I think you were labouring over the wrong denominator*.
As I understand Steve Kirsch:
Let’s have the observation window end at t = 0.
Then, let Bill receive an intervention at t = -v (the event).
Bill’s death occurs at t = -d (with d necessarily <= v).
Then Bill’s daisy-pushing ratio is d/v.
We could base our analysis upon the daisy-pushing ratio, but Steve, and everyone else, has used the breathing ratio, r = 1 – d/v.
For a cohort of Bills, in the absence of a correlation between the intervention and death (hence between v and d), the expectation of r is 0.5, providing deaths are not otherwise skewed.
To remove any skew, how about we find the cohort of people who have never receive the intervention, and remained alive at the time Bill received his? We can calculate the average breathing time, for them (being the average of (v – d’), where -d’ denotes each such individual’s time of death). Then, instead of analysing Bill’s breathing ratio, r, we can look at his effect ratio, f = (v – d)/Avg(v – d’). Proceeding on this basis, the average of f, for all Bills, will be 1, when the intervention has no effect, less than 1, where the intervention appears to truncate life, and greater, where it appears to extend life.
But, back to r. r < 0.5 suggests that the intervention is life-shortening.
The “boosting” problem is genuine. If Bill is absolutely determined to receive a booster every ten days, say, then there was never any possibility that his death would be more than ten days after his most recent booster. This makes it all but inevitable that the average of r, for the intervention group, will be less than 0.5 We could try to move forward by analysing an effect ratio, as above, but for which the non-intervention cohort consisted of people who died within that ten-day period. However, we simply don’t know the scheduled dates of each Bill’s first, postmortem, booster. Possibly, we can assume a minimum boosting interval (which may well be the time imposed between dose #1 and dose #2, of the original “fully vaccinated” programme), and construct each Bill’s comparison cohort based upon it.
After Steve switched to taking dose #1 as the intervention, and accepting his dismissal of skew, there is no flaw in his logic exposed by the discussion, thus far.
However, suppose the intervention has material effect in blocking infection. This will manifest itself in a reduction of infection-ascribable deaths. A consequence of this is that deaths in the intervention group will comprise disproportionately more, with infection as cause, shortly after the intervention (where the infection occurred just prior to the intervention), than longer after the intervention. This, of course, would move r below 0.5, just as Steve observed it to be, and, indeed, would move f below 1.
Thus, Steve’s finding is consistent with a 100% safe vaccine which has material efficacy against infection. Which is, undoubtedly, exactly what is being sold to us!!!
* Your denominator was the full, two-year, observation window.
Thank you for this challenge - I found it compelling.
Having thought about it all day I think you may have goofed after all.
To make the problem simpler we can assume that everyone was vaccinated after one year. If deaths occur evenly as in your hypothesis, at the point of vaccination, half of the deaths will already have occurred. Being vaccinated is therefore a marker of being alive at a later date.
If we took your square diagram and instead of having random events made all the events line up at the half way point then you can see that the denominator for the vaccinated should not include those deaths that have already occurred. Instead a new square can be drawn that includes only the top left hand corner of your original square. From this mini square the ratio of life lived to potential life lived would still be 0.5 for that subgroup.
There is a bias created, however. It is a bias in the unvaccinated. For them the first year passes with half of the cohort dying. At the point of vaccination a large proportion of the cohort moves into the vaccinated category. The remaining unvaccinated cohort go on to have deaths in the second year but these will be far fewer than those that occurred in the first year because there are fewer people who remain unvaccinated for the second year. Therefore, the deaths in the unvaccinated cohort will be disproportionately in the first year creating an artificially low ratio.
I think the methodology is sound but that does not mean there is no room for there being a bias in the data. The bias that concerns me is that it is possible that a proportion of Steve's followers had relatives who did die coinicidentally after vaccination. Because that subgroup might be over-represented there may be a bias that reduces the ratio for the vaccinated. Indeed, I do not think the ratio could fall as low as 0.3 without there being some such bias present.
P.S. I'm looking forward to your part 3 on the worry window. It has been over 18 months of not having had that hypothesis challenged and it needs to be.
Have you thought about having a debate about this with Steve Kirsch? It will be interesting to see Norman Fentons take on this? If there is anything wrong with this survey HE will definitely find it. However it turns out, I think we should all be thankful that at least we can talk about it? We need to find the truth for the future, or as near as we can get anyway! You ALL need to pat yourselves on the back for trying, under impossible circumstances, to make sense of all this!
Jan 31, 2023·edited Jan 31, 2023Liked by Brian Mowrey
I saw Steve's post this morning(and the original for the survey back whenever) and wasn't sure what I didn't like about it. I read your post and still didn't get it. I write this here because it is quieter and more friendly to skeptical opinion. BTW I like Kirsch and the way he is trying to fight this is not meant as a shot at him.
One of the best tricks to understanding a math problem is to game it out for a specific case. I will draw out Steve's survey for one data point.
So, let's imagine that the total period of the study is 100 days, I think it is actually from Jan 1 2021 to the date he started collecting data but replacing things with powers of ten let's us look at statistics without getting bogged down in arithmetic. So, we take our case and have him die on day 10. He lived 10 out of a possible 100 days for a value of .1
Now, let's take the same guy and vaccinate him on day 5. He still dies on the same day, day 10 of the study which is 5 days after vax. From Vday to end of study is 95 days. He lived 5 days after vaccination out of a possible 95 days for a value of .052. Died on the same day but because the numerator is smaller, shrinking it and the denominator both by 5 days has a bigger effect on the numerator.
The vaccine would actually have to extend his life by 4.5 days to 9.5 days post Vax to get the same .1 result.
If I understand the study right, then that is my analysis. I'm not sure I understand Steve's method though. If this helps anyone collect the prize please contribute to Feeding Jon's Kids a wonderful charity that I will be starting soon to help feed impoverished children who live in my house so that I can use the money that is currently feeding them to buy better booze, I mean for some noblish purpose.
Jan 31, 2023·edited Jan 31, 2023Liked by Brian Mowrey
Is this just Steve “jumping the gun” again?
I like that he brings these things up and he provides some interesting points of view but many on closer inspection turn out be 🤷
I couldn’t really follow his methodology at a glance, sounded good but when you start taking ratios at different points in time and taking averages etc. it’s a good way to mess things up if your not careful. Not saying he has, but if what you've written is a fair assessment of his method, I can totally see the issue. I can clearly follow your example but find it hard to relate it directly to Steve’s analysis mainly because I can’t follow Steve’s logic fully (could be lack of trying on my part).
I don’t really want to spend the time unraveling his post. I’m sure others like yourself did/will do a better job at it. I hope Norman Fenton or Clare Craig will do a deep dive after being name dropped. Even if they agree with his methodology they would be able to show why his critics are wrong.
I look forward to more skeptical discussion of his findings, not because I disagree with him, I hope he has found a silver bullet but it has to stand up better scrutiny than just “put up or shut up.”
Jan 31, 2023·edited Jan 31, 2023Liked by Brian Mowrey
You can also prove this wrong a different way: if you assume a constant probability p of death each day, the resulting distribution of deaths is not uniform, because, with the exception of Damar Hamlin, people generally only die once and then stop. So, deaths will be weighted more strongly towards the start. The odds of dying on the first day is p, but of dying the nth day would be ((1-p)^(n-1))*p. The other problem as you also point out is that death rates are not uniform over the year (and there could be confounding correlation between time of vax and overall death rates)
Is this Groundhog’s Day? Didn’t we go through this circus act like a year ago? With the same incredibly small sample size with self-reported results submitted by an audience that is definitely not a random distribution? Is this what handwaving looks like?
I’d probably reflexively puke in my mouth less if it wasn’t presented like a “get rich quick” or “miracle cure” scam website. COMPLETE with BOLDED TEXT and APPEALS TO AUTHORITY to demonstrate that IT IS IMPOSSIBLE I am wrong! And if I am, you CAN WIN BEN STEIN’S MONEY!!!
Maybe I’m just cranky, but I’m past the point where these things are just a cute character quirk, and their heart is in the right place, yada yada. If this is what the being anti-vax means, I don’t even care which side wins. You’re just exchanging one group of egotistical sociopaths for another (which is on brand for humanity, give a cursory review of history).
Jan 31, 2023·edited Jan 31, 2023Liked by Brian Mowrey
So if I were to get the analogy and the setup correct, it appears that the proof was already constructed in such a way that the inclusion of any data point would inherently bias the results in favor of shortened life?
I tried reading through the first portion (Edit: of Steve's post) and to be honest I'm caught off-guard a bit by the early comments which appear more to prop up the supposed data.
The use of his own survey should raise serious criticisms as well. I think I've heard this comment before, but I think there's a saying that "the people who answer surveys usually aren't the people you want answering surveys", so I'd be curious how you control for such biases. The causes of death (and key symptoms) included in the survey, I feel, are also inherently biased in favor of arguing a relationship between a relative's death and the vaccines. I certainly wouldn't list vaccines with (the "cure"). Again, that's already biasing results. I haven't produced my own surveys, but I find that the survey is sprinkled with far too many *wink, wink; nudge, nudge* towards the vaccines.
The only you will get Kirsch to admit he’s wrong is to apply Captain Kirk wits to the Kobayashi Maru dilemma- you would need someone to trick him into admitting he’s made a mistake.
On that discussion we had about the message that all vaccines warrant further investigation- Andrew Bostom I think did a good job of cracking open the door with this post about the ineffective flu vaccines.
Was your main criticism similar to this comment by Prof Norman Fenton?
https://stevekirsch.substack.com/p/game-over-medicare-data-shows-the/comment/13112807
Alternate link in case the above doesn't load: https://archive.is/smwPc
I think you were labouring over the wrong denominator*.
As I understand Steve Kirsch:
Let’s have the observation window end at t = 0.
Then, let Bill receive an intervention at t = -v (the event).
Bill’s death occurs at t = -d (with d necessarily <= v).
Then Bill’s daisy-pushing ratio is d/v.
We could base our analysis upon the daisy-pushing ratio, but Steve, and everyone else, has used the breathing ratio, r = 1 – d/v.
For a cohort of Bills, in the absence of a correlation between the intervention and death (hence between v and d), the expectation of r is 0.5, providing deaths are not otherwise skewed.
To remove any skew, how about we find the cohort of people who have never receive the intervention, and remained alive at the time Bill received his? We can calculate the average breathing time, for them (being the average of (v – d’), where -d’ denotes each such individual’s time of death). Then, instead of analysing Bill’s breathing ratio, r, we can look at his effect ratio, f = (v – d)/Avg(v – d’). Proceeding on this basis, the average of f, for all Bills, will be 1, when the intervention has no effect, less than 1, where the intervention appears to truncate life, and greater, where it appears to extend life.
But, back to r. r < 0.5 suggests that the intervention is life-shortening.
The “boosting” problem is genuine. If Bill is absolutely determined to receive a booster every ten days, say, then there was never any possibility that his death would be more than ten days after his most recent booster. This makes it all but inevitable that the average of r, for the intervention group, will be less than 0.5 We could try to move forward by analysing an effect ratio, as above, but for which the non-intervention cohort consisted of people who died within that ten-day period. However, we simply don’t know the scheduled dates of each Bill’s first, postmortem, booster. Possibly, we can assume a minimum boosting interval (which may well be the time imposed between dose #1 and dose #2, of the original “fully vaccinated” programme), and construct each Bill’s comparison cohort based upon it.
After Steve switched to taking dose #1 as the intervention, and accepting his dismissal of skew, there is no flaw in his logic exposed by the discussion, thus far.
However, suppose the intervention has material effect in blocking infection. This will manifest itself in a reduction of infection-ascribable deaths. A consequence of this is that deaths in the intervention group will comprise disproportionately more, with infection as cause, shortly after the intervention (where the infection occurred just prior to the intervention), than longer after the intervention. This, of course, would move r below 0.5, just as Steve observed it to be, and, indeed, would move f below 1.
Thus, Steve’s finding is consistent with a 100% safe vaccine which has material efficacy against infection. Which is, undoubtedly, exactly what is being sold to us!!!
* Your denominator was the full, two-year, observation window.
Brian, there were a number of errors in the article which are now being fixed. Check back in a few days.
Oh Brian, Brian, Brian...you're such a party pooper. 😄
Thank you for this challenge - I found it compelling.
Having thought about it all day I think you may have goofed after all.
To make the problem simpler we can assume that everyone was vaccinated after one year. If deaths occur evenly as in your hypothesis, at the point of vaccination, half of the deaths will already have occurred. Being vaccinated is therefore a marker of being alive at a later date.
If we took your square diagram and instead of having random events made all the events line up at the half way point then you can see that the denominator for the vaccinated should not include those deaths that have already occurred. Instead a new square can be drawn that includes only the top left hand corner of your original square. From this mini square the ratio of life lived to potential life lived would still be 0.5 for that subgroup.
There is a bias created, however. It is a bias in the unvaccinated. For them the first year passes with half of the cohort dying. At the point of vaccination a large proportion of the cohort moves into the vaccinated category. The remaining unvaccinated cohort go on to have deaths in the second year but these will be far fewer than those that occurred in the first year because there are fewer people who remain unvaccinated for the second year. Therefore, the deaths in the unvaccinated cohort will be disproportionately in the first year creating an artificially low ratio.
I think the methodology is sound but that does not mean there is no room for there being a bias in the data. The bias that concerns me is that it is possible that a proportion of Steve's followers had relatives who did die coinicidentally after vaccination. Because that subgroup might be over-represented there may be a bias that reduces the ratio for the vaccinated. Indeed, I do not think the ratio could fall as low as 0.3 without there being some such bias present.
P.S. I'm looking forward to your part 3 on the worry window. It has been over 18 months of not having had that hypothesis challenged and it needs to be.
I don't think you goofed here. You should be able to write a simple python script and illustrate this.
I really hope Steve gets to hear about your criticism in time.
Have you thought about having a debate about this with Steve Kirsch? It will be interesting to see Norman Fentons take on this? If there is anything wrong with this survey HE will definitely find it. However it turns out, I think we should all be thankful that at least we can talk about it? We need to find the truth for the future, or as near as we can get anyway! You ALL need to pat yourselves on the back for trying, under impossible circumstances, to make sense of all this!
By reading this it seems you do like to play with fire.
I saw Steve's post this morning(and the original for the survey back whenever) and wasn't sure what I didn't like about it. I read your post and still didn't get it. I write this here because it is quieter and more friendly to skeptical opinion. BTW I like Kirsch and the way he is trying to fight this is not meant as a shot at him.
One of the best tricks to understanding a math problem is to game it out for a specific case. I will draw out Steve's survey for one data point.
So, let's imagine that the total period of the study is 100 days, I think it is actually from Jan 1 2021 to the date he started collecting data but replacing things with powers of ten let's us look at statistics without getting bogged down in arithmetic. So, we take our case and have him die on day 10. He lived 10 out of a possible 100 days for a value of .1
Now, let's take the same guy and vaccinate him on day 5. He still dies on the same day, day 10 of the study which is 5 days after vax. From Vday to end of study is 95 days. He lived 5 days after vaccination out of a possible 95 days for a value of .052. Died on the same day but because the numerator is smaller, shrinking it and the denominator both by 5 days has a bigger effect on the numerator.
The vaccine would actually have to extend his life by 4.5 days to 9.5 days post Vax to get the same .1 result.
If I understand the study right, then that is my analysis. I'm not sure I understand Steve's method though. If this helps anyone collect the prize please contribute to Feeding Jon's Kids a wonderful charity that I will be starting soon to help feed impoverished children who live in my house so that I can use the money that is currently feeding them to buy better booze, I mean for some noblish purpose.
Is this just Steve “jumping the gun” again?
I like that he brings these things up and he provides some interesting points of view but many on closer inspection turn out be 🤷
I couldn’t really follow his methodology at a glance, sounded good but when you start taking ratios at different points in time and taking averages etc. it’s a good way to mess things up if your not careful. Not saying he has, but if what you've written is a fair assessment of his method, I can totally see the issue. I can clearly follow your example but find it hard to relate it directly to Steve’s analysis mainly because I can’t follow Steve’s logic fully (could be lack of trying on my part).
I don’t really want to spend the time unraveling his post. I’m sure others like yourself did/will do a better job at it. I hope Norman Fenton or Clare Craig will do a deep dive after being name dropped. Even if they agree with his methodology they would be able to show why his critics are wrong.
I look forward to more skeptical discussion of his findings, not because I disagree with him, I hope he has found a silver bullet but it has to stand up better scrutiny than just “put up or shut up.”
You can also prove this wrong a different way: if you assume a constant probability p of death each day, the resulting distribution of deaths is not uniform, because, with the exception of Damar Hamlin, people generally only die once and then stop. So, deaths will be weighted more strongly towards the start. The odds of dying on the first day is p, but of dying the nth day would be ((1-p)^(n-1))*p. The other problem as you also point out is that death rates are not uniform over the year (and there could be confounding correlation between time of vax and overall death rates)
Welcome Brian,
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Is this Groundhog’s Day? Didn’t we go through this circus act like a year ago? With the same incredibly small sample size with self-reported results submitted by an audience that is definitely not a random distribution? Is this what handwaving looks like?
I’d probably reflexively puke in my mouth less if it wasn’t presented like a “get rich quick” or “miracle cure” scam website. COMPLETE with BOLDED TEXT and APPEALS TO AUTHORITY to demonstrate that IT IS IMPOSSIBLE I am wrong! And if I am, you CAN WIN BEN STEIN’S MONEY!!!
Maybe I’m just cranky, but I’m past the point where these things are just a cute character quirk, and their heart is in the right place, yada yada. If this is what the being anti-vax means, I don’t even care which side wins. You’re just exchanging one group of egotistical sociopaths for another (which is on brand for humanity, give a cursory review of history).
So if I were to get the analogy and the setup correct, it appears that the proof was already constructed in such a way that the inclusion of any data point would inherently bias the results in favor of shortened life?
I tried reading through the first portion (Edit: of Steve's post) and to be honest I'm caught off-guard a bit by the early comments which appear more to prop up the supposed data.
The use of his own survey should raise serious criticisms as well. I think I've heard this comment before, but I think there's a saying that "the people who answer surveys usually aren't the people you want answering surveys", so I'd be curious how you control for such biases. The causes of death (and key symptoms) included in the survey, I feel, are also inherently biased in favor of arguing a relationship between a relative's death and the vaccines. I certainly wouldn't list vaccines with (the "cure"). Again, that's already biasing results. I haven't produced my own surveys, but I find that the survey is sprinkled with far too many *wink, wink; nudge, nudge* towards the vaccines.
The only you will get Kirsch to admit he’s wrong is to apply Captain Kirk wits to the Kobayashi Maru dilemma- you would need someone to trick him into admitting he’s made a mistake.
On that discussion we had about the message that all vaccines warrant further investigation- Andrew Bostom I think did a good job of cracking open the door with this post about the ineffective flu vaccines.
https://twitter.com/andrewbostom/status/1619038258209619979
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