Why was this study retracted?
Dr. Robert Malone made factual statements about the Risk-Benefit ratio of mRNA vaccines.
- Malone said he has a “bias that the benefits probably don’t outweigh the risks” for younger Americans receiving the vaccine, but said the risk-benefit analysis is not being done.Malone said there is a “pretty good chance” that the risk-benefit ratio for those 18 years old and younger “doesn’t justify vaccination in these very young adults.”
- “According to Dr. Malone, a pioneer of mRNA technology, his LinkedIn account was deleted without notice after he expressed on his profile his concern for the risks of mRNA COVID vaccines. Malone said that vaccine risks are being downplayed by public health bodies. His statements were deemed against the misinformation policies of social media.”
- A representative from Microsoft-owned LinkedIn told Malone that his account had violated its user agreement by posting “misleading or inaccurate information” about vaccines and COVID-19, according to The Epoch Times.
- Linked-In later re-instated Dr. Malone’s account (1 week suspension).
Ripple effect to the study:
- Risk-Benefit Ratio: world looking to the USA for this info. Risk of COVID death vs. adverse event caused death. That calculation does not favor mRNA vaccine.
- This generated push back from academics and people on LinkedIn/Twitter. (quoting Dr. Malone)
- They wrote directly to the journal to get the study withdrawn (“The Safety of COVID-19 Vaccinations – We should Rethink the Policy“).
Randomized Control Trials
Reference: Do Masks Work? A Review Of The Evidence
- The most reliable way to test medical interventions (the “gold standard“).
- A randomized controlled trial divides participants into different groups on a randomized basis.
- At least one group receives an “intervention,” or treatment, that is generally tested against a control group not receiving the intervention.
- The twofold strength of an RCT is that it allows researchers to isolate one variable—to test whether a given intervention causes an intended effect—while at the same time making it very hard for researchers to produce their own preferred outcomes.
- Intention-to-treat analysis avoids bias and “preserves the benefits of randomization, which cannot be assumed when using other methods of analysis.” – Eric McCoy, M.D., University of California.
“Multivariable analysis only adjusts for measured confounding”
– Marlies Wakkee, M.D., Ph.D. at Erasmus University Medical Center in the Netherlands
… that which a researcher decides is worth examining. (Confounders are extra variables that affect the analysis; for example, eating ice cream may be found to correlate with sunburns, but heat is a confounding variable influencing both.)
- There may be variables that are discarded by the researcher.
- Convenience? Money? Time? Funding source? Personal agenda?
- Some variables may be known but can’t be measured.
- There will always be variables which exist but are unknown to present day science.
- Do Masks Work? A Review Of The Evidence
- Susceptible to “cherry-picking”—as researchers hunt for anything showing statistical significance—or to being swayed by random chance.
- In one famous example, aspirin was found to help prevent fatal heart attacks, but not in the subgroups where patients’ astrological signs were Gemini or Libra.
- Departs from randomization by basically allowing participants to self-select into, or out of, an intervention group.
- McCoy writes, “Empirical evidence suggests that participants who adhere [to research protocols] tend to do better than those who do not adhere, regardless of assignment to active treatment or placebo.”
- Per-protocol analysis is more likely to suggest that an intervention, even a fake one, worked.
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