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> What I'm saying is that the vaccine surely reduces the number of people whose death is attributed to covid IIRC, you have provided no explanation for a high

by csee 5y ago

> What I'm saying is that the vaccine surely reduces the number of people whose death is attributed to covid IIRC, you have provided no explanation for a high VE against Covid-death post vaccination plateau. The only remaining explanation is that the vaccines are effective at preventing death from Covid. The point you are making here is a separate one which is that the vaccine is actively killing people, unrelated to Covid, and that this downside probably offsets the upside of VE against Covid-death for people below 60. > On the basis of the proof you gave above yourself. 24% more deaths within 6 months of the vaccine, for a group of (essentially) healthy 18-50 year olds. Your calculation showed that if it increases death by 24% in that group, you wouldn't see it. No, if there were more people that were dying in both groups, a 24% difference would be highly statistically significant. Given that so few people were dying in both groups, a 24% difference is not statistically significant. Imagine if there were 2 car crashes in a group of 30,000 and 1 car crash in another group of 30,000. That's a 100% difference. Massive, right? It should be statistically significant because it's 100%? No, it is indistinguishable from noise. An attempt to read signal into the 2 vs 1 car crashes here is not good statistics, despite a 100% difference in deaths. The only valid conclusion from the RCT is that the vaccines seem safe over the 6 month window. (Technically speaking, we have failed to reject the null of vaccine safety). > [US data] I guess, for this, I will need to really dig into the data in order to give your case a fair go. Let's focus on the Israel data for now. > [Israel data] Why cant [1] for 20-29 year old males just be noise? That bump is similar in magnitude to the 2017-2018 dip. What caused the 2017-2018 dip? [1] https://twitter.com/OS51388957/status/1485609530612400136/photo/1 https://twitter.com/OS51388957/status/1485609530612400136/ph...

beagle3 5y ago
> IIRC, you have provided no explanation for a high VE against Covid-death post vaccination plateau. The only remaining explanation is that the vaccines are effective at preventing death from Covid. Israeli data does not show a high VE as such, and in fact there is no vaccination plateau in many places thanks to boosting and stuff. I'm not familiar with the data in every country, if you want to bring a specific case, we can discuss. And no, that is not the only explanation - while death is never disputed, covid attribution often is. > No, if there were more people that were dying in both groups, a 24% difference would be highly statistically significant. Given that so few people were dying in both groups, a 24% difference is not statistically significant. I guess we have a misunderstanding. You have shown that if there was a 24% increase in death, it would not appear to be statistically significant. Ergo, this test (at n=44,000, with p_death=~1/1000) is not powerful enough to detect a 24% increase in death. In order to detect 24% increase in death, you would need a much larger n. Therefore, you cannot rule out a 24% increase in death based on the only RCT ever done. Your prior says it is therefore rejected. But again, that's your prior taking precedence, it is NOT the test powerful enough to show safety. > No, it is indistinguishable from noise. An attempt to read signal into the 2 vs 1 car crashes here is not good statistics, despite a 100% difference in deaths. Exactly. The RCT shows neither that the intervention is effective or safe. It doesn't show it is ineffective or unsafe. At the rate of 1/30K, it is underpowered, which is what I said. > The only valid conclusion from the RCT is that the vaccines seem safe over the 6 month window. (Technically speaking, we have failed to reject the null of vaccine safety). No, that is your prior. The only valid conclusion is that the test is not powerful enough to show 24% increase (or 40% increase) in death, even if it is there. You have a test, that, given n=44,000 and p_death=1/1000, will only show an increase in death if it tops 100% or so. > I guess, for this, I will need to really dig into the data in order to give your case a fair go. Let's focus on the Israel data for now. https://data.cdc.gov/d/9dzk-mvmi/visualization https://data.cdc.gov/d/9dzk-mvmi/visualization ; choose "start date" as dimension and "symptoms, signs .. not otherwise" (one before last) as measure. > Why cant [1] for 20-29 year old males just be noise? That bump is similar in magnitude to the 2017-2018 dip. What caused the 2017-2018 dip? Note these are cumulative graphs. the 2017-2018 dip was "repaid" later (some people call it "the dry tinder" hypotheses, a version of "reversion to the mean" - won't go into this), whereas 2021 isn't. It is different. The reason I do not believe it is noise is that I looked at the monthly data on the Israeli central bureau of statistics, and it concentrates in the months following the vaccine drive for that age group. I don't have the latest data in a handy form, but look at these graphs mid year: https://twitter.com/RanIsraeli/status/1403798079699144706/photo/1 https://twitter.com/RanIsraeli/status/1403798079699144706/ph... - title says "Mortality in Israel, ages 20-29, feb-01 to april-29, 2016-2021" (The reason apr-30 was excluded is because of an unrelated disaster that would have bumped 2021 higher by ~40 deaths[1]) https://twitter.com/RanIsraeli/status/1422674509535064070/photo/2 https://twitter.com/RanIsraeli/status/1422674509535064070/ph... - this also reflects june data. Israel did vaccination by age bands, with 16-29 mostly feb-apr. (But not in the order you'd expect - a big chunk of 18-22 went first through the army, then rest of 20-29, then rest of 16-19) > Why cant [1] for 20-29 year old males just be noise? It could. But ... that's your prior speaking, not the data. If your prior is not "vaccines are safe and effective", but rather "I don't know if they are safe or effective", the data can be read either way, and if your prior is "vaccines are unsafe and ineffective", you can also support it with the data. I try to always use the most uninformative prior. [1] https://en.wikipedia.org/wiki/2021_Meron_crowd_crush https://en.wikipedia.org/wiki/2021_Meron_crowd_crush
- csee 5y ago
  I have realized that the Fenton & Neil trick logically can't explain VE against death if we use, say, a 6 months or 1 year window to calculate it. The Fenton & Neil trick only has a non-trivial impact when looking at the instantaneous percentage. If you expand the sliding window to 1 year, the trick's impact asymptotes to zero and simply can not explain a high VE. Moreover, the way he's set up the example exaggerates the effect. He's assuming that 78% of the population gets vaccinated between weeks 6 and 11! Not even Israel is that quick. This is a moot point, anyway, given the above. Given Fenton & Neil can't explain it, what other explanation is there for a high VE against death, when that VE is calculated using 6-12 months of data? > Exactly. The RCT shows neither that the intervention is effective or safe. So you have moved me a bit on this. I now believe: - The RCT shows that the vaccine is highly effective at preventing infection itself in a previous strain. - The RCT can't possibly show that the vaccine is effective at preventing Covid mortality (due to lack of Covid circulation over study window). - The RCT can't possibly show that the vaccine is safe within the bounds of a 20-50% death rate increase. - The RCT shows that the vaccine isn't so dangerous as to be massively increasing the death rate (within the limitations of N=44,000 power). > Israel's 20-29 mortality Yes, it's interesting for sure. It could still be noise. You dismiss the 2017-2018 dip because it was "repaid". However there's no mechanism for repayment. The 2017-2018 data is i.i.d. barring some understanding of what's driving it (e.g. knowledge of a war or something). The existence of the repayment is really further evidence that the bump in 2021 could be noise. That dip & repayment both show that this level of random variance is to be expected in this data - we've seen it twice. BUT! Given that these are largely cardiac deaths, and we see it in 3 different age bands at the same time, and we know the mRNA vaccines cause myocarditis, I think you could be right about the interpretation. I just want to see it in other countries. If it's really driving mortality in this way, we should be able to find it elsewhere.
  - beagle3 5y ago
    I think we are in agreement about most things. Did you check the cdc link? Do you have a theory for the rising curve? Re:the “repayment” - every single person can be modeled I.I.d, but the event of death of each person is an increasing failure rate function. Someone who is in bad condition will (a) die at a significant challenge, be it flu, Covid, or other reasons; and (b) will only die once. In that sense there’s repayment - a weak flu season gives some people 6-12 until the next bad season, but usually not much more if a bad flu year would have killed them. This is known as “dry tinder” hypothesis in epidemiology. It’s not a law of nature, or a certainty, but has been observed in the past in some cases. Thank you very much for an enriching, respectful conversation. Really uncommon on this subject these days.

by csee 5y ago

beagle3 5y ago

> IIRC, you have provided no explanation for a high VE against Covid-death post vaccination plateau. The only remaining explanation is that the vaccines are effective at preventing death from Covid. Israeli data does not show a high VE as such, and in fact there is no vaccination plateau in many places thanks to boosting and stuff. I'm not familiar with the data in every country, if you want to bring a specific case, we can discuss. And no, that is not the only explanation - while death is never disputed, covid attribution often is. > No, if there were more people that were dying in both groups, a 24% difference would be highly statistically significant. Given that so few people were dying in both groups, a 24% difference is not statistically significant. I guess we have a misunderstanding. You have shown that if there was a 24% increase in death, it would not appear to be statistically significant. Ergo, this test (at n=44,000, with p_death=~1/1000) is not powerful enough to detect a 24% increase in death. In order to detect 24% increase in death, you would need a much larger n. Therefore, you cannot rule out a 24% increase in death based on the only RCT ever done. Your prior says it is therefore rejected. But again, that's your prior taking precedence, it is NOT the test powerful enough to show safety. > No, it is indistinguishable from noise. An attempt to read signal into the 2 vs 1 car crashes here is not good statistics, despite a 100% difference in deaths. Exactly. The RCT shows neither that the intervention is effective or safe. It doesn't show it is ineffective or unsafe. At the rate of 1/30K, it is underpowered, which is what I said. > The only valid conclusion from the RCT is that the vaccines seem safe over the 6 month window. (Technically speaking, we have failed to reject the null of vaccine safety). No, that is your prior. The only valid conclusion is that the test is not powerful enough to show 24% increase (or 40% increase) in death, even if it is there. You have a test, that, given n=44,000 and p_death=1/1000, will only show an increase in death if it tops 100% or so. > I guess, for this, I will need to really dig into the data in order to give your case a fair go. Let's focus on the Israel data for now. https://data.cdc.gov/d/9dzk-mvmi/visualization https://data.cdc.gov/d/9dzk-mvmi/visualization ; choose "start date" as dimension and "symptoms, signs .. not otherwise" (one before last) as measure. > Why cant [1] for 20-29 year old males just be noise? That bump is similar in magnitude to the 2017-2018 dip. What caused the 2017-2018 dip? Note these are cumulative graphs. the 2017-2018 dip was "repaid" later (some people call it "the dry tinder" hypotheses, a version of "reversion to the mean" - won't go into this), whereas 2021 isn't. It is different. The reason I do not believe it is noise is that I looked at the monthly data on the Israeli central bureau of statistics, and it concentrates in the months following the vaccine drive for that age group. I don't have the latest data in a handy form, but look at these graphs mid year: https://twitter.com/RanIsraeli/status/1403798079699144706/photo/1 https://twitter.com/RanIsraeli/status/1403798079699144706/ph... - title says "Mortality in Israel, ages 20-29, feb-01 to april-29, 2016-2021" (The reason apr-30 was excluded is because of an unrelated disaster that would have bumped 2021 higher by ~40 deaths[1]) https://twitter.com/RanIsraeli/status/1422674509535064070/photo/2 https://twitter.com/RanIsraeli/status/1422674509535064070/ph... - this also reflects june data. Israel did vaccination by age bands, with 16-29 mostly feb-apr. (But not in the order you'd expect - a big chunk of 18-22 went first through the army, then rest of 20-29, then rest of 16-19) > Why cant [1] for 20-29 year old males just be noise? It could. But ... that's your prior speaking, not the data. If your prior is not "vaccines are safe and effective", but rather "I don't know if they are safe or effective", the data can be read either way, and if your prior is "vaccines are unsafe and ineffective", you can also support it with the data. I try to always use the most uninformative prior. [1] https://en.wikipedia.org/wiki/2021_Meron_crowd_crush https://en.wikipedia.org/wiki/2021_Meron_crowd_crush

csee 5y ago
I have realized that the Fenton & Neil trick logically can't explain VE against death if we use, say, a 6 months or 1 year window to calculate it. The Fenton & Neil trick only has a non-trivial impact when looking at the instantaneous percentage. If you expand the sliding window to 1 year, the trick's impact asymptotes to zero and simply can not explain a high VE. Moreover, the way he's set up the example exaggerates the effect. He's assuming that 78% of the population gets vaccinated between weeks 6 and 11! Not even Israel is that quick. This is a moot point, anyway, given the above. Given Fenton & Neil can't explain it, what other explanation is there for a high VE against death, when that VE is calculated using 6-12 months of data? > Exactly. The RCT shows neither that the intervention is effective or safe. So you have moved me a bit on this. I now believe: - The RCT shows that the vaccine is highly effective at preventing infection itself in a previous strain. - The RCT can't possibly show that the vaccine is effective at preventing Covid mortality (due to lack of Covid circulation over study window). - The RCT can't possibly show that the vaccine is safe within the bounds of a 20-50% death rate increase. - The RCT shows that the vaccine isn't so dangerous as to be massively increasing the death rate (within the limitations of N=44,000 power). > Israel's 20-29 mortality Yes, it's interesting for sure. It could still be noise. You dismiss the 2017-2018 dip because it was "repaid". However there's no mechanism for repayment. The 2017-2018 data is i.i.d. barring some understanding of what's driving it (e.g. knowledge of a war or something). The existence of the repayment is really further evidence that the bump in 2021 could be noise. That dip & repayment both show that this level of random variance is to be expected in this data - we've seen it twice. BUT! Given that these are largely cardiac deaths, and we see it in 3 different age bands at the same time, and we know the mRNA vaccines cause myocarditis, I think you could be right about the interpretation. I just want to see it in other countries. If it's really driving mortality in this way, we should be able to find it elsewhere.
- beagle3 5y ago
  I think we are in agreement about most things. Did you check the cdc link? Do you have a theory for the rising curve? Re:the “repayment” - every single person can be modeled I.I.d, but the event of death of each person is an increasing failure rate function. Someone who is in bad condition will (a) die at a significant challenge, be it flu, Covid, or other reasons; and (b) will only die once. In that sense there’s repayment - a weak flu season gives some people 6-12 until the next bad season, but usually not much more if a bad flu year would have killed them. This is known as “dry tinder” hypothesis in epidemiology. It’s not a law of nature, or a certainty, but has been observed in the past in some cases. Thank you very much for an enriching, respectful conversation. Really uncommon on this subject these days.