> 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 ; 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/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/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