Side effect truth (speech, elect, problems, broadcast)
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"he is also an assistant professor of pharmaceutical health services research at the University of Maryland School of Pharmacy"
He still has no expertise in vaccinology or epidemiology. The link I gave refutes each argument he made against the mRNA vaccine trials.
Quote:
Originally Posted by Good4Nothin
What he said about relative vs risk reduction is a FACT. The drug companies do this deliberately to make their results look impressive.
This is a more recent article describing multiple problems with the research.
Vaccine efficacy is calculated the way it was done in the trials, not the way Doshi wants to do it. He does not get to change the definition to suit his purposes. It demonstrates his lack of expertise in epidemiology.
"Vaccine efficacy/effectiveness is interpreted as the proportionate reduction in disease among the vaccinated group. So a VE of 90% indicates a 90% reduction in disease occurrence among the vaccinated group, or a 90% reduction from the number of cases you would expect if they have not been vaccinated."
There really is no grand worldwide conspiracy to cover up vaccine side effects.
As far as the link in the first post is concerned, how many of those people have GoFundMe accounts?
If they got COVID they were already infected when they got the vaccine. The vaccine itself cannot cause COVID because there is no virus in it.
There is no grand conspiracy to change society. It's a dangerous virus (how many deaths does it take to convince you of that?), the vaccine works, and side effects and adverse reactions are being reported. Folks who take the vaccine are given info on how to report them.
No, they have not "admitted that". The data are still being developed. Both Moderna and Pfizer have info from non-human primate studies supporting that the vaccines prevent infection, and Moderna has data from the time of the second dose showing fewer asymptomatic infections in the vaccinated group versus the placebo group.
Every vaccine takes time to work. That is to be expected, and no vaccine is 100% effective. The 95% rates for the mRNA vaccines are superb, though.
Exactly. Which means the test they were given was wrong or they got Covid at the vaccine site or in the car on the way home. They had already been self isolating before getting tested and vaccinated (they are both over 70). My guess is that the test was wrong and we will never know the true number of real cases.
He still has no expertise in vaccinology or epidemiology. The link I gave refutes each argument he made against the mRNA vaccine trials.
Vaccine efficacy is calculated the way it was done in the trials, not the way Doshi wants to do it. He does not get to change the definition to suit his purposes. It demonstrates his lack of expertise in epidemiology.
"Vaccine efficacy/effectiveness is interpreted as the proportionate reduction in disease among the vaccinated group. So a VE of 90% indicates a 90% reduction in disease occurrence among the vaccinated group, or a 90% reduction from the number of cases you would expect if they have not been vaccinated."
But only a very small percentage were infected. You don't know enough about statistics to understand this.
And if someone supposedly "refuted" everything he said, how do you know which is correct? The one you want to believe, of course.
But only a very small percentage were infected. You don't know enough about statistics to understand this.
And if someone supposedly "refuted" everything he said, how do you know which is correct? The one you want to believe, of course.
It appears that you do not understand the concept of statistical power and how the number of people needed to participate in a vaccine trial is calculated.
In the article I cited please tell us specifically what you disagree with and why.
Doshi still does not get to change the way vaccine efficacy is defined.
It appears that you do not understand the concept of statistical power and how the number of people needed to participate in a vaccine trial is calculated.
In the article I cited please tell us specifically what you disagree with and why.
Doshi still does not get to change the way vaccine efficacy is defined.
I UNDERSTAND the concept of statistical power. There were thousands of subjects in the vaccine trials, but only about 100 were infected. The 95% effective risk reduction statement is only based on that small sample. When you consider the entire sample, and calculate the absolute risk, the risk reduction is more like 1%.
But I doubt you know enough about statistics to understand this.
And if you want me to look at the article you cited again, you could link it again, or paste the relevant quote.
I UNDERSTAND the concept of statistical power. There were thousands of subjects in the vaccine trials, but only about 100 were infected. The 95% effective risk reduction statement is only based on that small sample. When you consider the entire sample, and calculate the absolute risk, the risk reduction is more like 1%.
But I doubt you know enough about statistics to understand this.
And if you want me to look at the article you cited again, you could link it again, or paste the relevant quote.
I quite understand the statistics.
The study was powered to detect a statistical significance when that number of infections was reached.
You are still trying to pull Doshi's stunt, which is to change the definition of vaccine efficacy.
"The sample size is driven by the total number of cases to demonstrate
VE (mRNA-1273 vs. placebo) to prevent COVID-19. Under the
assumption of proportional hazards over time and with 1:1
randomization of mRNA-1273 and placebo, a total of 151 COVID-19
cases will provide 90% power to detect a 60% reduction in hazard
rate (60% VE), rejecting the null hypothesis H0: VE ≤ 30%, with
2 IAs at 35% and 70% of the target total number of cases using a
1-sided O’Brien-Fleming boundary for efficacy and a log-rank test
statistic with a 1-sided false positive error rate of 0.025. The total
number of cases pertains to the Per-Protocol (PP) Set accruing at
least 14 days after the second dose. There are 2 planned IAs in this
study, which will be performed when approximately 35% and 70% of
the target total number of cases have been observed. Approximately
30,000 participants will be randomized with the following assumptions:
• The target VE against COVID-19 is 60% (with 95%
confidence interval lower bound ruling out 30%, rejecting the
null hypothesis H0: VE ≤ 30%).
• A 6-month COVID-19 incidence rate of 0.75% in the placebo
arm.
• An annual dropout rate of 2% (loss of evaluable participants).
• Two IAs at 35% and 70% of total target cases across the 2
treatment groups with O’Brien-Fleming boundaries for
efficacy monitoring.
• 3-month uniform accrual.
• Approximately 15% of participants will be excluded from the
PP population, and participants are at risk for COVID-19
starting 14 days after the second dose.
"The sample size is driven by the total number of cases to demonstrate
VE (mRNA-1273 vs. placebo) to prevent COVID-19. Under the
assumption of proportional hazards over time and with 1:1
randomization of mRNA-1273 and placebo, a total of 151 COVID-19
cases will provide 90% power to detect a 60% reduction in hazard
rate (60% VE), rejecting the null hypothesis H0: VE ≤ 30%, with
2 IAs at 35% and 70% of the target total number of cases using a
1-sided O’Brien-Fleming boundary for efficacy and a log-rank test
statistic with a 1-sided false positive error rate of 0.025. The total
number of cases pertains to the Per-Protocol (PP) Set accruing at
least 14 days after the second dose. There are 2 planned IAs in this
study, which will be performed when approximately 35% and 70% of
the target total number of cases have been observed. Approximately
30,000 participants will be randomized with the following assumptions:
• The target VE against COVID-19 is 60% (with 95%
confidence interval lower bound ruling out 30%, rejecting the
null hypothesis H0: VE ≤ 30%).
• A 6-month COVID-19 incidence rate of 0.75% in the placebo
arm.
• An annual dropout rate of 2% (loss of evaluable participants).
• Two IAs at 35% and 70% of total target cases across the 2
treatment groups with O’Brien-Fleming boundaries for
efficacy monitoring.
• 3-month uniform accrual.
• Approximately 15% of participants will be excluded from the
PP population, and participants are at risk for COVID-19
starting 14 days after the second dose.
I understand what they did. It's an old drug industry trick. If the risk is about 3% for getting the virus, and the vaccine lowered it to about 0.1%, then the absolute risk reduction is about 2.9%. The relative risk reduction, which they report is 95%.
What you pasted there is not relevant to understanding any of this.
I understand what they did. It's an old drug industry trick. If the risk is about 3% for getting the virus, and the vaccine lowered it to about 0.1%, then the absolute risk reduction is about 2.9%. The relative risk reduction, which they report is 95%.
What you pasted there is not relevant to understanding any of this.
I understand what they did. It's an old drug industry trick. If the risk is about 3% for getting the virus, and the vaccine lowered it to about 0.1%, then the absolute risk reduction is about 2.9%. The relative risk reduction, which they report is 95%.
What you pasted there is not relevant to understanding any of this.
It is fundamental to understanding how the study was conducted.
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