Alan Richards it’s the old baffle them with bullshit brigade.
Had they even attempted to do correct, but short trials they wouldn’t have had to bully the planet into taking the junk jabs. We know Pfizer’s trials were unblinded, bias was shown in testing the placebo group regularly instead of all routinely.
Had they done so they’d have know efficacy was in the dirt along with safety, would have never been rolled out though and they’d missed out on all that spondooly.
Article I read in chemistry world in December 20/20 states the vaccines look promising, they were expecting efficacy of around 50% at best, the 85% plus far exceeded what they were hoping to achieve.
Near the end of the article two doctors, one on YouTube called Gregory Poland states how safety and efficacy shall be monitored post rollout. Well we know how well that one’s worked out don’t we!
None existent monitoring, denial and burying the numbers of those damaged.
The other doctor states that mask wearing and social distancing cannot be ruled out and should still be continued amongst the vaccinated.
So here we’re have it folks, the all singing dancing save the day vaccines were promoted in such a wonderful way, yet they were admitting in the same breath they didn’t know if they worked due to lack of long term data.
Well we all know the answer to that baloney, just as they did back then passing off this crap to the mugs they call the general public.
I'm not clear what is being plotted. Are person years counted as the time a person was hospitalized before death? How does that minimize survivor bias?
And does %PY refer to (sum of all person years for deaths from covid type) / (sum of all person years for deaths from all causes) ?
Yep, PY% is the percentage derived from summed person years in each category. One way to look at this is that person years is a weighting factor. If I can find the time I'll run a simulation comparing different methods of accounting for age.
I think Fenton's example is technically immortal time bias. Though I guess that may count as a type of survivor bias.
I can for example understand 3rd-dosers being healthier than 2nd-dosers owing to survivor bias. But "who" here is surviving "what" such that a bias would be introduced?
I've re-worded the articles to differentiate between use of person years 'proper' as a means to control for differing exposure (Fenton's article) as opposed to person years 'improper' as a weighting for age (my article).
Still not clear what "person-year" means. (The Fenton article you linked to concerns periods of time spent unvaccinated versus periods spent vaccinated, during an overall period, say a year; but the present extremely interesting-looking article has nothing to do with vaccinated v. unvaccinated.). Could you supply a child's guide to "person-year" in the present context, please.
Person years is classically used to account for differing exposure to a treatment such that 100 people getting jabbed in January will notch up 600 months (50 person years) of post treatment period between them come June, compared to only 300 months (25 person years) for folk getting jabbed in March.
We can also use 'person years' as a form of weighting such that a cohort of 100 60-year-olds will possess a combined age of 6,000 person years, compared to 3,000 person years for a sample of 30-year olds. When using person years in this manner - as I have done for part 6 - you get age-weighted results that are similar to those if we just count heads and ignore age at admissions. The difference between the two highlights a shift in the age profile, which can be very useful.
There are thus two forms of 'person years' but I'll re-word my articles and use something like 'age-weighted using person years' to avoid confusion.
Helpful; thankyou. I remain puzzled that this particular method of age-weighting counts the death of an 80-year old (with an average life expectancy of say 2 years) as twice the death of a 40-year old (with life expectancy of say 42). I'd have thought that, e.g., the precise opposite method would be more revealing of interesting period-to-period shifts.
That's a different approach altogether concerned with life years lost. The idea of weighting is to provide consistency across samples with different age profiles. If I were running a multivariate model such as logistic regression then age would be a covariate. I wouldn't be looking at years left to live unless we wanted to estimate life years lost or gained due to the therapy. Have you read part 6?
We should stop reinforcing the lie of waning efficacy. They actually make it worse.
From thesaurus.com
ANTONYMS FOR efficacy
inadequacy
ineffectiveness
enervation
failure
idleness
impotence
inability
inactivity
incapacity
incompetence
inefficiency
lethargy
powerlessness
weakness
inefficacy
unproductiveness
uselessness
Alan Richards it’s the old baffle them with bullshit brigade.
Had they even attempted to do correct, but short trials they wouldn’t have had to bully the planet into taking the junk jabs. We know Pfizer’s trials were unblinded, bias was shown in testing the placebo group regularly instead of all routinely.
Had they done so they’d have know efficacy was in the dirt along with safety, would have never been rolled out though and they’d missed out on all that spondooly.
Article I read in chemistry world in December 20/20 states the vaccines look promising, they were expecting efficacy of around 50% at best, the 85% plus far exceeded what they were hoping to achieve.
Near the end of the article two doctors, one on YouTube called Gregory Poland states how safety and efficacy shall be monitored post rollout. Well we know how well that one’s worked out don’t we!
None existent monitoring, denial and burying the numbers of those damaged.
The other doctor states that mask wearing and social distancing cannot be ruled out and should still be continued amongst the vaccinated.
So here we’re have it folks, the all singing dancing save the day vaccines were promoted in such a wonderful way, yet they were admitting in the same breath they didn’t know if they worked due to lack of long term data.
Well we all know the answer to that baloney, just as they did back then passing off this crap to the mugs they call the general public.
I'm not clear what is being plotted. Are person years counted as the time a person was hospitalized before death? How does that minimize survivor bias?
And does %PY refer to (sum of all person years for deaths from covid type) / (sum of all person years for deaths from all causes) ?
Instead of counting people I'm summing their age at admission. Have a look at this article....
https://wherearethenumbers.substack.com/p/never-vaccinated-vs-ever-vaccinated
Yep, PY% is the percentage derived from summed person years in each category. One way to look at this is that person years is a weighting factor. If I can find the time I'll run a simulation comparing different methods of accounting for age.
I think Fenton's example is technically immortal time bias. Though I guess that may count as a type of survivor bias.
I can for example understand 3rd-dosers being healthier than 2nd-dosers owing to survivor bias. But "who" here is surviving "what" such that a bias would be introduced?
I've re-worded the articles to differentiate between use of person years 'proper' as a means to control for differing exposure (Fenton's article) as opposed to person years 'improper' as a weighting for age (my article).
Still not clear what "person-year" means. (The Fenton article you linked to concerns periods of time spent unvaccinated versus periods spent vaccinated, during an overall period, say a year; but the present extremely interesting-looking article has nothing to do with vaccinated v. unvaccinated.). Could you supply a child's guide to "person-year" in the present context, please.
Person years is classically used to account for differing exposure to a treatment such that 100 people getting jabbed in January will notch up 600 months (50 person years) of post treatment period between them come June, compared to only 300 months (25 person years) for folk getting jabbed in March.
We can also use 'person years' as a form of weighting such that a cohort of 100 60-year-olds will possess a combined age of 6,000 person years, compared to 3,000 person years for a sample of 30-year olds. When using person years in this manner - as I have done for part 6 - you get age-weighted results that are similar to those if we just count heads and ignore age at admissions. The difference between the two highlights a shift in the age profile, which can be very useful.
There are thus two forms of 'person years' but I'll re-word my articles and use something like 'age-weighted using person years' to avoid confusion.
Helpful; thankyou. I remain puzzled that this particular method of age-weighting counts the death of an 80-year old (with an average life expectancy of say 2 years) as twice the death of a 40-year old (with life expectancy of say 42). I'd have thought that, e.g., the precise opposite method would be more revealing of interesting period-to-period shifts.
That's a different approach altogether concerned with life years lost. The idea of weighting is to provide consistency across samples with different age profiles. If I were running a multivariate model such as logistic regression then age would be a covariate. I wouldn't be looking at years left to live unless we wanted to estimate life years lost or gained due to the therapy. Have you read part 6?