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0 pointsthrowaway2902y ago0 comments

Sure, but cancer != positive test because of false positives. Therefore the question of odds.

I like your idea of working these things out statistically but by your phrasing it's not necessarily what's being done (as in there are no reliable enough numbers for odds that matter).

But even if those odds were available, no matter what patient chooses, once you tell then there is a positive result, the fact that false positive is possible and odds are not in favor of treatment so they make a choice to do nothing does not mean they get to live a normal life from now on. Patients are not pure bayesian choice machines. The choice you make will affect you in big unknown ways and the existence of such choice already affects you until your EOL and has repercussions. That cancer was detected in your body cannot be "unheard" or "unread" back. Maybe you manage to deal with it, maybe you will live in constant stress. and of course chronic stress is connected to tissue inflammation, sleep disruption and other issues.

Therefore your argument that screening is automatically good does not seem to be convincing to me

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DennisP2y ago

Right the numbers for "positive test" should include false positives. That's part of my point. I've worked out a sample calculation if you want to see it. We could get fancy with error bars but I don't think that's necessary for any common cancer, and rare cancers shouldn't be tested for anyway since you'll mostly have false positives.

As I said above, I don't think that screening is automatically good because in some cases, working out the above numbers will tell you not to treat even if you had a positive test. Those cases should not be tested since they're not actionable anyway. That would likely be the case for a rare cancer.

I agree that a false positive is not great. But if you have the data, then the impact of false positive tests is already included in your data. You're comparing the total rates of death and other unpleasant outcomes for a tested population vs. an untested population.

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