Survivor bias and how to overcome it
Your work is absolutely invaluable!
Thank you immensely for helping us to see the forest for the trees.
Another fabulous explanation....
For anyone who missed the podcast below,you will find lots of answers and numbers in one place. A terrific discussion and explanation Professor Norman - thank you also for addressing the seemingly stubborn mythical threads still pervading the collective consciousness. Where I live ...rolling out fifth (and sixth) booster.... simultaneously or spontaneously several people listening to white coats are also dutifully starting to wear masks. More people are also suddenly getting covid.... another strange coincidence of more and more madness.
Thanks for this very clear explanation. Showing you can get strange results even when there is no vaccine is a very important contribution.
Interesting video. I would submit that these kind of deceptions are constant throughout big pharma drug trials. The numbers and interpretations are so convoluted as to be rather meaningless. Negative data will always be purged and what remains will be polished and manipulated to a glaring sheen making even the worst drugs look like miracles.
The result is a squeaky clean vaccine empire never to be questioned and that doctors automatically accept as the holy gospel. If honest and truthful results were represented after drug trials, most drugs and vaccines would never see the light of day.
Your video was very clear and straight-forward, and I see why you advocate for person-years.
For me, the use of person-years introduces a different problem: given how the ONS, CDC etc have clearly fudged datasets (and are less than transparent or honest), person-years relies on access to accurate timestamps of receipt of dosage - data no organisation will provide on grounds it can violate patient privacy.
That means you're exchanging one type of bias (survivorship, in your case), for another one that is effectively a 'just trust us bro' (trust us bro these person years are accurate). I don't know if you read my article highlighting how the "new" ONS data (for 2021 published in 2023) conflicts with the old ONS data (for 2021 published in 2022), but person-years and death counts were both fudged (see: https://thedailybeagle.substack.com/p/fudgegate-ons-makes-person-years).
And I think you can concretely agree the agencies cannot be trusted, nor is this good data practices to take person-years on face value even if they were trustworthy, as unintended errors can be introduced into datasets, and it makes for bad accounting.
The issue with person-years becomes evident when agencies and health orgs intentionally have bad record keeping. For example, presuming all patients are unvaccinated (rather than asking for status), not sharing vaccination status between organisations, or even just recording data on untrackable post-it note cards (like what America used).
We also have no means to associate the deaths to the person-years group as external observers.
In truth we ought not to advise particular analysis techniques - the agencies should not be the "high priests" which interpret (and distort) the data for us. They should present the raw data as-is. So if it was me, the data fields would be a per-person breakdown:
Time(s) of each shot (blank if no record)
Type of each shot (manufacturer, brand, make, model, batch number, lot number, etc)
When they suffered an outcome (Date and time if possible)
Type of outcome they suffered
Such data would allow for a very in-depth analysis, the kind of which you propose. It also avoids classification of 'vaccinated' versus 'not vaccinated' using squiggly <21 days/<14 days BS. I don't think we should be advocating ONS analyse anything. They should be the Office of National Statistics, not the Office of Nasty Statistical Interpretations.
Gosh! I know absolutely nothing about this sort of thing and now I do. I would never have realised any of this - fiddling with numbers to get funny results. Who'd have thought it eh? Not me. What it actually means is that I can never trust any statistics or graphs or maths things ever again if they sound too good to be true or just a bit odd. The trouble is - will I realise they are a bit odd? Gosh!
Wonderful explanation. So this would be true for all vaccine reporting then? Also, does this type of bias apply with all medical trials?
You said it perfectly: "there are many biases and flaws in the way covid data is collected and analysed which (curiously) all favour exaggerated claims of vaccine efficacy and safety"- age is the only factor that would inherently disfavor the vaccine, and they ALWAYS make sure to adjust for age.
Meanwhile, the overall mortality impact of the mRNA vaccines in the two "gold standard" randomized blinded clinical trials (the ONLY studies which AVOID all of the substantial biases and flaws in vaccine studies) was "4 killed for every 3 saved". The separate Pfizer and Moderna "gold standard" randomized clinical trials BOTH had a 15-17% increase in non-COVID deaths, and specifically a 40-50% increase in cardiovascular-related deaths, with vaccine versus placebo.
Pfizer: https://www.nejm.org/doi/suppl/10.1056/NEJMoa2110345/suppl_file/nejmoa2110345_appendix.pdf – Table S4
Moderna: https://www.nejm.org/doi/suppl/10.1056/NEJMoa2113017/suppl_file/nejmoa2113017_appendix.pdf – Table S26
Normally, proper randomized clinical trials are the FIRST AND FOREMEST place everyone looks to evaluate whether a medical product save lives. In fact, these were the same two clinical trials used to approve the vaccines in the first place (based on the practically irrelevant "95% effectiveness" against mild-moderate infection). Doesn't it ever bother anyone that all of the "mRNA vaccines save lives" observational studies and data are NOT CONSISTENT with the randomized clinical trial mortality data?
It’s very disappointing that the CDC, with all its resources, can’t afford to employ any honest statisticians.
I wish you'd taught me stats at A level and my degree! Super clear again.
Thank you Sir. Yet another clear & erudite article.
The absolute core of their wretched evil in these few words indeed.
"therefore overestimating the mortality rate of the unvaccinated while underestimating the mortality rate of the vaccinated."
Wow. Fascinating. Then, of course, add in the fact that "unvaccinated" (vs. unjabbed ever) is a very messy category which confounds the efficacy #'s to a huge degree. Thank you!
Vaccinated only once should form a survival group. According to the clinical trial data they could make about 1,4 percent of population of vaccinated. Very interesting, large enough subgroup.
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I think this may be "immortal time bias". Maybe a subset of survivor bias.