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Comment by zyxzevn | Hacker News Reader

The experiments were setup to pass, so we are seeing record high numbers of successes.

A lot of companies are specialized in hacking the standards for testing. Depending on the company they use some of these tricks: These are all small sample sizes (around 100) picked from huge numbers of tested people (10s of thousands). They look at short term results, long term effects failed dramatically in animal testing. They use tests that can give false positives (PCR>26 cycles), and/or false negatives. Or just look at symptoms that are similar to flu. They do not have a proper (long term) placebo group (why not also use untreated patients as well). Other factors with huge impact as health, habits, food, Vitamin-D.

Big pharma wants to collect "their" billions for their medicine. Which is why we cant have nice things. (Or cheap medicine that do work well).

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These are pre-registration trials, which is a mechanism specifically designed to avoid this kind of cheating. Your comment also contains other, specific false statements:

> long term effects failed dramatically in animal testing.

That is incorrect.

> They use tests that can give false positives (PCR>26 cycles)

This false claim about how PCR works is a deliberate lie spread by conspiracy theorists. This is emphatically not how high cycle numbers of PCR are used.

> Or just look at symptoms that are similar to flu.

What does that mean?

> They do not have a proper (long term) placebo group (why not also use untreated patients as well).

They do that.

> Other factors with huge impact as health, habits, food, Vitamin-D.

That’s why you use large, randomised cohorts.

> Big pharma wants to collect "their" billions for their medicine.

I’m in favour of socialising big pharma companies. But this claim is still bullshit. Pharma companies can hike prices for working medication. They don’t need to invent fake medication that will be exposed in the long run and leads to company-destroying lawsuits.

This touches on a question I had. Out of the tens of thousands of people vaccinated in countries where COVID runs rampant, how were there only 90ish people in the control group? I would have expected thousands.

Both groups have (approximately) the same size, in the tens of thousands. The first evaluation is done when 90 or so cases have been confirmed, not knowing which group they belong to. The blinding of these subjects is then unsealed, in this case revealing that 90 of the 95 confirmed infections occurred in the control group. This distribution means the vaccine has been highly effective at preventing infection.

Sorry, I meant just 90 people infected (obviously the vaccine arm has much fewer), but after running the numbers that's actually about right for the prevalence rate we see in the US.

This isn't the final evaluation. There will be another at ~150 cases. The numbers may seem small, but this is enough for the purpose of deciding for or against approval. Anything over 80% effectiveness would likely be approved, so it doesn't matter whether the true value (insofar one exists) is 90%, 95%, or something in between. It's useful regardless.

Oh, I didn't know that, thanks. I'm sure it's enough for statistical significance given the difference, I was just wondering why there were so few cases in 30k people, but it looks like that's just the normal case rate.

Thousands (30) of people are in the trial, half were assigned randomly to the control group. So far 95 people in the trial have caught COVID and, when they unblinded the data, they discovered that 90 of those infections where in the control group. Since participants were randomly assigned into the test group vs the control group and so both groups should have the same amount of exposure, this is a strong signal that the vaccine was effective. Here's an article about Moderna's trial with a link to their 135 page (!) design doc https://www.livescience.com/moderna-vaccine-trial-protocol.h...

My question was "why did only 90 out of 15000 get infected", but the data shows that's about what we'd expect, thanks.

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