I have stated repeatedly that COVID vaccine studies failed to adjust for natural immunity, early treatment, co-morbidities, improved care, healthy vaccinee user bias; McCullough has been on point too

by Paul Alexander

as is this new Doshi study, very prescient & hits the spot though did not include full range of confounding factors yet powerful paper! McCullough shared & it supports what I/Peter have been saying

Firstly, Ioannidis put out a very good paper on the factors influencing estimated effectiveness of COVID-19 vaccines in non-randomized studies. Biasing the estimates of effect. This is a real concern for methodological purists such as Ioannidis and myself as well as Dr. Harvey Risch and Dr. Peter McCullough and Dr. Ramin Oskoui. Yet we also recognize the potency and utility and even superb status of well-conducted observational research that often can even supersede poorly conducted RCTs.



These observation studies that were used to gain EUA in this fraud COVID non-pandemic, and for approving children shots as well as the across the board boosters are just purely junk science. Garbage, absurd, and in no way have been trustworthy and all involved know this. The research methods are so very poor that it makes it difficult to properly evaluate and none of the observational study estimates for the COVID vaccine studies could have been reliable. Along with the stated confounders above, there are other key factors that bias the estimates of effect including i) vaccination status misclassification, ii) exposure differences, iii) testing differences, iv) attribution issues v) cross over etc. Cross over from unvaccinated status to vaccinated can severely damage the estimates of effect, leading to untrustworthy estimates. The reality is that you can never ever know the full list of observational confounders and thus will fail to fully adjust the study (procedurally and statistically account for them) though we often can include key important ones. Yet there are always key unmeasured confounders (residual biases aka distorting variables) that negatively e.g. overestimate or underestimate, the estimate of effect.

Doshi et al. now unpack three additional sources of bias in these observational vaccine studies that we should keep in mind as we interpret the reported results:




Doshi et al. give examples ‘based on actual data sets that quantify how case-counting window bias, age bias, and background infection rate bias can profoundly complicate the analysis of observational studies, shifting covid-19 vaccine effectiveness estimates by an absolute magnitude as high as 50% to 70%.’

I have said prior and will again, had we done NOTHING, zero, we would have lost far fewer lives than we did. We needed to do nothing other than isolate (not forcibly) the sick and unwell. That was all. The medical response killed most of our peoples, as did lockdowns, school closures, the delayed medical care and the fraud mRNA technology injection. The irony is that the infections were already plummeting and near flat by the time the vaccines were rolled out in Feb 2021 or so and infections rose post roll-out. In other words, we created and prolonged this fraud emergency with the vaccines.

This graph tells a punishing story, the infections were down in February 2021 way before the vaccine could have had its effect. We did not need this shot, especially carte blanche. We did not need to continue any lockdowns. None of it.



McCullough’s tack here:

Courageous Discourse™ with Dr. Peter McCullough & John Leake
COVID-19 Vaccine Efficacy Grossly Overestimated from Non-Randomized Studies
By Peter A. McCullough, MD, MPH Proponents of COVID-19 mass vaccination will admit the products are not perfect yet claim they saved “millions of lives.” Major therapeutic claims such as mortality reduction with a single novel product can only be made on the basis of large, prospective, randomized, double-blind, placebo-controlled randomized trials with…
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