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no to nuclear Iran, no to two-state solution, no to bi-national state
@Adam
In my summary of Fenton’s article, I left out a key point he highlights at the end of his article. He posits that we should look at all-cause mortality and seek a justification between the increased deaths from Dec 2020 onward against the vaccine. He feels it will over come the issue of petty arguments and focus on the cost and benefits of the vaccine rollout. He notes that there is a confounder present in that the unvaccinated could be receiving treatments that the vaccinated are not, but it is the best way he finds to separate the issues. He notes that there has been a study that was conducted to look at this very issue in England. The study showed that
He is concerned that they don’t have the ages to correlate with the deaths, which would be important factor that could present confounding
The topic of all-cause mortality is important because of the effect of the vax on the Tcells. The vax has been shown to cause the Tcell population to stop functioning. Dr. Cole spoke about this at the White Coat symposium last month. The Tcells are responsible for what is known as the cell mediated response of the immune system as opposed to the Bcells that are responsible for the antibody production. The Tcells don’t use antibodies, they just go to the site of infection and destroy any tissues that they detect as being infected. But the body should have the ability to have both Tcells and Bcells respond when the virus is reintroduced to the body and create two separate responses to the virus, this is the very purpose of vaccines. But it seems with Covid, the vax leaves the Tcells inoperative as well as the Bcells since the antibodies are not produced to protect against the virus. Why is this important? If the Tcells are not working following vaccination, the vaccines could be allowing infections by other diseases to enter the body unopposed. It could also allow previous virus’ that the patient is immune to, such as shingles, to resurface – which is occurring in many vaccinated people. Both of these facts could result lead to an increase in deaths following this breach in the immune system. By looking at all-cause mortality, we could gain an insight to see if the body’s immune system is prevented from actually being protective against diseases beyond Covid. If there is an increase in the all-cause mortality that is coincident with the vaccination program, it would be a very dangerous situation. This is a very important topic on which I am interested in seeing more details.
The fact that England showed an increase in their all-cause mortality coincident with the vaccines among the is very alarming.
The Open Public Session yesterday before the FDA advisory board, which overwhelmingly voted against universal booster program, is very informative. Even one or two of the vax advocates made some chilling observations that I did not expect from them. The video is here:
https://youtu.be/WFph7-6t34M?t=14587
It should start about 4hr 3min. mark but the important info begins around 4hr 10min. This lasts for about 55min and there is a lot of powerful info on the vax, though only a couple of brief mentions on treatments, it is a very concentrated discussion on so many topics, it could fill a book. I strongly recommend anyone, vax sceptic or vax advocates to find the time to listen to these comments.
I can’t reproduce the diagrams, but I can post the direct cite to them so they can be scrolled from one to the next. Sorry, I am not sure how to post a diagram here on Israpundit.
The charts are located here:
https://www.researchgate.net/figure/Data-from-Public-Health-England-June-2021_fig1_354601308
@Adam
Sorry, an RCT is a Randomized Clinical Trial. It is when you take two groups and compare them against eachother by changing a single variable or a couple of variables. Usually it is a single variable change such as using a drug cocktail in one group and giving a placebo in another group. The randomness is achieved by using various elements of society based on sex, age, socioeconomics, education, blue-collar vs white collar employment, etc. Every characteristic that is included in both groups in equal numbers will eliminate any bias between the two groups related to that characteristic. For example if they don’t correct for sex, that would incur a bias based upon sex which could shift the outcome of the study. Women for example catch Covid in higher numbers, but men experience more severe forms of the disease, so if sex was not randomized in a matching fashion, it would alter the results of the study’s outcome.
The RCT can be unblinded where the subject and the clinician conducting the study know who is in the the test group or placebo group. Alternatively, the RCT can be single blinded, where the subject does not know if they are in the test group or placebo group. It can also be double-blinded where both the clinicians and the subjects do not know if they are in the test group or placebo group. Double blinded is the better of these as it removes bias from the subject and the clinician, but it is also much more expensive to run and timely to plan.
Many believe the RCT is the gold standard for studies base upon the randomness. The truth is that good data can be gained at every level of trials from case review studies to observational studies to RCT. Bad data can also be seen in each of these. Too many RCTs are being run and it limits the ability to do followup studies based upon the findings in the first trial. For instance, if you look at the original EUA study results, it showed they had no statistical difference between the test group and the placebo group. What should have been done at that time is to redo the study using at-risk subjects, over 75yrs and in African Americans or Native Americans who all have higher risks in Covid. All of these were excluded in the EUA studies. They could also have followed up with studies considering the possibility of transmissible which was not tested for in the first study. Followup studies are very important to both confirm the original study’s findings and to pursue further inquiries as well.
Medical support for any treatment or vaccine is based upon the preponderance of evidence as in the EUA studies, but when the evidence is a single study with no statistical difference in the outcome you have no evidence to guide treatment. Even if you have a few studies or a few dozen studies, such things are very limiting to gain clinical confidence in the treatment, especially if the number of subject in a study are a couple dozen to a couple hundred as is the case time and again in the govts Covid research. Hundreds to thousands of studies provide a good basis for input, and less than this if the data is aligned as with IVM where it clearly shows a benefit, and the level of benefit is the only alternating issue between the studies. With this Covid crisis, the govt eeks out a single study to guide policy, again and again and they act like this is how things work, when in fact this is how things work when you want to control the outcome and you don’t want to have that outcome questioned. This is also the reasoning for the censorship and threats of retribution for treating the ill before they are sick enough to be hospitalized.
Peloni: Could you reproduce the all-important charts in DR. Fenton et al’s study for us? My computer simply refuses to print them. Without the charts, it is difficult to understand what these mathematicians are trying to tell us about the reliability of information from the CDC, Johns Hopkins, the British health ministry, etc. about covid and the vaccines that are being used to prevent and treat it.
Peloni–what is an RCT?
PreprintPDF Available
Paradoxes in the reporting of Covid19 vaccine effectiveness: Why current studies (for or against vaccination) cannot be trusted and what we can do about it
September 2021
DOI:10.13140/RG.2.2.32655.30886
Authors:
Norman Elliott Fenton at Queen Mary, University of London
Norman Elliott Fenton
Queen Mary, University of London
Martin Neil at Queen Mary, University of London
Martin Neil
Queen Mary, University of London
Scott Mclachlan at Queen Mary, University of London
Scott Mclachlan
Queen Mary, University of London
Preprints and early-stage research may not have been peer reviewed yet.
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References (14)
Figures (5)
Abstract and Figures
Given the limitations of the randomized controlled trials (RCTs) for Covid19 vaccines, we must increasingly rely on data from observational studies to determine vaccine effectiveness. But over-simplistic reporting of such data can lead to obviously flawed conclusions due to statistical paradoxes. For example, if we just compare the total number of Covid19 deaths among the vaccinated and unvaccinated then we are likely to reach a different conclusion about vaccine effectiveness than if we make the same comparison in each age category. But age is just one of many factors that can confound the overall results in observational studies. Differences in the way we classify whether a person is vaccinated or is a Covid19 case can also result in very different conclusions. There are many critical interacting causal factors that can impact the overall results presented in studies of vaccine effectiveness. Causal models and Bayesian inference can in principle be used to both explain observed data and simulate the effect of controlling for confounding variables. However, this still requires data about relevant factors and much of these data are missing from the observational studies (and the RCTs). Hence their results may be unreliable. In the absence of such data, we believe the simplest and most conclusive evidence of vaccine evidence is to compare all-cause deaths for each age category between those who were unvaccinated and those who had previously had at least one vaccine dose.
Data from Public Health England, June 2021
…
Causal model reflecting the observed data
…
Causal model as a Bayesian network with probability tables taken from the observed data
…
Marginal probabilities (Age and Vaccinated are rounded to 0 decimal places)
…
Unconfounded impact of vaccination: probability of death decreases from 0.417% to 0.104%
…
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1 Paradoxes in the reporting of Covid19 vaccine effectiveness Why current studies (for or against vaccination) cannot be trusted and what we can do about it Norman Fenton, Martin Neil and Scott McLachlan Risk Information and Management Research School of Electronic Engineering and Computer Science, Queen Mary University of London 15 Sept 2021 Abstract Given the limitations of the randomized controlled trials (RCTs) for Covid19 vaccines, we must increasingly rely on data from observational studies to determine vaccine effectiveness. But over-simplistic reporting of such data can lead to obviously flawed conclusions due to statistical paradoxes. For example, if we just compare the total number of Covid19 deaths among the vaccinated and unvaccinated then we are likely to reach a different conclusion about vaccine effectiveness than if we make the same comparison in each age category. But age is just one of many factors that can confound the overall results in observational studies. Differences in the way we classify whether a person is vaccinated or is a Covid19 case can also result in very different conclusions. There are many critical interacting causal factors that can impact the overall results presented in studies of vaccine effectiveness. Causal models and Bayesian inference can in principle be used to both explain observed data and simulate the effect of controlling for confounding variables. However, this still requires data about relevant factors and much of these data are missing from the observational studies (and the RCTs). Hence their results may be unreliable. In the absence of such data, we believe the simplest and most conclusive evidence of vaccine evidence is to compare all-cause deaths for each age category between those who were unvaccinated and those who had previously had at least one vaccine dose. creative commons license
2 The randomized controlled trials (RCTs) to establish the safety and effectiveness of Covid19 vaccines produced impressive results (Polack et al., 2020) but were inevitably limited in the way they assessed safety (Folegatti et al., 2020)1 and are effectively continuing (Ledford, Cyranoski, & Van Noorden, 2020; Singh et al., 2021) . Ultimately, the safety and effectiveness of these vaccines will be determined by real world observational data over the coming months and years. However, data from observational studies on vaccine effectiveness can easily be misinterpreted leading to incorrect conclusions. For example, we previously noted2 the Public Health England data shown in Figure 1 for Covid19 cases and deaths of vaccinated and unvaccinated people up to June 2021. Overall, the death rate was three times higher in the vaccinated group, leading many to conclude that vaccination increases the risk of death from Covid19. But this conclusion was wrong for this data because, in each of the different age categories (under 50 and 50+), the death rate was lower in the vaccinated group. Figure 1 Data from Public Health England, June 2021 This is an example of Simpson’s paradox (Pearl & Mackenzie, 2018). It arises here because most vaccinated people were in the 50+ category where most deaths occur. Specifically: a) a much higher proportion of those aged 50+ were vaccinated compared to those aged <50; and b) those aged 50+ are much more likely to die. So, as shown in Figure 2(a), ‘age’ is a confounding variable. While it is reasonable to assume that death is dependent on age, in a proper RCT to determine the effectiveness of the vaccine we would need to break the dependency of vaccination on age as shown in Figure 2(b), by ensuring the same proportion of people were vaccinated in each age category. 1 Some participants and sites were unblinded and non-randomised and others were effectively unblinded when they received paracetamol prior to jab 2 https://probabilityandlaw.blogspot.com/2021/06/simpsons-paradox-in-interepretation.html
3 Figure 2 Causal model reflecting the observed data The Appendix demonstrates how this causal model, and Bayesian inference, can both explain the paradox and avoid it (by simulating an RCT). Using the model in Figure 2 (b), which avoids the confounding effect of age, we conclude (based only on the data in this study) that the (relative) risk of death is four times higher in the unvaccinated (0.417%) than the vaccinated(0.104%), meaning the absolute increase in risk of death is 0.313% greater for the vaccinated. An excellent article by Jeffrey Morris3 demonstrates the paradox in more detail using more recent data from Israel. Clearly confounding factors like age (and also comorbidities) must, therefore, always be considered to avoid underestimating vaccine effectiveness data. However, the conclusions of these studies are also confounded by failing to consider non-Covid deaths, which will overestimate the safety of the vaccine if there were serious adverse reactions. In fact, there are many other confounding factors that can compromise the results of any observational study into vaccine effectiveness (Krause et al., 2021). By ‘compromise’ we mean not just over- or under-estimate effectiveness, but – as in the example above – may completely reverse the results if we fail to adjust even for a single confounder (Fenton, Neil, & Constantinou, 2019). In particular, the following usually ignored confounding factors will certainly overestimate vaccine effectiveness. These include: • The classification of Covid19 deaths and hospitalizations. For those classified as Covid19 cases who die (whether due to Covid19 or some other condition), there is the issue of whether the patient is classified as dying ‘with’ Covid19 or ‘from’ Covid19. There may be differences between vaccinated and unvaccinated in the way this classification is made. The same applies to patients classified as Covid19 cases who are hospitalized. • The number of doses and amount of time since last dose used to classify whether a person has been vaccinated. For example, any person testing positive for Covid19 or dying of any cause within 14 days of their second dose is now classified by the CDC as ‘unvaccinated’ (CDC, 2021). While this definition may make sense for determining effectiveness in preventing Covid19 infections, it may drastically overestimate vaccine safety; this is because most serious adverse reactions from vaccines in general occur in the first 14 days (Scheifele, Bjornson, & Johnston, 1990; Stone, Rukasin, 3 https://www.covid-datascience.com/post/israeli-data-how-can-efficacy-vs-severe-disease-be-strong-when-60-of-hospitalized-are-vaccinated
4 Beachkofsky, Phillips, & Phillips, 2019) and the same applies to Covid19 vaccines (Farinazzo et al., 2021; Mclachlan et al., 2021). There is also growing evidence that people hospitalized for any reason within 14 days of a vaccination are classified as unvaccinated and, for many, as Covid19 cases4. • The accuracy of Covid19 testing and Covid19 case classification. These are critical factors since there may be different testing strategies for the unvaccinated compared to the vaccinated. For example, in the large observation study of the Pfizer vaccine effectiveness in Israel (Haas et al., 2021) unvaccinated asymptomatic people were much more likely to be tested than vaccinated asymptomatic people, resulting in the unvaccinated being more likely to be classified as Covid19 cases than vaccinated5. Even if we wish to simply study the effectiveness of the vaccine with respect to avoiding Covid infection (as opposed to avoiding death or hospitalization) there are many more factors that need to be considered than currently are. To properly account for the interacting effects of all relevant factors that ultimately impact (or explain) observed data we need a causal model such as that in Figure 3. Figure 3 Causal model to determine vaccine effectiveness 4 https://www.bitchute.com/video/lXrcpFe4V4U2/ 5 https://probabilityandlaw.blogspot.com/2021/05/important-caveats-to-pfizer-vaccine.html
5 As in the simple model of Figure 2, the nodes in the model shown in Figure 3 correspond to relevant factors (some of which relate to individuals – like age, and some of which relate to the population – like whether lockdowns are in place) and an arc from one node to another means there is a direct causal/influential dependence in the direction of the arc. For example: younger people – and those who have immunity from previous Covid infection – are less likely to be vaccinated than older people; older people are more likely to have comorbidities and more likely to have symptoms if they are infected. However, while those factors and relationships are widely considered in observational studies, most of the other factors in the model are not. The first thing to note is that the model makes clear the critical distinction between whether a person is Covid19 infected (something which is not easily observable) and whether they are classified as a Covid19 case (i.e. the ones who are recorded as cases in any given study). The latter depends not just on whether they are genuinely infected but also on the accuracy of the testing and whether they are vaccinated. If (as in the Israel study described above) the unvaccinated are subject to more extensive (and potentially inaccurate) testing, then they are more likely to be erroneously classified as a case. The model also makes clear the critical distinction between those who have been vaccinated (at least once) and those classified as vaccinated in the study. The latter depends on the number of doses, time since last dose, and whether the person tests positive. Moreover, whether a person gets more than one dose will depend on whether they suffered an adverse reaction first time; those who do and who do not get a second dose are generally classified as unvaccinated – and this will compromise any studies of risk associated with the vaccine. Indeed, even the results of randomized controlled trials were compromised both by ‘removing’ those who died within 14 days of the second vaccination and ‘losing’ many subjects after the first dose6. The causal model makes clear that a person cannot become infected with the virus unless they come into contact with it. The latter depends not just on age, ethnicity and profession (so young people who live, work and travel in crowded environments are more likely to come into contact with the virus as are any people in a hospital environment) but also on changing population factors like lockdown restrictions in place and current population infection rate. Assuming a person comes into contact with the virus, whether they get infected depends on whether they have natural immunity and whether they are vaccinated. If we had relevant data on all of the factors in the model then, as in the case of the simple model in the Appendix, we can capture the probabilistic dependence between each node and its immediate parents, and then use Bayesian inference to determine the true effect of vaccination. In principle, this enables us to properly explain all observed data, adjust for all confounding factors, and provide truly accurate measures of effectiveness. The problem is that several key variables are generally unobservable directly while many of the easily observable variables are simply not recorded. While we can incorporate expert judgment with observed statistical data to populate the model, this can be extremely complex and subjective. Moreover, if you think the model is already very complex, then it should be noted that it is far from fully comprehensive. Even before we consider all the additional factors and relationships needed to consider the outcomes of hospitalization and death (and the accuracy of reporting these), the model does not take account of: different treatments given; different morbidities and lifestyle choices; seasons over which data are collected; different strains of the virus; and many other factors. Nor does it account for the fact that all observational data are biased (or ‘censored’) in the sense that it only contains information on people who are available for the study; so, for example, studies in particular countries will largely contain people of a specific ethnicity, while all studies will generally exclude certain classes of people (such as the 6 Some of the covid vax trials were unblinded, others were only single-blinded. Yet more were non-randomised and others were accidentally unblinded when the treatment recipients were given paracetamol prior to their covid jab
6 homeless). This means that, while such studies could be useful in determining effectiveness at a ‘local’ level, their conclusions are not generalizable. Indeed, they may are completely unreliable because of another paradox (called collider or Berkson’s paradox) unless we have explicitly adjusted for this as described in (Fenton, 2020). Given the impossibility of controlling for all these factors in randomized trials, and the overwhelming complexity of adjusting for them from observational data there is little we can reliably conclude from the data and studies so far. And we have not even mentioned the general failure of these studies to consider the impact and trade-offs of safety on effectiveness. So, what can we do about this mess? We believe there is an extremely simple and objective solution: if we ignore the cost of vaccination, then ultimately we can all surely agree that the vaccine is effective overall if there are fewer deaths (from any cause) among the vaccinated than the unvaccinated. This combines both effectiveness and safety since it encapsulates the trade-off between them. It is not perfect, because there could be systemic differences in treatments given to vaccinated and unvaccinated7, but it completely bypasses the problem of classifying Covid19 ‘cases’ which, as we have noted, compromises all studies so far. So, provided that we can agree on an objective way to classify a person as vaccinated (and we propose that, for this purpose, the fairest way is to define anybody as vaccinated if they have received at least one dose), then all we need to do is compare all-cause mortality rates in different age categories of the vaccinated v unvaccinated over a period of several months8. A recent analysis does indeed look at all-cause deaths in vaccinated and unvaccinated (Classen, 2021). The study shows that, for all three of the vaccines for which data were available, all-cause deaths is significantly higher in the vaccinated than the unvaccinated. However, this study did not account for age and hence its conclusions are also unreliable. We could immediately evaluate the effectiveness to date of vaccines in the UK by simply looking at the registered deaths since the start of the vaccination programme in December 2020. All we need to know for each registered death is the person’s age and whether they received at least one dose of the vaccine before death. Although a longer period would, of course, be better it is still sufficiently long to show a real effect if the vaccines work as claimed and if Covid19 is as deadly as claimed. Moving forward we should certainly be collecting this simple data, but our concern is that (in many countries) the ‘control group’ (i.e. unvaccinated) may soon not be large enough for such a simple evaluation. 7 There are multiple anecdotal reports that Australian hospitals are now giving ivermectin only to vaccinated patients 8 https://probabilityandlaw.blogspot.com/2021/06/why-all-studies-so-far-into-risks-andor.html
7 References CDC. (2021). COVID-19 Breakthrough Case Investigations and Reporting | CDC. Retrieved September 15, 2021, from https://www.cdc.gov/vaccines/covid-19/health-departments/breakthrough-cases.html Classen, B. (2021). US COVID-19 Vaccines Proven to Cause More Harm than Good Based on Pivotal Clinical Trial Data Analyzed Using the Proper Scientific Endpoint, “All Cause Severe Morbidity.” Trends in Internal Medicine, 1(1), 1–6. Retrieved from https://www.scivisionpub.com/pdfs/us-covid19-vaccines-proven-to-cause-more-harm-than-good-based-on-pivotal-clinical-trial-data-analyzed-using-the-proper-scientific–1811.pdf Farinazzo, E., Ponis, G., Zelin, E., Errichetti, E., Stinco, G., Pinzani, C., … Zalaudek, I. (2021). Cutaneous adverse reactions after m?RNA COVID?19 vaccine: early reports from Northeast Italy. Journal of the European Academy of Dermatology and Venereology, 35(9), e548–e551. https://doi.org/10.1111/jdv.17343 Fenton, N. (2020). Why most studies into COVID19 risk factors may be producing flawed conclusions – and how to fix the problem. ArXiv. https://doi.org/http://arxiv.org/abs/2005.08608 Fenton, N. E., Neil, M., & Constantinou, A. (2019). Simpson’s Paradox and the implications for medical trials. Retrieved from http://arxiv.org/abs/1912.01422 Folegatti, P. M., Ewer, K. J., Aley, P. K., Angus, B., Becker, S., Belij-Rammerstorfer, S., … Oxford COVID Vaccine Trial Group. (2020). Safety and immunogenicity of the ChAdOx1 nCoV-19 vaccine against SARS-CoV-2: a preliminary report of a phase 1/2, single-blind, randomised controlled trial. Lancet (London, England), 396(10249), 467–478. https://doi.org/10.1016/S0140-6736(20)31604-4 Haas, E. J., Angulo, F. J., McLaughlin, J. M., Anis, E., Singer, S. R., Khan, F., … Alroy-Preis, S. (2021). Impact and effectiveness of mRNA BNT162b2 vaccine against SARS-CoV-2 infections and COVID-19 cases, hospitalisations, and deaths following a nationwide vaccination campaign in Israel: an observational study using national surveillance data. Lancet (London, England), 397(10287), 1819–1829. https://doi.org/10.1016/S0140-6736(21)00947-8 Krause, P. R., Fleming, T. R., Peto, R., Longini, I. M., Figueroa, J. P., Sterne, J. A. C., … Henao-Restrepo, A.-M. (2021). Considerations in boosting COVID-19 vaccine immune responses. The Lancet, 0(0). https://doi.org/10.1016/S0140-6736(21)02046-8 Ledford, H., Cyranoski, D., & Van Noorden, R. (2020). The UK has approved a COVID vaccine — here’swhat scientists now want to know. Retrieved from https://www.nature.com/articles/d41586-020-03441-8?utm_source=Nature+Briefing&utm_campaign=597ee8dba8-briefing-dy-20201203&utm_medium Mclachlan, S., Osman, M., Dube, K., Chiketero, P., Choi, Y., & Fenton, N. (2021). Analysis of COVID-19 vaccine death reports from the Vaccine Adverse Events Reporting System (VAERS) Database Interim: Results and Analysis. Retrieved from http://dx.doi.org/10.13140/RG.2.2.26987.26402 Pearl, J., & Mackenzie, D. (2018). The book of why?: the new science of cause and effect. New York: Basic Books. Polack, F. P., Thomas, S. J., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., … C4591001 Clinical Trial Group. (2020). Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. The New England Journal of Medicine, 383(27), 2603–2615. https://doi.org/10.1056/NEJMoa2034577 Scheifele, D. W., Bjornson, G., & Johnston, J. (1990). Evaluation of adverse events after influenza vaccination in hospital personnel. CMAJ?: Canadian Medical Association Journal = Journal de l’Association Medicale Canadienne, 142(2), 127–130. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/2295029 Singh, J. A., Kochhar, S., Wolff, J., Atuire, C., Bhan, A., Emanuel, E., … Upshur, R. E. G. (2021). Placebo use and unblinding in COVID-19 vaccine trials: recommendations of a WHO Expert Working Group. Nature Medicine, 27(4), 569–570. https://doi.org/10.1038/s41591-021-01299-5 Stone, C. A., Rukasin, C. R. F., Beachkofsky, T. M., Phillips, E. J., & Phillips, E. J. (2019). Immune?mediated adverse reactions to vaccines. British Journal of Clinical Pharmacology, 85(12), 2694–2706. https://doi.org/10.1111/bcp.14112
8 Appendix The prior probabilities based on the study data are shown in Figure 4 Figure 4 Causal model as a Bayesian network with probability tables taken from the observed data This results in the so-called marginal probabilities shown in Figure 5. Figure 5 Marginal probabilities (Age and Vaccinated are rounded to 0 decimal places) By entering observations on Age we can see the overall effect on probability of vaccinated and death as shown in Figure 6.
9 Figure 6 Overall impact of age on probability of vaccinated and death However, the real power of the Bayesian network comes in the backward inference shown in Figure 7 that enables us to determine the impact of vaccination status on age as well as death. Figure 7 Impact of vaccination status on age and death Here we see (as noted in the original data) that the vaccinated are four times more likely to die than the unvaccinated. But this is explained by the vaccinated being much more likely to be 50+
10 Next we use the model to see the impact of vaccination respectively on those aged <50 and those aged 50+. In Figure 8 we see that, for those aged <50 there is a small decrease on probability of death among the vaccinated. Figure 8 Impact of vaccination on those age 50 year-olds, women and those with prior symptomatic/confirmed COVID-19. Adults receiving heterologous schedules on clinical … [Show full abstract]Read more
Preprint
Low seropositivity and sub-optimal neutralisation rates in patients fully vaccinated against COVID-1…
July 2021
Thomas Fox
Thomas FoxAmy A. KirkwoodLouise Enfield[…]Emma Morris
Patients with haematological malignancies are at increased risk of severe disease and death from COVID-19 and are less likely to mount humoral immune responses to COVID-19 vaccination, with the B cell malignancies a particularly high-risk group. Our COV-VACC study is evaluating the immune response to COVID-19 vaccination in patients with B cell malignancies. Eligible patients were either … [Show full abstract]Read more
Last Updated: 17 Sep 2021
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(3 of 3)
Contrary to public perception, RCTs are not infalluable and they are the basis of approximately 6% of all medical trial work, historically. Using modeling to correct for confounders can help root out some obvious confounders with known rates of occurence, but too much authority is placed in a single persons judgement on guessing, even if their input in not intentionally slanted. By collecting and publishing actual data, full discussions on the topic can be had in multiple symposiums on the subject of Covid, not just by the doctors either. Nurses and the clinical techs each have their own associations in which they discuss and analyses data and their input is also critical to patient care. No two clinicians ever agree on any topic and a spirited discussion can often be very enlightening – steel sharpens steel. But these steps require the return of freedom of speech to do so. It also requires that the studies be readily and fairly evaluated and printed regardless of their findings. Currently, those articles that don’t support the vax agenda are kicked down the road for months to over a year waiting to be published, whereas the articles supporting the vax agenda are published freely. If the censorship is not corrected, we will have science by fiat and then more and more people will die, needlessly. But they will rig their RCTs to show the public that the deaths will have no relationship to their product. Hope this helps. Let me know if I lost you somewhere.
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The use of the 14day delay for the vaccines is an example of how this leeway can be ignored, misunderstood, or abused. When a vaccine is administered, it is important that you not overstate the safety and simultaneously not understate the efficacy. It is an important balance to strike. The 14day delay between the vaccine administration and counting an infected person is to maximize the efficacy of the vaccine because presumably the antibodies are not available for that time. In truth, 14days is a long period to claim here. The antibodies should begin to be available somewhere around day 8 more or less, so 14 is excessive in my opinion even to their intended purpose. But this ignores the fact that vaccines diminish the immune system, and this vaccine greatly diminishes it due to the overwhelming immune reaction that results in the many inflammatory diseases shown in VAERS/Yellowcard. So in the 14day period in question many people could die due to Covid, or anything really, due to the immune suppression associated with the vaccination. But the immune suppression is directly associated with the vaccination. By placing someone who becomes ill in this 14day period in with the unvaccinated disadvantages the unvaccinated category because of the vaccination was the initiating cause of the reduced immune response. This definition of considering the vaccinated subject as unvaccinated for upto 2wks post-vaccination is unheard of in medical drug trials. When you are vaccinated you are included in the vaccinated group, period. Also, if you are conducting an RCT, the two groups are randomized and matched perfectly. If you vaccinate 100 people and 6 become ill within a week and are transferred to the placebo group, it screws up the matching, the randomization and the balance of the study. So, without discussing the statistical models they employ, this is the gist of what the article discusses, albeit with my own examples.
My opinion on this topic is to ignore RCTs. They cost a lot, it requires a complete dependency on the govt and Pharma and we need data today, not in two yrs. And one study is just one study, ie nothing can be certified by its results alone as the vaccines have demonstrated routinely. NIH just funded 4 studies on the irregular menstrual bleeding in women and very young girls following vaccination and we won’t know anything til next year at the earliest, and those responsible for the studies are either govt study groups or highly financed groups associated with the govt. All matter of data can be collected over the next couple months. Some will be good and some will be crap. The preponderance of the data, however will be best known when it is collected in massive amounts in small trials which raise further questions and those are pursued in new studies which will both confirm the results of the previous study and also gain further info on the subject. This is how all medical knowledge has been collected and assessed over the years.
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@Adam
So, the issue that distinguishes RCT from the cheap and easy observational studies is the topic of confounding factors. In a study that examines the relationship between two variables, such as between smoking a new cigarette and low blood pressure in the smokers. An observational study might not have any placebo group, and if you strictly consider the fact that those people smoking this new cigarette have lower blood pressure than found in people smoking other brands, you could be missing a confounding factor that explains the lower blood pressure that has nothing to do with the new cigarette at all. A confounding factor in this example might be that subjects that agreed to be included in the study are exclusively by 20yr old women – who have low blood pressure, regardless of the cigarettes. The presumption of association between the cigarettes and low blood pressure is confounded by not examining other factors about the person that might need to be teased out, such as age and sex. The benefit of RCTs is that if you do a really really good job on the matching and randomizing between the test subjects and the placebo group, you can ignore this complication, ie in the above example, there would be equal numbers of men, women, different races and socioeconomic groups, education levels, etc in both a test group and a placebo group. If this is not clear let me know because it is the basis of everything that follows.
RCTs are very time consuming, upto 2yrs to plan and execute, and very expensive, upto and in excess of $1million. Observational studies are cheap, sometimes free, but they don’t use randomization to match the test group with the placebo group. To get around this you can use what you know about certain confounding issues to eliminate the confounding issue using statistical models and known relationships between the people in the test group. For example, in Israel, the Heredi are known to be over represented in the unvaccinated group. Increased heart disease is also known to exist in their community, disregarding Covid or the vax. So you could find an expected rate of heart disease in the Heredi and use that known rate to explain and eliminate the bias of an elevated heart disease when looking at the affects of either their receiving or not receiving the vax. A problem with this is that it places a lot of subjective input into the hands of those conducting the study. It also assumes that the person conducting the study can know all the confounding issues that might be related to the outcome of the study, which they can’t. Lastly, it assumes that there are known data about every confounder, eg the level of heart disease in a given homeless community – most confounders have no known data for such things. For this last issue, where the data does not exist, it requires that the individual who is administering the study must guess at an appropriate rate for the unknown confounders, which gives them a lot of power to steer the outcome of the study. In truth even for the confounders where the data is known, the rates have various estimates based on differing sources, so there is leeway there too. It is a sad state of affairs, that politics has invaded the scientific community as it has because it is itself a confounder that can not be eliminated, so we must always watchful for its impact.
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Peloni, please read this article and “translate” it for us, with your comments and reactions. Thanks.
@Bear
From the trial design listed in the study you cited:
There is a built in bias in this study that you shared that removes any input from the consequence of the vaccine lowering the immunity of the subjects being vaccinated. The vaccine overwhelms the immune system which is how the antibody titer lifts so dramatically, but in doing so, the focus of the immune system on the vaccine lays the normal defenses down which allows for what is known as an opportunistic infection, ie infections from Covid as well as other pathogens which would normally be prevented by a normally functioning and unchallenged immune system. By employing these bizarre definitions of only counting a vaccine casualty beyond the window in which the vaccine would limit the immune system(12 days or more) completely ignores the risk associated with the vaccine. Placing the burden of such victims with the unvaccinated renders this study pretty useless without the documented numbers who fall into this 12-day period. You can’t pretend they weren’t vaccinated or that their immune systme wasn’t challenged or reduced in that 12day window. In other words, if there were virtually no casualties in that window, then it would not matter, and if they cared to separate these numbers it would be useful to discuss further. But given the fact that they chose to employ this special definition and gained these remarkable results, suggests it is another fudging of data to support their political goals. Adam shared an article discussing this among other topics, but it really requires no sourcing as lowered immunity following the inoculation is very well known fact, which, if ignored, renders this study as fairly useless beyond a political tool to support political agendas.
Any positive post on the kill shot shows the mass psychosis is REAL.
Link to study I referenced in post below is https://www.nejm.org/doi/full/10.1056/NEJMoa2114255?query=featured_home
But a separate study conducted at Sheba Medical Center in Ramat Gan, outside Tel Aviv, has stoked optimism as to the amount of time for which the booster shot retains its protection.
The study found that the antibody levels a week after the third COVID-19 vaccine dose was administered to its staff were ten times higher than their levels a week after the second dose was administered.
The link to the publication of the original Study is below from the New England Medical Journal.
Protection of BNT162b2 Vaccine Booster against Covid-19 in Israel.
Our findings give clear indications of the effectiveness of a booster dose even against the currently dominant delta variant.
https://www.nejm.org/doi/full/10.1056/NEJMoa2114255?query=featured_home
Israeli study of 3rd booster found infection rate was at least 5 times lower in the group that had received the booster shot, the Health Ministry said in a statement.
The research includes data from more than 1 million Israelis. Among those who hadn’t received a booster shot despite being eligible, there were 4,439 confirmed infections, including 294 serious patients. Among those who received the booster at least 12 days previously, there were 934 infections including 29 serious cases.
I can’t imagine that a valid study on the effects of the 3rd jab can be conducted so soon after vaccinating a million people.
This just cannot be done.
The rest of the article can be read at :
https://childrenshealthdefense.org/defender/natural-immunity-cdc-vaccine-political-narrative/
Very interesting…The science continues to appear to be not quite as settled as the CDC would like the public to believe.
So, the Israeli 3rd jab gambit paid off? Let’s hope so. It was never based on data, good science, marginal input or any form of proper provenance. As the Director of the CDC stated it was “based on hope for now, we don’t have data, yet.” Of course the data the American CDC was waiting on was the thumbs up or down based on the reckless testing of the entire nation of Israel which was being employed as a nation sized laboratory study with a 3rd dose of an untested experimental gene therapy being employed as a vaccine against a very treatable disease. At least the use of the first two jabs was based on some data, though there was minimal to no data collected on a host of topics which are required by both vaccines and gene therapies including the mutagenic studies, and teratological studies among other very significant topics. What they did have was the very limited evidence that, based on a few hundred challenged cases, demonstrated that there was no statistical difference between the test subjects and the placebo groups. It wasn’t much but it was something.
But for this 3rd jab gambit, there was no data, nothing – only hope. So with the combined support of PM Bennett, Dual PM Lapid and Former PM Netanyahu, they placed the fate of their entire nation on a hope that the third shot would not leave them all dead, diseased or sterile. The lethal irony unfolded as, even after the Israeli nation accepted their part in this uncharted game of risk, the CDC and the FDA still refused to support the 3rd jab program in their own nation of America, so uncertain were they that the Israelis hadn’t just jumped off a cliff with a piece of swiss cheese as its only parachute. So the shareholders demanded they have some data on the books which has since created a rift within the medical community and the White House and a civil war within the FDA. It is, however, terribly unfortunate that there are more significant risks than angering the dreaded CCP member who is now drooling on the US nuclear codes even as he slumbers in the Oval Office.
The immune system is an intimidatingly complex system of multilevel interdependent variables. This 3rd jab policy which was established upon the foundation of hope which was itself based on a balance of equal parts of ignorance, hubris and naivete. So it is good that there seems there might be a benefit gained by it, for the moment, as there was a great deal wagered on this throw of the dice, and that wager will remain under threat for some time to come. There are long term consequences that can only be guessed at as the authorities scoff at such difficult topics as autoimmune disorders and high load tolerance which each lay as a possible outcome, as well as other undreamt miseries. So, let us pray that hopes bear out as well placed, but faith holds very different facets than knowledge, and, hence, no one can know the coming consequence of any of this. So let us all continue to hope for good news in the coming years, as hope is all we have to support this house of cards masquerading as science.
https://www.timesofisrael.com/study-covid-booster-recipients-20-times-more-protected-against-serious-illness/
Another voice of truth extinguished. https://m.youtube.com/watch?v=BnN1VeSDzSY&t=70s
Nobel prize winner, Dr. Carry Mullis, inventor of the PCR test, said the test was NOT for diagnosis, its cycles could be raised to show the presence of ANY molecule, which the nefarious med community did, and he dared to expose Dr. Anthony Fauxi. Well, too bad, he died suddenly last year in a hospital. Convenient, eh, fauxi?
This is how Israel deals with a dissenter!
https://www.rt.com/news/534785-israel-coronavirus-shaulian-poisoning-death/
The Israeli study, combined with other increasingly surfacing evidence, may raise questions about why the US government is imposing forced vaccinations on Americans working for large companies despite evidence indicating that vaccinated individuals are overwhelmingly more likely to catch, spread, and be hospitalized by COVID-19. https://nationalfile.com/israeli-study-fully-vaxxed-are-27-times-more-likely-to-get-covid-compared-to-people-with-natural-immunity/
Bear Klein oh, yeah? Are you high? What idiot would post that kill shots work?
https://nationalfile.com/israeli-study-fully-vaxxed-are-27-times-more-likely-to-get-covid-compared-to-people-with-natural-immunity/
So natural immunity works that is good news. Vaccine works that is also good news.
Do you believe you have a right to live? Bennet doesn’t. You can get this on amaZon. https://www.bitchute.com/video/t7IDgJJitaVF/ Take matters into your own hands and live!
https://www.bmj.com/content/374/bmj.n2101/rr-0
This new definition is evidence of the sinister nature of the CDC’s sponsorship of the vaccines.