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# Solution to the Steve Kirsch Survey Proof

### As far as I can tell, it is a Kobayashi Maru exercise for the vaccine.

**edit, February 1: Kirsch has now scrapped the “proof” and suspended the corresponding bet offer based on a limited, or should I say, modified acceptance of my solution. More details in the “update” section below.*

And so welcome back, readers, to *Unglossed*, the anti-Covid-vaccine blog that enjoys double-checking the work of other anti-Covid-vaccine blogs, in the spirit of providing the reader an accurate picture of reality, regardless of whatever narrative it supports, despite having *just* declared that exact endeavor impossible.

Today, Steve Kirsch has published an alleged proof that Covid vaccines shortened life-spans:

I won’t reproduce the puzzle here. I am merely publishing what I *believe* is a simple proof that the proof is not valid.

*Thus, the solution below is covered under spoiler alert.*

It was actually fun to work out the problem in Steve’s proof. So if the reader wants to *crack the puzzle for themselves*, then it goes without saying that they should not look at the solution here. Rather, go read Kirsch’s post.

(Also the solution is technically obvious, and had already been pointed out in comments in Kirsch’s post — but it is only obvious as words. Akin to how it’s “obvious” that if a car’s average speed through a tunnel is speeding, it must have been speeding *at some point*; but proving it still requires thought.)

## Solution: Vaccine Kobayashi Maru

The Kobayashi Maru, if any readers are unfamiliar, is a fictional training simulation in *Star Trek*. The *problem* with the Kobayashi Maru is that it is absolutely unwinnable, much to the chagrin of Kirstie Alley’s character in the second movie (RIP).

How we deal withdeath, is at least as important as how we deal withlife, wouldn’t you say?

With the tunnel problem in mind, I realized at last that demonstrating / proving the solution which seemed obvious in words merely required showing that the design of Kirsch’s “proof” was a Kobayashi Maru.

Therefore, assume that deaths are distributed perfectly evenly through time, and all die-ers are observed from a uniform starting date.

Kirsch’s design would compute the ratio of time alive to observed time, which in the model below would be the volume of the yellow area divided by the volume of yellow and purple together. It would be exactly half the rectangle, i.e. .5:

This is essentially how the unvaccinated die-ers are measured. The result is not .5 because the distribution is not perfectly even for the unvaccinated. This doesn’t matter for our model.

Next, assume any event is being observed:

In this case, Kirsch is measuring the date of last Covid vaccine injection. Note that this will exacerbate the effect as the late die-ers (top) would be “re-upping” their injections with boosters as they went along the year not dying.

This exacerbation issue isn’t important for our proof, because all that matters is that no matter what event is measured or when, it is completely impossible to return a result equal to or higher than .5:

Thus, it is shown that the design creates a no-win situation for the vaccine if an even distribution of deaths is assumed. Thus, no result can be meaningful or revealing of anything besides perhaps how frequently people tended to “re-up” with boosters before death.

But *is* the distribution even? In fact, for the vaccinated, deaths are lopsided toward the middle of the observation period, which would reflect the winter 2021/22 BA.1 and other respiratory illness surge. However, as best as I could calculate (which obviously was done before launching into a proof using an even distribution), the no-event alive/observed ratio for the vaccinated is .58. Therefore, the proof above should be adequate and not require a simulation using a different distribution:

*(Note that the original version of this post reported the rate as .5, as I still had that cell set to report the average for the column.)*

.58, in other words, seems to be the result for the vaccinated if they are measured the same way as the unvaccinated, from the beginning of the observation window to the end (which results in .55 - **edit: .55 was the value obtained due to an error in the airtable function for days alive since January 1, 2021. I had already realized this was the case for the vaccinated, hence my use of a virtual days alive value based on mid-month days, but had not yet confirmed it for the unvaccinated. Kirsch has since also noticed the error and fixed the corresponding function. The correct unvaccinated days alive / observed ratio is .42.*).

Moreover, a more apt comparison would be between the Kobayashi-Maru-ed vaccinated group and the unvaccinated who are individually “tagged” on the same exposure date as the vaccinated. Lots of studies work this way, finding a demographically matched control and watching them *from the same day* as the vaccinated subject.

Anyhow, that’s my proof I think, or maybe *I* goofed this one!

## Update on February 1, 2023

Kirsch subsequently acknowledged the partial problem of “re-up-ing” with boosters, but not the overall veracity of my “any event” proof. To quote the current wording of his position:

So I could have picked vax #1 date, vax #2 date, vax #3 day, etc. date for an individual (which would vary for each person), an arbitrary day of the year (assuming nothing external is happening like a Delta surge), date of vax availability, etc. You just can’t pick a start time that has implicit knowledge of the date that the person died (such as “your last vaccination before you died”). I apologize for the error.

Note that Kirsch credits the “discovery” of the acknowledged problems that led to these revisions to Claire Craig, although they are mentioned explicitly in my post (even if my post doesn’t rest its case on the acknowledged problems). However, my post predates the same so-called “discovery” of the re-up-ing problem, even if it does not *emphasize* / rest its case on the “re-up-ing” problem. Therefore, it eludes me how the following sentence on Kirsch’s post can be considered fully transparent:

It would seem more accurate to say that the bet offer has been suspended because I discovered and posted the risk factors in question, both in Kirsch’s comments and here, in the spirit of providing value to him and to my readers. Kirsch’s disagreement that my proof applies to any event does not change who pointed out that it is even “worse” for a relative, “most recent” event.

Moving on.

Kirsch is certainly free not to *accept* my proof, 100% — but, this refusal does not constitute a refutation of my proof. As far as I can ascertain, he has *not* demonstrated that for some reason *only* death-relative events, not any events, cause the no-win scenario my model proves. How could he? Therefore (as far as I can ascertain), my model and proof remain unimpeached. As an easy demonstration, observe that the model still shows the same no-win scenario even if applied to a *mixed* population:

Have *I* just proven that the Covid vaccine is also killing the *unvaccinated*?! (No.)

Rather, it is simply not possible to observe *any* neutral event, absolute or death-relative, without reducing the alive / observed ratio vs. a neutral starting date. How can neutral events occurring *after* the starting date possibly *add* time alive?

Thus, observation of reduced survival time vs. a neutral starting date *can not tell us that the event in question is non-neutral*. It is a Kobayashi Maru.

#### And so, my response to the February 1 semi-rebuttal

Nonetheless, Kirsch has provisionally reconstructed his proof around a time from first-dose analysis (i.e., because he asserts this affords an out to my proof). To reiterate, it is totally fine for Kirsch to assert (incorrectly) that my model and proof do not demonstrate that using “any event” is doomed to create a no-win scenario; but this same assertion is not in any sense a mathematical *demonstration* that my proof is wrong. I welcome being shown wrong. But being asserted wrong without proof is, imho, meaningless.

Regardless, I have replicated my previous proof from scratch, this time by using by-month first-vaccine-date values according to Kirsch’s remodeling of the airtable.

**update, February 2:*

In a follow-up post, I provide more detail on the *mea culpas* in Kirsch’s updates that are dissonant with the actual flaws which have been realized and modified due to the (partial) acknowledgement of my proof.

If you derived value from this post, *please* drop a few coins in your fact-barista’s tip jar.

## Solution to the Steve Kirsch Survey Proof

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.