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Direct link to the peer-reviewed CDC report: R. Link-Gelles, et al., Early Estimates of Bivalent mRNA Booster Dose Vaccine Effectiveness in Preventing Symptomatic SARS-CoV-2 Infection Attributable to Omicron BA.5– and XBB/XBB.1.5–Related Sublineages Among Immunocompetent Adults — Increasing Community Access to Testing Program, United States, December 2022–January 2023, MMWR Morb Mortal Wkly Rep. (25 January 2023)

Notes:

• Vaccine effectiveness was compared to fully vaccinated individuals (2-4 monovalent doses) with no bivalent booster dose.
• Protection was reduced in older groups: 40% effective in adults ages 50 to 64 and 43% effective in adults 65 and older.
• The XBB sublineage accounted for >50% of sequenced lineages in the Northeast by December 31, 2022, and 52% of sequenced lineages nationwide as of January 21, 2023.
• Tests from persons who reported a positive SARS-CoV-2 test during the preceding 90 days were excluded to avoid analyzing multiple tests from the same infection.
  • Statistical power was not adequate to stratify by presence of prior infection >90 days earlier.
• All persons should stay up to date with recommended COVID-19 vaccines, including receiving a bivalent booster dose when they are eligible.

48% is much better than 0%... even half of prevented cases of Anything is a benefit... but I wonder how long it will take to see numbers above 70%

That would likely require an updated vaccine to account for mutations in the latest predominant variants.

Probably. I'm sure it will happen sooner than not at all

the real question is how much protection it offers against the hospitalization/death risk, and the long-term symptoms risk

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Still too early to know for the XBB/XBB.1.5 subvariant:

>Officials said there isn’t enough data yet to know how well the updated boosters protect against more severe disease, hospitalization and death. But they expect that the updated boosters will provide higher protection against these outcomes.

Worth noting the current study also assessed bivalent vaccine effectiveness against symptomatic BA.5 infection at 52%. A study conducted between September and November 2022, when BA.5 was dominant, found the bivalent dose had 57% effectiveness against hospitalization compared with no vaccination, 38% compared with monovalent vaccination only (last dose 5–7 months earlier), and 45% compared with monovalent vaccination only (last dose ≥11 months earlier). It wouldn't be surprising to see similar results for XBB/XBB.1.5 once they're available.

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Assuming the remaining 52% is coming from mutation and not time since booster or other factors.

yes its definitely harder to measure. anyway i only meant the present result is only the first half of the answer

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It's good to remember that vaccination coverage is just as important as efficacy.

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Unfortunately only 15.3% coverage for the bivalent booster dose.

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That’s also 48% to prevent any symptoms. A very mild case, like the sniffles, would still be “symptomatic”.

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I'm quintuple vaxxed and intend to continue collecting the whole set.

How do they know the statistics for people who didn’t get Covid because they got the vaccine?

I’d be on the monthly vaccine plan if there was one.

Honestly. I'm immuno-compromised and at the height of lockdowns I would have let them inject me with bone marrow and toast crumbs if it would have helped.

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They compare similar populations, mathematically control the variables, and see if there's a difference. It's all explained in detail in the study. It's also excruciatingly reviewed by other scientists for errors in a peer review before it's published and can be referenced in other studies/experiments

Wait 48%? Like by itself or what if this is your 4th shot?

That actually makes me feel a little better! I can deal with a cold

Novavax blows this out of the water.

This is like playing with toys when we have Novavax that is still working and at a much higher level.

So what is that in terms of actual incident rates?

Medical interventions should be based on a cost/benefit analysis. If costs are reported as incident rates but benefits aren't, how can I compare the two adequately?

So what were the actual incident rates for the different groups? That seems like pretty valuable information for any cost/benefit analysis, right?

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So where is the Novavax studies against XBB1.5?

Still better than the flu shot most seasons then.

There's no material number of people besides newly old enough children who are getting their first sequence anymore. This is boosted vs. unboosted vaccine.

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Looking at the "answer" as having two parts (1. How effective is it against symptomatic infection and 2. How effective is it against hospitalization/death) highlights one of the biggest complaints many people have about the entire conversation around COVID vaccines/boosters.

The question isn't just what benefits it provides. The question is also what costs are associated with it. To be clear, costs includes vaccine-related risks but also includes other issues as well.

Add to that the fact that "bivalent effectiveness against symptomatic infection for the first 3 months compared to persons who had previously received 2-4 monovalent boosters" leaves out a ton of the questions surrounding benefits. For example, what is the effectiveness compared to getting a standard monovalent booster at the same time? Is the bivalent actually better? If so, how much? Is that enough to offset any potential differences in risk profile? Since it looks like we might be moving towards a yearly booster program similar to what we have with the flu, what's the effectiveness look like at 1 year? Etc.

Or what about a more detailed breakdown of what "symptomatic infection" looks like? For example, when I got my first booster, I was laid out for a couple days. I was fine with that as I considered it the price to pay to avoid potentially serious consequences from catching COVID. However, it wouldn't have been worth it to prevent a "symptomatic infection" that consisted of the sniffles and a sore throat for 3-4 days.

The point I'm making is that the questions we need answers to are numerous and complex. We can't be satisfied with one or two answers that leave out tons of relevant pieces of information.

I can understand that in theory, but I'm not sure how much useful information this study gives us in that respect. Medical interventions should be based on a cost/benefit analysis. What is the cost to the intervention and what benefit do I get in return?

If the benefit of interest is preventing symptomatic infection, then the most logical cost to compare that to is the side effects associated with vaccination. Even if we ignore injection site reactions (pain, redness, swelling, etc.), there is still a 48.9% chance of systemic reaction after getting the bivalent booster, as shown here on the CDC website.

So if the vaccine is 48% effective at preventing symptomatic illness, but has a 48.9% chance of causing a systemic reaction, then does the benefit actually outweigh the cost?

The answer is that we don't know. If the distribution of specific symptoms is exactly the same and the duration is exactly the same in both scenarios, then it doesn't seem like the vaccine provides a clear net benefit. On the other hand, if the symptoms the vaccine prevented are generally more severe and of greater duration than the side effects of getting the vaccine, then the net benefits would seem to outweigh the costs.

Again though, we don't know that from the data the CDC provided here on the benefits side. More to your point, people don't really have the data to check and determine for themselves if getting the booster has a positive net benefit.

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Yearly flu shots are not boosters

>Well, one advantage for working people is that they could schedule the booster before their day off from work. But the illness itself can not be scheduled in such a fashion, forcing them to use sick days or even potentially having to go to work when they are sick.

I get how this is a benefit, but I've never heard a doctor promote a vaccine primarily because it allows you to better schedule your symptoms.

Also, there is the issue that this study only last 3 months. If the CDC is looking to move to a yearly booster model (which seems like a good possibility based on a number of data points), the real question is what does it do over a 12 month period, especially because we know that the protection granted by the monovalent booster waned with time.

I mean, if getting the vax gives you symptoms right away and then you still get symptomatic COVID 6 months down the road, is that better than just getting COVID in 2 months?

To be fair, we don't know if this is how it would look. Maybe it gives you significant protection for the full 12 months. Maybe the people that don't get it catch COVID twice in that period instead of once. The point is that we don't have the data to say with any certainty.

>And this is granting that the side effects are comparable, which seems unlikely for a vaccine without a live virus or adjuvant.

The problem is that there are factors that would tend to skew the cost/benefit analysis both for and against the booster. One of the biggest ones highlights one of the complaints people have with reporting relative effectiveness but not absolute numbers, and can probably be best illustrated by an example.

The rate of vaccine side effects applies to everyone that got the vaccine. So if 300M people get the vaccine, we end up with ~150M people with experiencing systemic side effects.

Ok, now how much symptomatic illnesses are actually prevented in that 3 month period. Some people would look at it and say:

>Well about the same number. ~48% effectiveness at preventing symptomatic illness times 300M people means ~150M symptomatic illnesses prevented.

The problem with that is that the 48% is a relative effectiveness. That means that the hypothetical calculation above that would say it prevented ~150M symptomatic illnesses only works if all 300M people would actually get symptomatic COVID in the 3 month period without the booster.

In reality, CDC reporting only shows something like ~40M cases over the past 3 months. The math is more complicated, but to simplify for the purpose of a reddit discussion, that means we are looking at a ~20M reduction in symptomatic illness over the 3 month period if everyone got boosters.

Even if the side effects of the vax skew more mild than the symptomatic diseases prevented, it would seem to be overwhelmed by the fact that vaxxing everyone would result in having ~130M more instances of symptomatic side effects from vaxxing everyone compared to reduction in illnesses prevented in the 3 months we are looking at.

The fact that not all cases are reported to the CDC would serve to prop up the "vax benefits" side and the fact that side effects are under reported would serve to prop up the "vax costs" side. How much this impacts the final analysis isn't know.

This lack of data and transparency this far into the pandemic is a major problem.

I know, which is why I said "similar" and not "exact." In the yearly booster approach to COVID, the expected process would be:

• Guess the most relevant/prevalent lineage(s) you want to protect the populace against over the next year
• Develop a vaccine that specifically targets that lineage(s)
• Recommend that people get one of these targeted vaccinations on an annual basis, with roll-out, messaging, and timing recommendations geared towards the period of the year where COVID infections are expected to be most prevalent

That program sounds pretty "similar" to what we have with flu shots, no?

The previous numbers we were seeing during the pandemic were mostly focused on severe infection, hospitalization etc - those that stated we were north of 90% less likely to be hospitalized with a severe infection - and that also included the elderly where this caps off at 49.

If we are in a 'healthy' age range, this booster will effectively halve your chances of getting a symptomatic case if COVID. I'd be interested to see how this compares to the flu shot, and it's effectiveness within that age group for the same conditions.

If I'm reading this all right, this is more or less what people would expect from a vaccine - asymptomatic infection at worst. I think we were in a unique situation in that we had broad testing during this pandemic and vaccine roll out, and previous vaccines (ie polio) were not matched with this type of broad testing. To the regular person, symptoms=sick, and it could just be a matter of perception.

Do we get asymptomatic cases of measles, mumps, polio etc that we are just unaware and our bodies just know how to handle the disease/virus from the vaccine?

I'm thinking we are seeing that these vaccines don't prevent the viruses from entering your body, but train up your immune system to recognize and take it out/contain it - and educating people on that will be good. With COVID, you can still spread it - but you reduce it's replication in your body and eliminate symptoms and therefore reduce your ability to spread it by SOME degree.

The distinction is important because people are leaning on "still getting infected and can spread it, what's the point of the vaccine" and it's usually a politically flamed reaction around those concerns. The question is reasonable if someone doesn't understand. But diving into it and explaining, if my understanding above is accurate, can be an important precursor to someone getting vaccinated and can have the same if not greater impact on society as you getting vaccinated yourself.

I did research the vaccines as I'm a primary-source-of-information kind of person prior to getting vaccinated and I think most people have a desire to understand that gets hijacked when there is a void of information/understanding by those who "just ask the questions" and conveniently avoid the answers. They prey on the idea of "you aren't getting the truth from anywhere else but here" by injecting unhealthy skepticism based in fear.

My whole point in writing this: take time to explain it if you do understand. Don't worry if it will fall on deaf ears, just do your part where you can.

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well thats what i'm saying, that this isn't the complete answer

but it's partial simply because some answers are easier to get than others. Since the results are announced as we go, of course the answers for the simpler questions will be available first

but then, very importantly, having one answer already modifies the likelihoods for what the answers will be to the other questions -- that is, the expectations we can have regarding those other questions given our knowledge of related questions and our general knowledge of infectious diseases and immunology

specifically, if we know the booster works against the XBB variant in general, then it's very likely it also helps prevent serious illness/hospitalization

>but it's partial simply because some answers are easier to get than others. Since the results are announced as we go, of course the answers for the simpler questions will be available first

While this sounds ok in theory, we run into troubles when try to apply that in the real world scenario we find ourselves in.

First, there is no clear explanation in the official report detailing what makes this particular answer easier to get than something like how effective it is at reducing hospitalizations.

For example, why exactly can't they tell us how effective it is at preventing severe disease and hospitalization? I mean, if someone is hospitalized, they are going to get tested for COVID and it is going to get recorded if that is a contributing cause to their hospitalization. The additional article that OP linked above claims they don't have enough data, but the official report, which carries more weight than the secondary article, doesn't say that and it certainly doesn't explain why they don't have that data.

Second, the reason some of these answers are hard to get are specifically because of design choices that were made by the CDC/FDA. For example, they pulled the EUA for using the monovalent vaccines as boosters as of September 1st. That means that they created the conditions that make it hard to include an additional comparison here for those that picked the monovalent booster over the bivalent.

"These answers are too hard to get right now" doesn't fly as a legit excuse if they themselves are the reason that it is hard to get the data.

>but then, very importantly, having one answer already modifies the likelihoods for what the answers will be to the other questions -- that is, the expectations we can have regarding those other questions given our knowledge of related questions and our general knowledge of infectious diseases and immunology

Ok, so support that. Use the data that we have on the 3 month protection from symptomatic disease from the bivalent boosters, combine hat with what you know about infectious diseases and immunology and give me some specific predictions on things like:

• What will be the 3 month efficacy in preventing hospitalizations/severe disease/death in the 18-49 age range for bivalent boosted vs. those with 2-4 monovalent shots but no bivalent booster? What does that translate to in absolute terms (i.e., what is the rate of hospitalization/severe disease/death per 100K individuals in each of these two groups)?
• Do the same thing, but for a one year time frame, both for symptomatic disease as well as for hospitalization/severe disease/deaths.

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> First, there is no clear explanation in the official report detailing what makes this particular answer easier to get than something like how effective it is at reducing hospitalizations.

They don't have to explain; anyone who's trained in statistics knows

It's always the same reason: you have many more people to work with; because comparatively very few people get hospitalized. It's like if you're trying to check if two dice are loaded, but there's one die you can roll every few seconds and another you can roll only once every hour

Of course if you could collect literally all data on all patients nationwide/worldwide you'd have enough cases, but one, the logistics of that would be extremely difficult to organize, and two, people in the US/Europe don't want such a totalitarian surveillance of their personal health anyway. So there's a limit to the scale of the studies that are practically feasible

None of these technicalities matter anyway. I don't think you realize just how much the cost/benefit ratio is skewed towards getting vaccinated. You're basically complaining that the weight of your car is given in pounds rather than in ounces

Perhaps this is why XBB 1.5 hasn't resulted in a massive spike in cases and hospitalization, despite it's high R0.

>They don't have to explain; anyone who's trained in statistics knows

Whenever someone starts off by trying to make it known how much smarter and better educated/trained they are, I know I'm in for some excellent analyses and good faith engagement.

>It's always the same reason: you have many more people to work with; because comparatively very few people get hospitalized.

So the reason one question is easier to answer than another is "always" because one has "many" more examples to work with? It doesn't matter how more complex one system might be, how easy or hard it is to actually observe the two systems, how easy or hard it is to make accurate measurements, etc.?

It "always" comes down to the one that has "many" more examples to work with?

>It's like if you're trying to check if two dice are loaded, but there's one die you can roll every few seconds and another you can roll only once every hour

So the first die is rolled 3,600 times more frequently than dice two. Your statistical training tells you that this means that it will "always" be easier to tell if the first dice is loaded than the second?

So what if the second dice is 10 billion times less fair/more loaded than the first dice? I would think that, even with the slower rolling speed, I'll be able to determine that the second die is loaded long before I can tell the first die is loaded.

Your stance is that I'm wrong and that the error in my thinking is because of the superior statistical training that you have?

>Of course if you could collect literally all data on all patients nationwide/worldwide you'd have enough cases

Of course, but how is that relevant? OP never suggested that you'd need a data set that big. I never suggested that you would need a data set that big. The CDC never said you would need a data set that big.

Are you saying that you'd need a data set that big?

If so, maybe we should discuss your thought process here. Otherwise, if literally nobody that matters to the conversation is suggesting we need a data set that large, then why are you choosing to focus on it?

>I don't think you realize just how much the cost/benefit ratio is skewed towards getting vaccinated.

Obviously, at least for the vast vast majority of people. We aren't talking about getting vaccinated though. We are talking about getting boosted.

So, again, how is this relevant?

>You're basically complaining that the weight of your car is given in pounds rather than in ounces

If I have the accurate weight of the car in pounds, I can literally directly calculate how many ounces it is. Are you telling me that knowing:

>The bivalent mRNA boosters from Pfizer-BioNTech and Moderna were 48% effective against symptomatic infection from the predominant omicron subvariant (XBB/XBB.1.5) in persons aged 18-49 years according to early data published by the CDC

...allows you to directly calculate the answers to all the questions I asked? NGL, that would be really impressive.

> Whenever someone starts off by trying to make it known how much smarter and better educated/trained they are, I know I'm in for some excellent analyses and good faith engagement.

I'm telling you that the answer you're looking is statistical in essence, and that you cannot understand the answer if you do not understand the underlying statistical approach

> So what if the second dice is 10 billion times less fair/more loaded than the first dice?

even if the die always rolled the same number you'd still need at least 5 hours to go anywhere. In those 5 hours you would be able to have detected/excluded very small deviations in the other die (note that you can never exclude extremely small deviations)

So of course there are other factors involved, but statistical power is always hugely dependent on the raw numbers

the current problem isn't like this anyway. Proving that the booster is at most 99% efficient against hospitalization is relatively easy, but it's a result that's useless, as instead the question that's relevant for policy is to get an estimate of its efficiency within a maybe 10% ballpark; is it around 20%, 50%, 80%? So it's the same order of magnitude as for the efficiency on symptoms

> Your stance is that I'm wrong and that the error in my thinking is because of the superior statistical training that you have?

It's not a stance. Whenever statistics are involved, it's intrinsically harder to work with events that are rare, and it's something that's very intuitive to all statisticians. I've tried to explain why that's the case but it's useless if you don't listen

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>I'm telling you that the answer you're looking is statistical in essence, and that you cannot understand the answer if you do not understand the underlying statistical approach

That's fundamentally very different from what you actually said though.

You can't understand the answer without understanding the approach != If you were trained in statistics, you wouldn't need someone to explain the answer to you.

If you honestly can't see the different implications in those two statements, I'm not sure what to tell you.

>(note that you can never exclude extremely small deviations)

Again, this is fundamentally at odds with what you said previously. Based on your earlier claims, it is "always" easier to determine if die 1 is loaded than die 2. That means that by the time we have enough info that die 2 is loaded, we should "always" have enough info to determine die 1 is loaded, regardless of how extremely small the deviations are.

Is that really the conclusion that your statistical training leads you to? Or were you just making dishonest overgeneralizations to try to shut down questions you didn't like?

>So of course there are other factors involved,

Of course there are!

That's literally not what you said earlier though.

>...but statistical power is always hugely dependent on the raw numbers

Nobody is arguing against this. Your claim is that relative ease is "always" determined by this. Again, those are two massively different claims. I have a hard time believing that you don't see this.

>So it's the same order of magnitude as for the efficiency on symptoms

If the efficiency on symptoms is 48%, that is .48 or 4.8*10^-1 for an order of magnitude of -1. Using an order of magnitude of -1, we can have anything from 1.0*10^-1 to 9.9*10^-1. Stated another way, everything from 10% effective to 99% effective has the same order of magnitude as 48%.

It seems like you agree that this is too wide of a range to be useful in a practical sense.

>Whenever statistics are involved, it's intrinsically harder to work with events that are rare,

Well this isn't true though. Go back to the dice example. What if die 1 is rolled inside a completely black box that you have no ability to interact with. You have literally no way of observing (directly or indirectly) what the die lands on when it is rolled. On the other hand, die 2 is perfectly observable but is rolled 1,000 times less frequently than die 1.

Or what if you have a sensor to 0automatically measure and record what number you get for each dice roll. However, the sensor for die 1 is broken and always spits out a completely random result regardless of what the true result is on die 1. In contrast, the sensor for die 2 is known to be 100% accurate, but die 2 is rolled 1,000 times less frequently.

The idea that it is intrinsically harder to work with die 2 in these scenarios because the events are more rare is just flat out wrong.

> ...and it's something that's very intuitive to all statisticians.

If the professors in your statistics program are telling you die 2 is harder to work with in those scenarios, you should probably ask for a refund.

>I've tried to explain why that's the case but it's useless if you don't listen

Have you considered the possibility that you don't actually know as much as you think you do?

there's nothing I can do for you here. You need to read through a basic statistics book. Seems like you have some training in physics so hopefully the mathematical aspects won't be a problem for you

And stop believing people more competent than you on a subject are out to get you. I'm trying to explain in a few lines things you need several years of formal learning to fully understand, of course there are going to be many caveats. That doesn't mean I'm not doing my best to portray things honestly. Now if you don't want to trust me, well, as I said, learn statistics yourself

>You need to read through a basic statistics book.

I think that this might highlight your problem here. This idea that it "always" comes down to which scenario has more frequent occurrences is exactly the type of dumbed-down, overgeneralized claim you'd find in a basic statistics book.

While it might be useful for discussing the issue with someone who has literally zero knowledge of the field, it has no place in a higher level discussion of real-world studies with other professionals.

Seriously, have you ever been involved in a real-world research study where you were going to have to collect a ton of data and then analyze it? I have. "How do we measure what's happening?" and "How do we know our measurements are accurate?" and "How do we reduce system complexity to isolate the thing that we are actually interested in?" are some of the very first conversations that we have.

If someone on the team responded by essentially saying:

>None of that matters. All we have to do is pick something that happens more frequently because that is always the answer.

...we are going to start wondering how this guy ever got on the team in the first place.

>And stop believing people more competent than you on a subject are out to get you.

It is more about people that:

• Don't know anything about me.
• Insist on repeatedly stressing how incompetent/untrained/unskilled I am, despite knowing nothing about me.
• Use these repeated, baseless claims about my competency to dismiss my clearly accurate and legitimate criticisms/critiques out of hand.

If you are doing that, which you clearly are, you might not be out to get me, but you are certainly trying to shut down rational discussion.

>Now if you don't want to trust me, well, as I said, learn statistics yourself

Have you considered the possibility that I don't trust you and have learned statistics myself? You think that not properly considering this is a root cause of our disagreement?

>That doesn't mean I'm not doing my best to portray things honestly.

I mean, I don't know any professional in my field that would dismiss the importance of observability, measurement accuracy, system complexity etc. when attempting to perform statistical analysis of some event of interest.

You've dismissed the importance of these things over and over in this conversation by repeatedly stressing that it "always" comes down to how frequently something occurs. When I call you out, you resort to personal attacks on my understanding, training, and competency.

I'm not a mind reader so I can't say with 100% certainty what your motivation is, but it certainly doesn't seem like you are making a serious effort to engage honestly.

> I think that this might highlight your problem here. This idea that it "always" comes down to which scenario has more frequent occurrences is exactly the type of dumbed-down, overgeneralized claim you'd find in a basic statistics book

not at all; I'm not recommending a basic book because it will give you the answer you're looking for, but because it's where you need to start

> Seriously, have you ever been involved in a real-world research study where you were going to have to collect a ton of data and then analyze it?

its my job

> Don't know anything about me.

it seems you have formal training in physics but not in statistics

> Insist on repeatedly stressing how incompetent/untrained/unskilled I am, despite knowing nothing about me.

you keep denying elementary statistical principles, so I assume that you don't have that knowledge

you keep failing to see the problem from a broad statistical perspective. That alone is proof of your incompetence, and why I recommended reading a basic book. You don't have the foundation to transfer your knowledge of physical data analysis to medical data analysis

>not at all; I'm not recommending a basic book because it will give you the answer you're looking for, but because it's where you need to start

That implies that I have no "start" in understanding statistics. This is a baseless (and inaccurate) implication.

>its my job

Ok, so apply that. If you get a real-world scenario that you are trying to analyze, you don't consider how observable it is? You don't consider how easily you can measure it? You don't consider how accurate your measurements are? You don't consider how complex the system is?

Instead as long as it occurs "many" times more frequently than a problem that can be successfully analyzed with a high degree of accuracy, then you "know" that this problem will be "easier?"

Seriously, there are instances where you can get statistically meaningful results with a frequency of a few dozen. 1,000 is certainly "many" more than that. Your stance is literally that you can model the most complex systems in the universe as long as they have happened at least 1,000 times.

Not 1,000 times that you've seen. Not 1,000 times that you can accurately measure. They just have to have happened 1,000, period.

Again, this assertion is just ridiculous on its face, yet that's what is suggested by your position. When I've pointed out that it is ridiculous, your go to move is to resort to personal attacks.

>it seems you have formal training in physics but not in statistics

That's the assumptions you've made. That's not the same as that actually being the case, nor is it the same as there being a logical basis to from that conclusion.

>you keep denying elementary statistical principles, so I assume that you don't have that knowledge

The idea that many more occurrences always makes one thing easier to analyze than another, regardless of relative observability, measurability, accuracy of measurements, system complexity, etc. is not an elementary statistical principle. Saying it repeatedly doesn't change the reality.

>you keep failing to see the problem from a broad statistical perspective.

Well your claims aren't limited to a broad statistical prospective though. When you claim that this is "always" the case and you make personal attacks on the knowledge base of anyone that disagrees, then you are pretty clearly taking the stance that it applies in any and all scenarios, including very specific circumstances.

> That's the assumptions you've made. That's not the same as that actually being the case, nor is it the same as there being a logical basis to from that conclusion

there's definitely a logical basis :) now of course, you're clearly not honest with me, so I'm only permitted suspicions

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Look in the mirror.

You're either pretending to have a job collecting and analyzing data or you are pretending to believe that you can easily reach statistically relevant results for any question of interest, as long as something has happened ~1,000 times, even if it is impossible to observe or measure these ~1,000 events.

Not only that, you claim that this is an "elementary statistical principle." Maybe you should pump the breaks on accusing others of not being honest here.

I've already given answers to these arguments. You're over-interpreting what I've said and have built a straw man that I won't bother taking down

if you want to believe you know, do just that

>You're over-interpreting

Nope. You said "always." I called you out on that as being an over generalization that didn't hold water when applied to all specific instances. You response was to make personal attacks about how I don't understand statistics.

>...and have built a straw man that I won't bother taking down

You didn't say "always"? You didn't push back and resort to personal attacks when I called you out on this being an over generalization?

Or are you saying that you agree that it was a over generalization, but you still personally attacked me for pointing it out?

> of course there are other factors involved, but statistical power is always hugely dependent on the raw numbers

always is correct

my very first answer could have specified "always in epidemiology studies", but it was evident from context; unless you've forgotten what this discussion is about (which very much seems to be the case, at this point you just want to convince yourself that you are right to doubt the faithfulness of the original article and whoever defends it)

>always is correct

Now you are just being disingenuous. You and I both know that this wasn't your first use of the word "always," nor was it the one I was referring to.

>my very first answer could have specified "always in epidemiology studies", but it was evident from context;

Really? Your very first answer include the following example:

>It's like if you're trying to check if two dice are loaded, but there's one die you can roll every few seconds and another you can roll only once every hour

The reality is that, unless you are suggesting that rolling dice is an epidemiology study, then the context clearly wasn't limiting your claim to epidemiology studies. At the very least, the context was applying your statement to dice rolls as well.

EDIT: Funny that you don't even attempt to address my claim (e.g., at the very least, the context of your example was meant to apply to epidemiology studies and dice rolls). Instead, you just make a reply that doesn't attempt to address this point and then block me.

the metaphor is valid for epidemiology studies. at the core you're just tallying the chances of an objectively observable binary outcome in a series of predetermined groups

I'm not sure where your experiment of rolling infinitesimally loaded dice in a sealed black box is coming from but it's so completely absurd and disconnected from the practical and theoretical considerations associated with epidemiology that I needn't comment on it