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Embracing Bayesian methods in clinical trials

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Re: Embracing Bayesian methods in clinical trials

#11

> Although an adaptive design guidance finalized in 2019 left the door open for bayesian trials, their use in drug development has to date been limited,4 such as in Ebola and SARS-CoV-2 epidemics and in pediatric and rare disease trials. the reason it has been limited to those cases is drug development, today, is constrained by commercialization. all four categories listed - ebola, sars-cov-2, pediatric and rare dise…

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Re: Embracing Bayesian methods in clinical trials

#12
post #3

Earlier quoted context omitted.

There's a weird thing that happens with cancer drugs that I just experienced. When they are working with a pharmacy and your insurance, the price they'll charge you is $10,000 for enough pills to last a month. But when your insurance says "We won't cover that" all the sudden you find out the company has a backdoor subsidization program which will fully cover the cost of the drug for reasons I can't really fathom (goo…

It's a bit like software pricing, the marginal cost of production is low. You often see massively different prices charged to different types of customer.

I used to support an application used by about half a dozen businesses. They all knew each other, that's how I got their business.

Two of them were paying significantly more for their support than the others. That's because those two had their phone numbers set to ring even when my phone is in quiet mode. Just for the privilege of being able to wake me whenever you want, and get my attention even when I'm in the middle of a hike, you're paying substantially more.

Re: Embracing Bayesian methods in clinical trials

#13
post #3

> Although an adaptive design guidance finalized in 2019 left the door open for bayesian trials, their use in drug development has to date been limited,4 such as in Ebola and SARS-CoV-2 epidemics and in pediatric and rare disease trials. the reason it has been limited to those cases is drug development, today, is constrained by commercialization. all four categories listed - ebola, sars-cov-2, pediatric and rare dise…

There's a weird thing that happens with cancer drugs that I just experienced. When they are working with a pharmacy and your insurance, the price they'll charge you is $10,000 for enough pills to last a month. But when your insurance says "We won't cover that" all the sudden you find out the company has a backdoor subsidization program which will fully cover the cost of the drug for reasons I can't really fathom (goo…

Isn’t it just regular price discrimination? Profit maximizing firms with market power charge different prices based on willingness to pay in order to sell to people who won’t pay a higher single value monopolist price.

https://en.wikipedia.org/wiki/Price_discrimination

Re: Embracing Bayesian methods in clinical trials

#14
> Bayesian inference assumes the observed data are fixed and aims to quantify the evidentiary support for all possible levels of treatment effectiveness based on the data at hand.

The problem with this approach is that we can only observe ONE level of treatment effectiveness, i.e., the level of treatment effectiveness that the treatment actually possesses. All other possible levels of effectiveness are entirely hypothetical. There's no data about all these other possible levels of effectiveness because they don't occur in reality. So the data cannot possibly tell you anything about how likely is the observed outcome, because the observed outcome is the only outcome that you observe. I

This criticism was made over 100 years ago, and Bayesians still don't have an answer. They just keep going as if nothing happened, but the reality is their methodology is utterly and fatally flawed.

Re: Embracing Bayesian methods in clinical trials

#15
post #14

> Bayesian inference assumes the observed data are fixed and aims to quantify the evidentiary support for all possible levels of treatment effectiveness based on the data at hand. The problem with this approach is that we can only observe ONE level of treatment effectiveness, i.e., the level of treatment effectiveness that the treatment actually possesses. All other possible levels of effectiveness are entirely hypot…

[deleted]

Re: Embracing Bayesian methods in clinical trials

#17
post #14

> Bayesian inference assumes the observed data are fixed and aims to quantify the evidentiary support for all possible levels of treatment effectiveness based on the data at hand. The problem with this approach is that we can only observe ONE level of treatment effectiveness, i.e., the level of treatment effectiveness that the treatment actually possesses. All other possible levels of effectiveness are entirely hypot…

> So the data cannot possibly tell you anything about how likely is the observed outcome, because the observed outcome is the only outcome that you observe.

This could also be viewed as supporting the Bayesian perspective, where the observed data are not viewed as random variables - they are fixed. This is because, as you say, the observed outcome is the only outcome that you observe. It is the classical setting, in comparison, where we instead do our analysis by treating the sample as a random variable, placing the counterfactual on other non-observed values ("what if I had drawn a different sample?"), even though we didn't. Bayesian methods treat the data as gospel truth, and place the counterfactual on the different parameters ("what if the population were different?"), even though it isn't.

The other criticism you have is

> The problem with this approach is that we can only observe ONE level of treatment effectiveness, i.e., the level of treatment effectiveness that the treatment actually possesses. All other possible levels of effectiveness are entirely hypothetical.

This is true of both Bayesian and classical methods. We build models that would explain how different hypothetical levels of effectiveness would affect what data we should expect to see - that is the whole point. Classical methods also involve exploring scenarios in which purely hypothetical values of the parameter may be potentially true, and characterizing counterfactual samples that could have been drawn from them, even though in real life they couldn't have been.

Re: Embracing Bayesian methods in clinical trials

#18
post #14

> Bayesian inference assumes the observed data are fixed and aims to quantify the evidentiary support for all possible levels of treatment effectiveness based on the data at hand. The problem with this approach is that we can only observe ONE level of treatment effectiveness, i.e., the level of treatment effectiveness that the treatment actually possesses. All other possible levels of effectiveness are entirely hypot…

It’s hard to understand what criticism you are making, or what alternative this criticism doesn’t apply to, in contrast. Would you care to elaborate?

Re: Embracing Bayesian methods in clinical trials

#19
post #3

> Although an adaptive design guidance finalized in 2019 left the door open for bayesian trials, their use in drug development has to date been limited,4 such as in Ebola and SARS-CoV-2 epidemics and in pediatric and rare disease trials. the reason it has been limited to those cases is drug development, today, is constrained by commercialization. all four categories listed - ebola, sars-cov-2, pediatric and rare dise…

There's a weird thing that happens with cancer drugs that I just experienced. When they are working with a pharmacy and your insurance, the price they'll charge you is $10,000 for enough pills to last a month. But when your insurance says "We won't cover that" all the sudden you find out the company has a backdoor subsidization program which will fully cover the cost of the drug for reasons I can't really fathom (goo…

Long wall of text incoming, but please read if you want to know why drug pricing works like it does:

It’s because of the way insurance works in the US. Insurance companies have formularies, which are essentially menus of what products they cover. They also will label certain medications as “preferred” and actively steer consumers to them. Pharma companies fight to get preferred coverage from insurers.

Because of this, pharmaceutical companies go through complex negotiations with insurance companies. In theory, this is to get pharma companies to compete on price and offer discounts (called “rebates”). An industry of middlemen called pharmacy benefit managers (PBMs) has arisen who negotiate with pharma companies on behalf of the insurers and create the formularies. The problem is, these guys take a percentage cut of the discount they secure for the insurers. This creates a perverse incentive to give preferred status to more expensive drugs. Take this hypothetical example of two competing drugs:

- Drug A costs $150, but gets negotiated down to $100 - Drug B costs $200, but gets negotiated down to $100 - The PBM gets a fee of 10% of the secured discount, meaning they make twice as much for creating a formulary with B rather than A

To no one’s surprise, drug B gets given preferred status over A.

Pharma companies figured this out a long time ago, and began to jack up their prices each year, only to immediately negotiate them back down to where they were previously, in the form of rebates, because this increased the likelihood of getting out ahead on the formulary by the PBM. The best example of this might be insulin, which has skyrocketed in list price, but profit for the insulin companies has actually remained much more stable. This is because each year the sticker price is raised, and then immediately slashed in the “negotiations” with the insurers.

After a while, the pure sticker shock began causing a lot of justified outrage among patients and the public. Pharma companies saw they were taking the blame for skyrocketing prices even though at the end of the day they were negotiating these prices down and weren’t actually making that money due to rebates. So they began to offer alternatives for customers not on insurance, in the form of “coupons”, “savings cards”, etc. These often get you prices close to what the actual cost of the medicine is before the “jack up the price then rebate it down” dog and pony show. But insurers/PBMs reacted poorly to these, and began punishing pharma companies through the formularies. This is why these have become shadowy “backdoor” programs.

It’s also important to note that the PBM’s aren’t even independent middlemen. 80% of the PBM industry is dominated by 3 companies, all of whom are owned by insurers: Express Scripts (Cigna), CVS Caremark (CVS/Aetna), and OptumRx (UnitedHealthcare)

So why even go through all this?

- Pharma companies don’t really have a choice, they have to to get on the formulary

- PBMs make their money entirely through this scheme

- Costs for insurers aren’t ultimately changing much from year to year. However, higher sticker prices means the public is ever more dependent on insurance for medical bills. It also provide justification for increasing premiums more than their costs might otherwise. And pharma ultimately takes the brunt of the blame.

Lastly, if you are wondering why this doesn’t exist in other countries, it’s an unsurprising reason: government subsidized health insurance. Unlike insurers and PBMs, there are no perverse incentives or profit motive, so cheaper prices are an actual benefit. Medicare/medicaid doesn’t have to go through these shenanigans, but they aren’t available widely in the US. You don’t even need healthcare for all. You just need a public healthcare option for everyone. That introduces an actor whose motives actually align with consumers, invalidates this entire charade, and forces insurers and PBMs to actually compete on merit and price.

TLDR: The lack of public healthcare options in the US has created an insurance cartel that has both consumers and pharmaceutical companies by the balls.

Re: Embracing Bayesian methods in clinical trials

#20
post #14

> Bayesian inference assumes the observed data are fixed and aims to quantify the evidentiary support for all possible levels of treatment effectiveness based on the data at hand. The problem with this approach is that we can only observe ONE level of treatment effectiveness, i.e., the level of treatment effectiveness that the treatment actually possesses. All other possible levels of effectiveness are entirely hypot…

It’s hard to understand what criticism you are making, or what alternative this criticism doesn’t apply to, in contrast. Would you care to elaborate?

Imagine we want to know the ratio of men to women in a particular population. We could count all men and women one by one, but it would take too long, so instead we take a random sample and count the men and women in the sample, and from that we infer the quantity that we want to know. This is statistical inference.

In Bayesian inference, the population ratio is seen as a quantity that can take different values each with a associated probability (i.e. a random variable), and the result of Bayesian inference is an estimate of the probability distribution of the population parameter, in this case the population ratio. Now, in reality the population ratio is a concrete number, say 9-to-10, meaning that there are 9 men for every 10 women in the population. But Bayesians don't care. They'll tell you that the population ratio is a random variable which can take many values, and that the probability that it is equal to 9-to-10 is whatever number between 0 and 100%.

This is nonsense because the population ratio is NOT a random variable. People don't come in and out of existence randomly, right? In a way, they're saying there are infinitely many possible universes, each with a different population ratio, and then they come up with an estimate of the probability that the universe in which the ratio is 9-to-10 has whatever probability of occurring. This is absolutely BIZARRE. (I hope you agree). And it's wrong because it's impossible to know how likely one universe is compared to all other possible universes, since we live in our universe and this is all we can hope to observe.

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