqwerty 7y ago
  > I am suggesting that you are preferring a rate based on an even more biased method, because the method yields a bigger number. What method? I merely stated that PCR has limitations, I never said I was using it to calculate IFR. For the record, my assumptions are that IFR is around 1%. That is largely based on the Diamond Princess data. The original paper suggested it was 0.5%[1], but at the time there were only 7 fatalities. The current number is 13. The raw IFR is now up to 1.8%. Crudely adjusting for demographics based on the ratio in the paper puts the IFR around 1%. This is the only population that was both comprehensively tested and occurred long enough ago for most of the cases to resolve (although some cases are still active/critical[2]). And I fully acknowledge that this approach has limitations. It is a small population, it happened earlier in the crisis when we knew less about treatment, etc... But I sill think it is the best data point we have. > If a handful of internet denizens can quickly point out "methodological flaws", it usually means that the "methodological flaws" they have discovered are well-known and accounted for. Except the Santa Clara study did not do that. They acknowledged potential bias, but did nothing to adjust for it. From the source: "Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain." The LA and NY papers have not been published yet. [1] https://cmmid.github.io/topics/covid19/diamond_cruise_cfr_estimates.html https://cmmid.github.io/topics/covid19/diamond_cruise_cfr_es... [2] https://www.worldometers.info/coronavirus/ https://www.worldometers.info/coronavirus/

timr 7y ago

"You are essentially suggesting that we ignore the limitations and biases of these serological surveys." I am not. I am suggesting that you are preferring a rate based on an even more biased method, because the method yields a bigger number. "If a handful of internet denizens can quickly point out methodological flaws, maybe the researches should try to address or account for those." If a handful of internet denizens can quickly point out "methodological flaws", it usually means that the "methodological flaws" they have discovered are well-known and accounted for. In this particular case, sample bias is not a new statistical phenomenon, and we've been doing serological surveys for a long time.

qqqwerty 7y ago
> I am suggesting that you are preferring a rate based on an even more biased method, because the method yields a bigger number. What method? I merely stated that PCR has limitations, I never said I was using it to calculate IFR. For the record, my assumptions are that IFR is around 1%. That is largely based on the Diamond Princess data. The original paper suggested it was 0.5%[1], but at the time there were only 7 fatalities. The current number is 13. The raw IFR is now up to 1.8%. Crudely adjusting for demographics based on the ratio in the paper puts the IFR around 1%. This is the only population that was both comprehensively tested and occurred long enough ago for most of the cases to resolve (although some cases are still active/critical[2]). And I fully acknowledge that this approach has limitations. It is a small population, it happened earlier in the crisis when we knew less about treatment, etc... But I sill think it is the best data point we have. > If a handful of internet denizens can quickly point out "methodological flaws", it usually means that the "methodological flaws" they have discovered are well-known and accounted for. Except the Santa Clara study did not do that. They acknowledged potential bias, but did nothing to adjust for it. From the source: "Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain." The LA and NY papers have not been published yet. [1] https://cmmid.github.io/topics/covid19/diamond_cruise_cfr_estimates.html https://cmmid.github.io/topics/covid19/diamond_cruise_cfr_es... [2] https://www.worldometers.info/coronavirus/ https://www.worldometers.info/coronavirus/