The MRI analogy is not good. The false positive risk is only against the present-day distribution of MRIs mostly taken of symptomatic patients; if we had the dataset of "annual MRIs for everyone" we would very quickly recalibrate our sensitivity to the new baseline.
MRIs produce shadows that are indistinguishable from cysts or tumors all the time. They are benign, but no amount of data will reduce them. And telling someone "you have a shadow here, it's probably benign" makes people anxious and they go, "maybe I should boost it?" Which is needlessly invasive.
... yeah? You'd expect the false positive rate to be HIGHER when you're not looking at an enriched patient subset. That's why we're careful about recommending certain kinds of screening. See also: PSA screening.
Well, you missed my point. I'm not talking about "you look at the MRI and see something and say it's a positive", I'm referring to the process of reading MRIs as like a statistical model (even if in practice it exists in the minds of radiologists) which is trained on the corpus of MRI data. That model will depend in some way on the distribution of positive/negative examples in the corpus; if the corpus changes the model has to then be updated to match.
Point is, the false positive concern is only a concern if you use the old model with the new corpus. Don't do that! That's dumb!
The net effect of MRIing everyone on public health would likely be enormously positive as long as you don't do that.
Take PSA, since it's a simpler example. You're right that, if we screen everyone, taking action based on the outcome causes more harm than good. The response is to calibrate... which means we don't learn anything usefully actionable from the test and shouldn't apply it.
With the MRI, you don't get back simple dichotomous things, but you get back potential indications. That can be scary - talk about calibration all you want, but if patients see things and start thinking about the big C word there are likely to be a lot of unnecessary biopsies.
The bottom line is that it's possible to imagine a benefit, but it is not reasonable to pretend it's as simple as "just re-calibrate your interpretation of the results!". There's a reason that a lot of thought goes into when to do screening.
> which means we don't learn anything usefully actionable from the test and shouldn't apply it.
This just isn't true. In practice any such screening model can ALWAYS improve with more data—basically because the statistical power goes up and up—up to an asymptote set by noise in the physical process itself.
> That can be scary
Handling that is the job of professionals, is now and will continue to be.
It is extremely reasonable to imagine a benefit! What is doubtful is imagining there wouldn't be one!
I find the line of reasoning in this whole anti-MRI-everyone argument to be bewildering. I think it is basically an emotional argument, which has set in as "established truth" by repetition; people will trot it out by instinct whenever they encounter any situation that suggests it. It reflects lessons collectively learned from the history of medicine, its over-estimation of its own abilities and its overfitting to data, and its ever-increasing sensitivity to liability.
But it is not inherently true—it is really a statement about poor statistical and policy practices in the field, which could be rectified with concerted effort, with a potential for great public upside.
Not that any of this matters at the current price point. But, on a brief investigation, the amortized cost of a single MRI scan is ~$500-800—perhaps 1/5 what I would have guessed!
> This just isn't true. In practice any such screening model can ALWAYS improve with more data—basically because the statistical power goes up and up—up to an asymptote set by noise in the physical process itself.
That isn't how this works at all.
1. If you assume the test results are iid, sure you can increase your precision (presuming you're talking about repeatedly testing people?), but biology is messy and the tests are correlated. You can get all kinds of individual-specific cross-reactivity on a lab assay, for example. As another example, you can't just keep getting more MRIs to arbitrarily improve your confidence that something is cancer/not cancer/a particular type of cancer etc.
2. Statistical power is not relevant here, but rather different kinds of prediction error. It turns out that in the general population, it is NOT medically relevant that PSA is correlated with the presence of prostate cancer, because it is NOT predictive of mortality, and it IS a cause for unnecessary intervention and thus harm to patients.
I really don't mean to cause offense, but you're talking about this like someone who has no idea how these concepts interact with reality in the biomedical world. Like, you seem to be applying your intuition about how tabular data analysis tends to work in systems you're familiar with, and assuming it generalizes to a context where you don't have experience.
> this whole anti-MRI-everyone argument to be bewildering
It's not about being against MRIs, it's about the idea that (even ignoring costs/cost effectiveness) there are known real-world effects of over-screening people for things.
> But it is not inherently true—it is really a statement about poor statistical and policy practices in the field, which could be rectified with concerted effort, with a potential for great public upside.
This is still not at all a certainty. Let's say you lock this behind a screening system run by data scientists so that there's no patient or provider pressure to act in what you're calling a statistically poor manner. Ok, then what? They have to come up with a decision rule about when to dig deeper and get more data (which again, isn't an MRI, but rather is often an invasive procedure). It is not obvious that there exist any decision rule that could reasonably be arrived at that would be a good trade-off in terms of false positives and the corresponding additional burden.
I am 1000% willing to entertain the idea that new screening can be a net benefit, but we'd need to know what kind of sensitivity/specificity tradeoff would be involved to even start approximating the numbers, and then you'd need to do a trial to demonstrate that it's worthwhile, and even then you'd need to do post-trial monitoring to make sure there aren't unexpected second order effects. People DO, in fact, do this work.
The idea that "more data == better" is just way too simplistic when the data is messy and necessarily inconclusive, the outcomes of interest are rare, and the cost of additional screening can be severe - again also ignoring that all of this is expensive in the first place.
