4 ms·

Death with Covid is the baseline requirement (although, ideally, we look at death by Covid, even though that's methodologically difficult) for the above data to

by csee 5y ago

Death with Covid is the baseline requirement (although, ideally, we look at death by Covid, even though that's methodologically difficult) for the above data to have any meaning for the question of whether vaccines work. Suppose there's 0 Covid cases over the study period. Then this is exactly the result we expect to see. Approximately half the deaths being among the vaccinated, and half the deaths being among the unvaccinated. Because there's going to be heart attacks and so on in the two cohorts over the study window.

peteradio 5y ago
Sorry, I think I'm confused, it sounds like all-cause mortality is not the important number to be looking at in your opinion? The person you responded to reports more deaths in the vaccinated group. But you were querying about Covid deaths. Why would you ignore the larger all cause death and concern yourself only with Covid deaths? Suppose someone offered you a pill. They say with certainty that you can trust that it will prevent you from dying of Covid but increase your likelihood of dying of something else by so much that your overall likelihood of dying actually increases. Is that some pill you'd want to take? edit: I just finally got your point about a period with zero covid cases. Kind of makes sense, but how could you ever use that to make a claim about a vaccines effectiveness if there is known zero challenge?
- csee 5y ago
  If the difference was statistically significant, sure. But it isn't, is it? ~50% of the time, the vaccinated group will have more deaths by chance, even if the vaccine is safe - assuming either little to no community spread during the study, or a small study of young people. All this result shows is that the vaccine isn't effective at preventing car crashes, which is something we already knew.
  - beagle3 5y ago
    I urge you to actually read the report - https://www.fda.gov/media/151733/download https://www.fda.gov/media/151733/download on page 23 has the latest counts (21:17) and no exact list of causes, but an earlier version https://www.medrxiv.org/content/10.1101/2021.07.28.21261159v1.supplementary-material https://www.medrxiv.org/content/10.1101/2021.07.28.21261159v... which only lists 15:14 deaths, page 12 does list causes: There are 4 deaths from cardiac arrest in the vaccine arm, vs. 1 in the placebo; and 1 death from covid vs. 2 in the placebo. Taken altogether, especially with what we now know about myocarditis/pericarditis risk from the vaccine, non trivial input. And I don't know if you're aware, but the original (dec 2020) approval basically rested on 4:1 placebo:vaccine severe cases. It was approved based on statistically insignificant data, but somehow the same counts of death is not even interesting. Depending on your model, 21:17 may or may not be statistically significant. I didn't see any "official" modeling of that, especially given that "no deaths were attributed to the vaccine" - if your model says something can't happen, it obviously can't statistically happen ..... There is one "death" (which might as well be a car crash) out of the 29=(15+14) causes listed. All others are medical.
    - deleted 5y ago
      [deleted]
    - csee 5y ago
      It is not close to statistically significant. Here are two monte carlos under different assumptions which checks how often we expect 21 or more deaths by chance under the null hypothesis of no-effect on non-Covid mortality (i.e., that the vaccines are safe). ## >>> import numpy >>> import random >>> N_vaccine_deaths = [] >>> for j in range(500): ... x = [] ... x = [0 for i in range(46000)] + [1 for i in range(38)] # 38 deaths, ~46000 non-deaths ... random.shuffle(x) ... N_vaccine_deaths += [np.sum(x[0:int(len(x)/2)])] >>> N_vaccine_deaths = np.array(N_vaccine_deaths) >>> p = np.sum(N_vaccine_deaths >= 21)/len(N_vaccine_deaths ) >>> p 0.296 >>> x = [] >>> for i in range(5000): ... x += [sum([random.random() < (38/46000) for i in range(int(46000/2))])] >>> p = np.sum(np.array(x) >= 21)/len(x) >>> p 0.3446 Even just looking at cardiac deaths only and it turns out to be statistically insignificant with a p-value around 0.2. And this is actually p-hacking since we didn't prespecify and lock-in that hypothesis before the study began. > And I don't know if you're aware, but the original (dec 2020) approval basically rested on 4:1 placebo:vaccine severe cases. It was approved based on statistically insignificant data, but somehow the same counts of death is not even interesting. I don't see how this is relevant. That was a long time ago and we didn't have much data, hence low statistical power, and the need to make a decision with an incomplete picture. We now have much more data from over 20 countries, and are able to draw more conclusive and confident conclusions about efficacy.
      - beagle3 5y ago
        Simulation is much appreciated, even if I would use a different method (but I didn’t have the time, so I’ll accept yours) We do not have efficacy data from 20 countries. E.g. israeli data which I am intimately familiar with is incredibly biased to show vaccine efficiency. Official data touted based on observational data showed 95% efficiency, while at the same time airport data which is the only place with unbiased testing showed about 60%. (That data is biased by demographics, but not by sampling). As you said, there’s no statistically significant RCT data showing vaccines are dangerous, but also no RCT data showing they are effective in preventing severe disease. Statistical significance cuts both ways.
  - s1artibartfast 5y ago
    Like you said, it all depends on the statistics. If the car crashes in the treated group are high enough, you have to start asking if there is a mechanism where vaccination side effects are the cause. This is why you don't drive or operate heavily machinery with certain drugs.

by csee 5y ago

peteradio 5y ago

Sorry, I think I'm confused, it sounds like all-cause mortality is not the important number to be looking at in your opinion? The person you responded to reports more deaths in the vaccinated group. But you were querying about Covid deaths. Why would you ignore the larger all cause death and concern yourself only with Covid deaths? Suppose someone offered you a pill. They say with certainty that you can trust that it will prevent you from dying of Covid but increase your likelihood of dying of something else by so much that your overall likelihood of dying actually increases. Is that some pill you'd want to take? edit: I just finally got your point about a period with zero covid cases. Kind of makes sense, but how could you ever use that to make a claim about a vaccines effectiveness if there is known zero challenge?

csee 5y ago
If the difference was statistically significant, sure. But it isn't, is it? ~50% of the time, the vaccinated group will have more deaths by chance, even if the vaccine is safe - assuming either little to no community spread during the study, or a small study of young people. All this result shows is that the vaccine isn't effective at preventing car crashes, which is something we already knew.
- beagle3 5y ago
  I urge you to actually read the report - https://www.fda.gov/media/151733/download https://www.fda.gov/media/151733/download on page 23 has the latest counts (21:17) and no exact list of causes, but an earlier version https://www.medrxiv.org/content/10.1101/2021.07.28.21261159v1.supplementary-material https://www.medrxiv.org/content/10.1101/2021.07.28.21261159v... which only lists 15:14 deaths, page 12 does list causes: There are 4 deaths from cardiac arrest in the vaccine arm, vs. 1 in the placebo; and 1 death from covid vs. 2 in the placebo. Taken altogether, especially with what we now know about myocarditis/pericarditis risk from the vaccine, non trivial input. And I don't know if you're aware, but the original (dec 2020) approval basically rested on 4:1 placebo:vaccine severe cases. It was approved based on statistically insignificant data, but somehow the same counts of death is not even interesting. Depending on your model, 21:17 may or may not be statistically significant. I didn't see any "official" modeling of that, especially given that "no deaths were attributed to the vaccine" - if your model says something can't happen, it obviously can't statistically happen ..... There is one "death" (which might as well be a car crash) out of the 29=(15+14) causes listed. All others are medical.
  - deleted 5y ago
    [deleted]
  - csee 5y ago
    It is not close to statistically significant. Here are two monte carlos under different assumptions which checks how often we expect 21 or more deaths by chance under the null hypothesis of no-effect on non-Covid mortality (i.e., that the vaccines are safe). ## >>> import numpy >>> import random >>> N_vaccine_deaths = [] >>> for j in range(500): ... x = [] ... x = [0 for i in range(46000)] + [1 for i in range(38)] # 38 deaths, ~46000 non-deaths ... random.shuffle(x) ... N_vaccine_deaths += [np.sum(x[0:int(len(x)/2)])] >>> N_vaccine_deaths = np.array(N_vaccine_deaths) >>> p = np.sum(N_vaccine_deaths >= 21)/len(N_vaccine_deaths ) >>> p 0.296 >>> x = [] >>> for i in range(5000): ... x += [sum([random.random() < (38/46000) for i in range(int(46000/2))])] >>> p = np.sum(np.array(x) >= 21)/len(x) >>> p 0.3446 Even just looking at cardiac deaths only and it turns out to be statistically insignificant with a p-value around 0.2. And this is actually p-hacking since we didn't prespecify and lock-in that hypothesis before the study began. > And I don't know if you're aware, but the original (dec 2020) approval basically rested on 4:1 placebo:vaccine severe cases. It was approved based on statistically insignificant data, but somehow the same counts of death is not even interesting. I don't see how this is relevant. That was a long time ago and we didn't have much data, hence low statistical power, and the need to make a decision with an incomplete picture. We now have much more data from over 20 countries, and are able to draw more conclusive and confident conclusions about efficacy.
    - beagle3 5y ago
      Simulation is much appreciated, even if I would use a different method (but I didn’t have the time, so I’ll accept yours) We do not have efficacy data from 20 countries. E.g. israeli data which I am intimately familiar with is incredibly biased to show vaccine efficiency. Official data touted based on observational data showed 95% efficiency, while at the same time airport data which is the only place with unbiased testing showed about 60%. (That data is biased by demographics, but not by sampling). As you said, there’s no statistically significant RCT data showing vaccines are dangerous, but also no RCT data showing they are effective in preventing severe disease. Statistical significance cuts both ways.
- s1artibartfast 5y ago
  Like you said, it all depends on the statistics. If the car crashes in the treated group are high enough, you have to start asking if there is a mechanism where vaccination side effects are the cause. This is why you don't drive or operate heavily machinery with certain drugs.