It would be misleading to declare that the vaccines are not effective against Omicron. Rather, the investigators from Denmark found them to be effective, but the durability of the vaccines raised concerns.
This was a Pfizer backed fake study...
It would be misleading to declare that the vaccines are not effective against Omicron. Rather, the investigators from Denmark found them to be effective, but the durability of the vaccines raised concerns.
This was a Pfizer backed fake study...
Display MoreIn the study, from this point on, there was an advanced statistical technique called Propensity Score Matching, where two groups were balanced to be similar with regard to age and risk factors, such as previous diseases and sex. Thus 3,034 citizens using ivermectin were matched and compared with 3,034 non-users.
The hospitalization rate among users of the preventive medicine was 1.6%. Among non-users, it was 3.3%. Therefore, ivermectin reduced the need for hospitalizations by 56%.
A 68% reduction in mortality among ivermectin usersAmong the 3,034 with COVID not users of preventive ivermectin, 79 people (2.6%), died. Among the 3,034 in the comparison group, ivermectin users, 25 people (0.8%) died. So there was a 68% reduction in mortality.
“There was no early treatment established by the municipality, so there was no interference in the study regarding hospitalization and death. It’s just the effect of prophylaxis. With treatment after infection, the numbers tend to improve,” says Dr Flavio Cadegiani.
“The statistical technique used, the PSM, allows us to affirm that the study has the power close to a randomized clinical trial,” he added.
Propensity score matching is a standard technique used to try to improve the robustness of observational data. Many, many studies use it. Unfortunately it suffers problems like any of the other similar techniques.
https://gking.harvard.edu/files/gking/files/psnot.pdf
We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal — thus increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSMcomesfromits attempts to approximate a completely randomized experiment, rather than, as with other matching methods, a more efficient fully blocked randomized experiment. PSM is thus uniquely blind to the often large portion of imbalance that can be eliminated by approximating full blocking with other matching methods. Moreover, in data balanced enough to approximate complete randomization, either to begin with or after pruning some observations, PSM approximates random matching which, we show, increases imbalance even relative to the original data. Although these results suggest researchers replace PSM with one of the other available matching methods, propensity scores have other productive uses.
If he says that, throw in his lap the Anglemeyer study, published in Cochrane in 2014, and watch him have a sudden malady. This study says that there are no significant differences between observational studies with “gold standard” RCT study, especially when the difference is greater than 8% efficacy, as is the case. In other words. It says that observational studies do prove efficacy.
Anglemeyer I've linked it for you (FM1, give your readers the courtesy of links please).
This is an interesting metastudy of reviews comparing observational and RCT results over all possible illnesses:
We examined systematic reviews that were designed as methodological reviews to compare quantitative effect size estimates measuring efficacy or effectiveness of interventions tested in trials with those tested in observational studies. Comparisons included RCTs versus observational studies (including retrospective cohorts, prospective cohorts, case-control designs, and cross-sectional designs). Reviews were not eligible if they compared randomized trials with other studies that had used some form of concurrent allocation.
So why is its data completely incomparable with the COVID situation?
Eleven (73%) reviews had low risk of bias for explicit criteria for study selection
Just to remind our audience - the "low risk of bias" metastudies come out neutral on ivermectin.
In addition the reviews were based on non-pandemic "take a long time to publish and incorporate peer review feedback" papers. So the overall quality would be expected to be much higher.
For COVID in an emergency situation, quite naturally:
There are lies, damned lies, and statistics. Worse than all of those is fanatics like the BIRD/FLCCC people who are convinced they are right and reckon publication is about PR not science, and anyone who dares to criticide what they say is not qualified to do so and part of a conspiracy.
Here what an unbiassed EMT doctor says in response to the FLCC claims (and note Tess Laurie's absolute fanaticism in being sure she knows better than anyone else because she is "expert". That is not how scientific experts behave. In fact it is the definition of not being expert). This is a real and highly independent (Rebel Wisdom) journalistic attempt to be unbiassed and evaluate non-standard views with an idea that often they are correct.
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THH
Why wouldn’t we make this available, especially if we want Hoosiers to stop using horse paste. Hoosiers should be able to care for their health safely and effectively.”
Hopefully the Bill sees the light of day sometime..
as they say in Hoosier land
"Sun don't shine on the same dog's ass everyday,
but, mister you ain't seen a ray of light since you got here.
Propensity score matching is a standard technique used to try to improve the robustness of observational data. Many, many studies use it. Unfortunately it suffers problems like any of the other similar techniques.
Maybe you missed it, but RCTs also "suffer problems". Is there any human study methodology that is without it's drawbacks? After all, we are not as simple a species to study as rats in a cage.
I have to wonder if you even looked closely at this Brazil study? It was extremely well done it appears to me. 113,000 volunteered to use Ivermectin vs 45,000 that refused. There was even a built-in bias against IVM, that had it been accounted for would have made the findings even more impressive.
The many authors, and matter experts invited in to assist, clearly understood that their work would be harshly, and unfairly, reviewed due the politics, so went to great lengths to make sure they did everything "by the book". If not, they knew their very reputation, and possibly their jobs, would be in jeopardy.
Perhaps the one thing they did not factor in was Confirmation Bias? That no matter how professionally they did their work, they would be judged more through the prism of personal politics, and bias, than the actual science.
Potential to Kinetic
A 75 kilogram circus star is shot from a cannon, straight up, to a height of 1000 meters with an initial velocity of 10 meters per second. The star then falls back into a net.
What is their kinetic energy when they reach 1000 meters?
0 joules
A dog is riding in the front seat of a car on the freeway and has a mass of 4 kilograms. The car is traveling at 40 meters per second. What is the kinetic energy of the dog?
3200 joules
No bias 👍〽️✔〽️✔〽️✔😊
Display MorePotential to Kinetic
A 75 kilogram circus star is shot from a cannon, straight up, to a height of 1000 meters with an initial velocity of 10 meters per second. The star then falls back into a net.
What is their kinetic energy when they reach 1000 meters?
0 joules
I am certain that a vertical ballistic trajectory of 1000 m with a 75 kg load cannot occur starting at an initial velocity of 10 m/s unless that was the relative velocity of the cannon before just before firing it the same direction.
More like a vertical trajectory of about 1020 mm, if gravity is 9.8 m/s2.
The alternative of taking nearly two minutes to apogee seems weird.
Display MorePropensity score matching is a standard technique used to try to improve the robustness of observational data. Many, many studies use it. Unfortunately it suffers problems like any of the other similar techniques.
https://gking.harvard.edu/files/gking/files/psnot.pdf
We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal — thus increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSMcomesfromits attempts to approximate a completely randomized experiment, rather than, as with other matching methods, a more efficient fully blocked randomized experiment. PSM is thus uniquely blind to the often large portion of imbalance that can be eliminated by approximating full blocking with other matching methods. Moreover, in data balanced enough to approximate complete randomization, either to begin with or after pruning some observations, PSM approximates random matching which, we show, increases imbalance even relative to the original data. Although these results suggest researchers replace PSM with one of the other available matching methods, propensity scores have other productive uses.
Anglemeyer I've linked it for you (FM1, give your readers the courtesy of links please).
This is an interesting metastudy of reviews comparing observational and RCT results over all possible illnesses:
We examined systematic reviews that were designed as methodological reviews to compare quantitative effect size estimates measuring efficacy or effectiveness of interventions tested in trials with those tested in observational studies. Comparisons included RCTs versus observational studies (including retrospective cohorts, prospective cohorts, case-control designs, and cross-sectional designs). Reviews were not eligible if they compared randomized trials with other studies that had used some form of concurrent allocation.
So why is its data completely incomparable with the COVID situation?
Eleven (73%) reviews had low risk of bias for explicit criteria for study selection
Just to remind our audience - the "low risk of bias" metastudies come out neutral on ivermectin.
In addition the reviews were based on non-pandemic "take a long time to publish and incorporate peer review feedback" papers. So the overall quality would be expected to be much higher.
For COVID in an emergency situation, quite naturally:
- Publication is fast with limited time for rewriting and consistency checking
- The barrier for publication is lower
- The Ivermectin positive reviews do not admit risk of bias as a selection criteria, so they include rubbish studies.
- The measure used: mortality, is very rare so it is more difficult to get statistically reliable data, and also easier for bias to move the very few deaths from one category to another.
There are lies, damned lies, and statistics. Worse than all of those is fanatics like the BIRD/FLCCC people who are convinced they are right and reckon publication is about PR not science, and anyone who dares to criticide what they say is not qualified to do so and part of a conspiracy.
Here what an unbiassed EMT doctor says in response to the FLCC claims (and note Tess Laurie's absolute fanaticism in being sure she knows better than anyone else because she is "expert". That is not how scientific experts behave. In fact it is the definition of not being expert). This is a real and highly independent (Rebel Wisdom) journalistic attempt to be unbiassed and evaluate non-standard views with an idea that often they are correct.
External Content www.youtube.comContent embedded from external sources will not be displayed without your consent.Through the activation of external content, you agree that personal data may be transferred to third party platforms. We have provided more information on this in our privacy policy.
THH
You just continue to deny the science!
The BMJ Demands More Vaccine Trial Data from Pharma and its Regulators
A prominent international medical journal has renewed demands for more transparency by coronavirus vaccine manufacturers, who have repeatedly delayed disclosing critical information about clinical trials and study results.
The BMJ, a weekly peer-reviewed United Kingdom-based medical journal, published an article this week calling on vaccine makers Pfizer, Moderna, and AstraZeneca, and also therapeutics manufacturers like Regeneron, which makes monoclonal antibodies and Gilead Sciences, maker of remdesivir to make their drug data public.
Pharma and Government Regulators Failing to Release Trial Data
The article’s author, Peter Doshi, an associate professor of pharmaceutical health services research in the School of Pharmacy and associate editor at The BMJ, also criticized medical journals for failing to hold pharmaceutical companies to account for missing data and government regulators at the U.S. Food and Drug Administration for being complicit in data secrecy.
“Journal editors, systematic reviewers, and the writers of clinical practice guideline generally obtain little beyond a journal publication, but regulatory agencies receive far more granular data as part of the regulatory review process,” Doshi wrote.
Doshi has been a critic of the government’s pandemic response and testified about his concerns at a hearing organized by U.S. Senator Ron Johnson (R-Wisconsin) as recently as November.
FDA Complicit in Pharma Secrecy
Doshi cited a recent Freedom of Information lawsuit against the FDA, which initially argued that it could only manage to release 500 pages per month of Pfizer’s mRNA vaccine data, which meant that the complete files would be hidden for decades.
“Among regulators, the U.S. Food and Drug Administration is believed to receive the most raw data but does not proactively release them,” Doshi said.
After a federal court ordered the agency to speed up disclosure the FDA agreed to release 55,000 pages per month, a development that calls into question the agency’s complicity with companies it claims to regulate.
The article cites company statements from Moderna, AstraZeneca, and Johnson & Johnson that suggest neither company will prioritize timely disclosure of trial data.
COVID Therapeutical Data Is Also Being Withheld
Doshi also noted that published reports of Regeneron’s phase III trial results for REGEN-COV lack participant level data. And in another example of government abetment, the U.S. National Institutes of Health, which funded clinical trials and runs a research portal for remdesivir that acknowledges that “longitudinal data set only contains a small subset of the protocol and statistical analysis plan objectives.”
EMA and Health Canada Slightly More Transparent
Doshi praised Health Canada and the European Medical Agency for releasing far more COVID vaccine and therapeutics clinical trial data than its American counterpart but remained critical of those agencies for delays in producing documents with copious redactions, missing appendices and omissions of participant level data.
Doshi argues that pharmaceutical companies, which have made billions of dollars from public financed vaccine contracts, and governments, which have mandated these drugs and instituted draconian public health policies based on these companies data, have an obligation to be more transparent.
“The purpose of regulators is not to dance to the tune of rich global corporations and enrich them further; it is to protect the health of their populations,” Doshi wrote. “We need complete data transparency for all studies, we need it in the public interest, and we need it now.”
The BMJ is a Long-Standing Critical Voice on Health Policy
Previously known as the British Medical Journal, The BMJ is one of the world’s oldest continuously published medical research publications. The journal made headlines last year with its report of clinical trial errors and fraudulent practices by Pfizer’s clinical research contractor Ventavia Research Group.
The journal reported that Ventavia, the largest privately owned medical research company in Texas suffered from poor laboratory management, patient safety concerns, and data integrity issues. The journal also reported the company fired a whistleblower who brought the problems to the attention of federal regulators
There are lies, damned lies, and statistics. Worse than all of those is fanatics like the BIRD/FLCCC people who are convinced they are right
Clowns never look in a mirror as it would be sad to see only a mediocre clown....
Your statement is made from a fascist stony heart and bears no reality...
Is there an automated script here that puts a 🏆 to each other posts from FM1 and Wyttenbach immediately? 😉
Display MorePropensity score matching is a standard technique used to try to improve the robustness of observational data. Many, many studies use it. Unfortunately it suffers problems like any of the other similar techniques.
https://gking.harvard.edu/files/gking/files/psnot.pdf
We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal — thus increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSMcomesfromits attempts to approximate a completely randomized experiment, rather than, as with other matching methods, a more efficient fully blocked randomized experiment. PSM is thus uniquely blind to the often large portion of imbalance that can be eliminated by approximating full blocking with other matching methods. Moreover, in data balanced enough to approximate complete randomization, either to begin with or after pruning some observations, PSM approximates random matching which, we show, increases imbalance even relative to the original data. Although these results suggest researchers replace PSM with one of the other available matching methods, propensity scores have other productive uses.
Anglemeyer I've linked it for you (FM1, give your readers the courtesy of links please).
This is an interesting metastudy of reviews comparing observational and RCT results over all possible illnesses:
We examined systematic reviews that were designed as methodological reviews to compare quantitative effect size estimates measuring efficacy or effectiveness of interventions tested in trials with those tested in observational studies. Comparisons included RCTs versus observational studies (including retrospective cohorts, prospective cohorts, case-control designs, and cross-sectional designs). Reviews were not eligible if they compared randomized trials with other studies that had used some form of concurrent allocation.
So why is its data completely incomparable with the COVID situation?
Eleven (73%) reviews had low risk of bias for explicit criteria for study selection
Just to remind our audience - the "low risk of bias" metastudies come out neutral on ivermectin.
In addition the reviews were based on non-pandemic "take a long time to publish and incorporate peer review feedback" papers. So the overall quality would be expected to be much higher.
For COVID in an emergency situation, quite naturally:
- Publication is fast with limited time for rewriting and consistency checking
- The barrier for publication is lower
- The Ivermectin positive reviews do not admit risk of bias as a selection criteria, so they include rubbish studies.
- The measure used: mortality, is very rare so it is more difficult to get statistically reliable data, and also easier for bias to move the very few deaths from one category to another.
There are lies, damned lies, and statistics. Worse than all of those is fanatics like the BIRD/FLCCC people who are convinced they are right and reckon publication is about PR not science, and anyone who dares to criticide what they say is not qualified to do so and part of a conspiracy.
Here what an unbiassed EMT doctor says in response to the FLCC claims (and note Tess Laurie's absolute fanaticism in being sure she knows better than anyone else because she is "expert". That is not how scientific experts behave. In fact it is the definition of not being expert). This is a real and highly independent (Rebel Wisdom) journalistic attempt to be unbiassed and evaluate non-standard views with an idea that often they are correct.
External Content www.youtube.comContent embedded from external sources will not be displayed without your consent.Through the activation of external content, you agree that personal data may be transferred to third party platforms. We have provided more information on this in our privacy policy.
THH
The only fanatic is you , pushing a failed vaccine and continually telling us they can be tweaked for new varients. We have had quite a few vaccine evasive varients yet no tweak. SA reports boosters are useless in stopping the spread. You also deny every positive result from off label cheap early treatment. You call yourself a man of science, yet you continually deny the science.
South Africa study shows boosters failed to block omicron, bolstering case for face masks, distancing and hand washing
Is there an automated script here that puts a 🏆 to each other posts from FM1 and Wyttenbach immediately? 😉
Thanks for the morning laugh. It does seem the you w myself and hux are online each day around the same time. For me it's after my morning chores. So good morning . Great minds think alike.
Like I said at the very beginning of this thread vaccination would be useless and only known antivirals used in combination early on in the illness , or used prophylactically would end this pandemic and result in herd immunity ie endemic. So now in the UK its just the commom cold!!!
Display MoreMaybe you missed it, but RCTs also "suffer problems". Is there any human study methodology that is without it's drawbacks? After all, we are not as simple a species to study as rats in a cage.
I have to wonder if you even looked closely at this Brazil study? It was extremely well done it appears to me. 113,000 volunteered to use Ivermectin vs 45,000 that refused. There was even a built-in bias against IVM, that had it been accounted for would have made the findings even more impressive.
The many authors, and matter experts invited in to assist, clearly understood that their work would be harshly, and unfairly, reviewed due the politics, so went to great lengths to make sure they did everything "by the book". If not, they knew their very reputation, and possibly their jobs, would be in jeopardy.
Perhaps the one thing they did not factor in was Confirmation Bias? That no matter how professionally they did their work, they would be judged more through the prism of personal politics, and bias, than the actual science.
(1) doubly-blinded placebo-controlled RCTs are inherently less biassed than any observational trial. This is a particular class of trials where we know bias is more of a problem than usual, so that counts.
Why do we know bias is more of a problem (other than the obvious things)? Because if you chart RoB against results for ivermectiin trials you get this very obvious line with higher RoB => larger apparent effect, with zero RoB => no statistically significant effect.
(2) This study does look better than some observational studies, but that is not saying a lot.
Good: clear methodology for patient selection etc
Bad: no sensitivity checks
Appalling: Propensity score is all wrong. (And it is that which determines the results).
Problematic, they do not mention how they chose their propensity score, or why they thing it is appropriate - that would be picked up in any decent peer review.
Why is the propensity score wrong?
It bands age into < 30, 30 - 50, > 50. These bands seem to be what they use for the PSM.
Thus for matching purposes an 80 year old and a 50 year old will match, but the 80 year old has 30X higher risk of mortality.
For COVID there is no excuse using wide bands on age - they lead to big artifacts, and in this case it is a very large study so difficult to see why they do this? COVID has this particular predictable and strong dependence on age. Not all illnesses are like that - and it means that above all you have to be careful how you adjust/match/score for age.
It is a shame because they could easily have done some sensitivity analysis with different bands, or a comparison, for example using logistic regression, to show that there was no problem. But then either of those would show there is a problem.
Interestingly this problem with large age bands is the same issue that make a lot of the comparisons on whole population mortality with vaccination data also invalid. You need narrow bands to reduce confounders.
Who peer reviewed this?
THH
The only fanatic is you , pushing a failed vaccine and continually telling us they can be tweaked for new varients. We have had quite a few vaccine evasive varients yet no tweak. SA reports boosters are useless in stopping the spread. You also deny every positive result from off label cheap early treatment. You call yourself a man of science, yet you continually deny the science.
How about you leave off the emotion and cite specific cases of bias. What have I said which is unjustified?
As far as pushing vaccines goes my latest post on that topic was remarkably unpushing of vaccines. Go check.
What are the Omicron facts we definitely know?
Related to case numbers::
1) Hospitalizations occur at a very low frequency of at least 10x less than from delta.
2) Duration of hospital stay is much shorter at least 50% less than from delta.
3) ICU from Omicron is very rare. Patients in ICU with Omicron have been delivered for other reason like brain stroke accident etc..
4) how long will the Omicron wave last? RSA shows a halve live of 4 weeks so it will last quite long until we are back to e.g. 1% of today's max cases. This could last up to 6 months.
5) Vaccine gene therapy
- RNA vaccines fuel the Omicron pandemic 2x vaxx 2x more Omicron cases 3x vaxx 4x more cases. (Could be due to preconditions as the vulnerable had the least chance to get a natural infection.
- So basically Omicron infects everybody at more or less the same rate (Israel data)
- Pfizer gives most people > 50% no protection also boosters. Best range 15..30% protection with boosters for some lucky well responding ones.
- Natural infection from delta 4x better protection than from 2xx vaxx but only 60% better if from Alpha....
- J&J data so far looks best for Omicron protection as it also was the only one that did work for Beta.
- Detailed Moderna data is still missing but certainly much ( >2x from Swiss data) better than Pfizer.
6) Actual data quality::
- Switzerland e.g. has a positive rate of 40% with about 110'000 tests/day. So we get much less than 1/10 of the actual infections.
- This implies that all rate data is at least 10x lower for severe events and 10x higher for infections...As explained the rollover factor is about 2 so the effective rate change is anywhere between 5..10.
The fake factors currently are high. Hospital statistics can be up to 90% wrong. Most FM/R/J/B mafia governments still spread fake news about only unvaxx in ICU. So never read news look at base data. Be aware that almost all papers are based on delta figures, that are off by magnitudes for Omicron.
7) Treatment
- Ivermectin works much better for Omicron than for Delta. According FLCCC there was no need to add other drugs to the base treatment protocol. For delta about 10 more drugs have been used in parallel....
The early Ivermectin states Uttar Pradesh/Delhi/West Bengal see a stable situation where as the vaccine terror state of Kerala is going through the roof with already 20x the cases of Uttar Pradesh...
Who should worry Omicron?
- Basically all vulnerable 2,3,4 x vaxx with Pfizer/Oxford-Zeneca and no access to real treatment. Best solution: FP 98 masks and standard preparation with high dose V-D3 (5000IU) + 200ug V-K2 25mg zinc Quercetin. Have budenosid, Betadiene (iodine 10:1) lotion ready. Buy some Nigella Sativa.
What are the Omicron facts we definitely know?
Related to case numbers::
1) Hospitalizations occur at a very low frequency of at least 10x less than from delta.
2) Duration of hospital stay is much shorter at least 50% less than from delta.
3) ICU from Omicron is very rare. Patients in ICU with Omicron have been delivered for other reason like brain stroke accident etc..
And, missing context:
So the question - which since it is difficult and does not have an "its obvious" answer antivaxxers will not touch, W just seems to be following them, is:
How much of this lower severe disease is from survivor immunity, how much from vaccination, how much from better treatment, how much because omicron is less severe, and how much because of age differences.
It will likely be all of these, and some will matter more than others. Just pointing to a less severe infection without analysis of all the other factors does not prove anything.
Of course - we should be heartily glad about its being less severe in effect, whatever the reasons!
THH
RNA vaccines fuel the Omicron pandemic 2x vaxx 2x more Omicron cases 3x vaxx 4x more cases
Quite a day fro Wyttenfacts. This is a juicy one.
Kerala is going through the roof with already 20x the cases of Uttar Pradesh..
Umm... why is that that developed provinces in India now see more cases, deaths than undeveloped?
I won't repeat it - remember there are at least 4 distinct reasons all of which skew the figures.
How about you leave off the emotion and cite specific cases of bias. What have I said which is unjustified?
As far as pushing vaccines goes my latest post on that topic was remarkably unpushing of vaccines. Go check.
Emotion? You are a very silly man and please provide the post you refer to. I went back a couple of pages but didn't see it. As for specific bias on your part, I don't have all day to point all of them out.