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(LifeSiteNews) — On November 8, 2021, the FDA published a report which included updated clinical-trial results of the Pfizer-BioNTech mRNA-based vaccine. The well-publicized trial included approximately 44,000 people over the age of 16, equally divided into two groups receiving either vaccine or placebo. 

On page 23 of this November 8 FDA report, we find that over the course of the trial (through March 31, 2021) there were a total of 38 deaths among participants: 21 in the vaccinated group and 17 in the placebo group. Because the number of deaths in the vaccinated group was not significantly less than in the placebo group, the vaccine failed to prove itself by the standard of all-cause mortality.

No one can truthfully claim that this vaccine has been proven by a clinical trial to save lives, yet people are being pressured to receive it under threat of court martial, lost employment, withheld medical treatment, exclusion from common life, and social stigma.

What is “all-cause mortality”? All-cause mortality is the number of deaths due to any cause in both the control and treated groups of a clinical trial. All-cause mortality is the most reliable clinical endpoint for the evaluation of a medical treatment intended to reduce mortality, because it is objective and properly accounts for unanticipated effects which may offset the anticipated benefits of treatment.

In 1991, the National Academy of Sciences published a volume of conference proceedings entitled, Modern Methods of Clinical InvestigationMedical Innovation at the Crossroads: Volume 1. In this volume, renowned anesthetist John P. Bunker contributed a paper entitled “The Selection of Endpoints in Evaluative Research,” in which he explained the evaluative advantage of all-cause mortality with many object lessons from the annals of medical history. He concluded the paper with this warning: “… when dealing with mortality as an endpoint of treatment, all-cause mortality is ignored at the peril of the investigators and the public.”

Bunker’s paper is highly readable — only six pages long — and well cited. Those who which to read it for themselves should download it, because it has now been “COVID-tagged.” (John Bunker is credited with founding the anesthesiology department of Stanford University School of Medicine.)

At first exposure, most people are surprised to learn that all-cause mortality is the standard endpoint for evaluating (intended) life-saving medical treatment. Why should we care about mortality from “all causes” when we are evaluating Treatment X for its mitigation of Disease X?

Let us suppose a person “Joe” received Treatment X, and it caused minor clots throughout his body. While Joe was driving, a clot broke loose and lodged in his brain, causing him to stroke and perish in the ensuing car accident. It would be nearly impossible to trace the true root cause of Joe’s death back to Treatment X.

But if Joe were part of a large clinical trial, these sorts of obscure effects of Treatment X would add up among the trial participants and manifest themselves in deaths due to “all causes,” and these would have to be considered relative to any lives saved due to the mitigating effect of Treatment X on Disease X. In short, all-cause mortality is the standard clinical endpoint, precisely because unanticipated effects of a treatment are notoriously difficult to discern.

But wasn’t the vaccine proven successful, because vaccine recipients were far less likely to contract COVID or to get a serious case of it? The prevention or mitigation of COVID is indeed a clinical endpoint with some merit, especially in the early stages of treatment development. A treatment which did not meet this endpoint in early trials could be dismissed from further consideration. However, as a final assessment, this clinical endpoint is inadequate, because it doesn’t properly account for obscure adverse effects which may offset the observed benefits.

I defer to John Bunker’s 1991 paper:

Proponents of new therapies understandably would prefer to judge their results on the basis of the specific condition the treatment is intended to relieve. An investigator might well ask why death from a completely unrelated cause should count against the proposed therapy. But it is not always clear that the “unrelated” cause is really unrelated. The latest example to come to my attention is a report from Scotland, in the British Medical Journal, in which the authors report an observational study correlating blood cholesterol levels with cardiac deaths and other endpoints, cancer in particular. The investigators found the predicted association between cholesterol level and cardiac deaths, but the reduction in cardiac deaths associated with lower cholesterol was offset by an equal increase in cancer deaths.

One may ask whether the difference in deaths is significant (21 in the vaccinated group vs. 17 in the placebo group). I don’t have the mathematical prowess to answer definitively, but I suspect it is not.

However, the question is largely irrelevant, because the burden of proving statistical significance was on the vaccine, not the placebo. The primary hypothesis of this clinical trial was that the vaccine would save lives. For the hypothesis to be upheld, the death rate in the vaccinated group had to be significantly less than that of the placebo group.

Obviously, this didn’t happen. As of yet, we have no scientific basis for retaining the hypothesis that the prescribed administration of the Pfizer COVID vaccine saves lives.

Some have argued that we can infer the life-saving qualities of vaccination by comparing the COVID death rates among societies with different vaccination rates. The argument looks like this: “Society X has a higher vaccination rate than Society Y, and the COVID death rate in Society X is much lower than that of Society Y. Thus, the vaccine must be saving lives in Society X.”

However, there are many reasons besides vaccination why the COVID death rate may be higher in one society than another, including different population-age distributions, vaccinations, and access to therapeutics.

But the most obvious problem with this analysis is the reinforcing bias of the clinician. In medical practice, the clinician knows whether the patient has been COVID-vaccinated. Among multiple co-morbidities, such a clinician might be more likely to attribute the death to COVID, if she knows that the patient is not vaccinated.

If this happens (and we must concede the possibility, else we would not demand double-blind clinical trials), then clinicians in populations with a higher vaccination rate would be less likely to attribute deaths to COVID than clinicians in populations with lower vaccination rates. There is simply no substitute for a controlled, double-blind clinical trial.

Might the Pfizer COVID vaccine be life-saving for some populations, especially older adults? Perhaps, but until proven in a clinical trial by the standard of all-cause mortality, the treatment remains experimental. It is not the regal pronouncement of the FDA which renders a treatment no longer “experimental,” but rather a successful clinical trial. Because the Pfizer COVID vaccine has not yet had a successful clinical trial by the standard of all-cause mortality, it remains experimental. And those who pressure others to receive it are still running afoul the Nuremberg code.

It is a futile exercise to make excuses for the trial. If only it had gone on longer or involved more people or older people, THEN we would have seen the lives saved. But we didn’t design the highest-profile clinical trial in world history. Pfizer did. Science isn’t about reinforcing what we hope or expect. Science is about learning what is. And what is not.

The author wishes to thank Alex Berenson for bringing public attention to the all-cause mortality results published in the November 8 FDA report.

Jennifer Hay lives in Farragut, Tennessee. She is a Catholic mother of six children, none of whom are impressed with her career as a mechanical engineer for a Fortune 500 company, her four patents, or her many, varied, and well-cited publications in scientific journals. 

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