First, ignoring frail patients means your trial isn't representative of the wider population, so it shouldn't be accepted for general use - only on people who were well-represented in the trial.
Second, you're ignoring the possibility of other treatment options. It isn't always the binary life-or-death you're making it, so SAEs do matter.
Third, a big part of trials is to discover and develop prevention methods for SAEs. Explicitly ignoring the people most likely to provide data valuable for the general population sounds like a pretty silly approach.
> Second, you're ignoring the possibility of other treatment options. It isn't always the binary life-or-death you're making it, so SAEs do matter.
A common reason for a drug (especially a cancer drug) going to trial is because other options have already failed. For example CAR-T therapies are commonly trialed on patients with R/R (relapsed/refractory) cohorts.
> "In subjects who have early-stage disease and available therapies, the unknown benefits of first-in-human (FIH) CAR T cells may not justify the risks associated with the therapy."
> First, ignoring frail patients means your trial isn't representative of the wider population, so it shouldn't be accepted for general use - only on people who were well-represented in the trial.
Sure, but including frail outliers does not automatically mean you can generalize to the whole population. People can be frail for a wide variety of reasons. Only some of those reasons will matter for a given trial. That means the predictive power varies widely depending on which subpopulation you're looking at, and you'll never be able to enroll enough of some of the subgroups without specifically targeting them.
The results in the posted paper seem valid to me, but the conclusion seems incorrect. This seems like a paper that is restating some pretty universal statistical facts and then trying to use that to impose onerous regulations that can't and won't solve the problem. It will improve generalizability for a small fraction of the population, at a high cost.
> Second, you're ignoring the possibility of other treatment options. It isn't always the binary life-or-death you're making it, so SAEs do matter.
Of course they do. It's a good thing we have informed consent.
> Third, a big part of trials is to discover and develop prevention methods for SAEs. Explicitly ignoring the people most likely to provide data valuable for the general population sounds like a pretty silly approach.
If your primary claim is that data from non-frail people is not generalizable to frail people, then how can you claim that data from frail people is generalizable to non-frail people? If the trials for aspirin found that hemophiliacs should get blood clot promoting medications along with it, then should non-hemophiliacs also be taking those medications?
I'm thankful we can extract some amount of useful data from these trials without undue risk. It's always going to be a balancing act, and this article proposes putting a thumb on the scale that reduces the data without even solving the problem it's aiming at addressing.
But you’re stating the obvious? It’s not like physicians don’t know trials are designed this way, and for good reasons.
Frail patients confound results. A drug may work great, but you’d never know because your frail patients die for reasons unrelated to the drug.
Second is obvious as well. Doctors know there are treatment alternatives (with the same drawback to trial design).
And I already touched on your third point. The alternative to excluding frail patients is not being able to tell if the drug does anything. In many cases that means the drug isn’t approved.
Excluding frail patients has its drawbacks, but it has benefits as well. This paper acts like the benefits don’t exist.
Second, you're ignoring the possibility of other treatment options. It isn't always the binary life-or-death you're making it, so SAEs do matter.
Third, a big part of trials is to discover and develop prevention methods for SAEs. Explicitly ignoring the people most likely to provide data valuable for the general population sounds like a pretty silly approach.