the economist put out a piece a few months ago providing just that. Specifically it compares overall cancer mortality rates (and more interestingly, mortality rates adjusted for age) and shows that cancer deaths have been dropping.
https://www.economist.com/briefing/2025/07/17/the-world-is-m...
So for example, if you have (hypothetically) an untreatable cancer that would take six years to kill you, if it is diagnosed right away, you would be counted as a survivor, but if you are diagnosed at year five, you'll only survive a year.
Weirdly enough that's the same mechanism hypothesized to play a partial role in why breast feeding is also associated with a reduced cancer risk.
Fascinating, weird, stuff.
That's a great argument in the abstract, but it ignores the fact that there are effective treatments for colon cancer. The fact that we can reproduce real survival rates in a counterfactual world where there are no effective treatments for colon cancer does not actually give us a model of the real world because the counterfactual explicitly contradicts known scientific facts.
What you have to do in order to make this argument is to show that there are Markov models where early detection does not work despite the fact that some cancers will cause death if untreated and not if treated. You cannot simply rely on models that have clearly impossible transition probabilities. You need possible models. Or you have to show that the absolutely massive amount of scientific literature and clinical experience about how to treat colon cancer is somehow flawed.
Some people are defending this because the blog post is attacking a specific argument, but I don't see how that can work. I am pretty sure that Nassim Taleb and most other people who are capable of putting together a coherent statistical argument (even a flawed one) understand that colon cancer can be treated sometimes.
One point in the article is that early detection would give you more years to live even if there were no treatment. Because "early" means "more years". This wasn't obvious to me right away.
But he is not saying don't get screened! He is not saying there are no cancer treatments! He's saying that the 5-year survival rate, considered alone, is a tricky measure that can fool our intuition. In my case he's right.
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Details.
Dumb toy model. Let Tumor X kill you exactly 8 years after it becomes detectable in screening. Assume screening is 100% accurate with no false positives. Assume X cancer kills you exactly 2 years after it causes symptoms. Imagine that there is no treatment for X cancer.
In this dumb model, everybody dies at exactly the same time after the tumor became detectable. The people who caught it in screening had more warning, but otherwise they didn't get a better outcome. Even though screening boosts the 5-year survival rate from 0% to 100%.
Never mind his like 7-state Markov model. OMG. Why.
The specifics of your case will strongly affect what happens to you. And even for cancers that are a guaranteed death sentence, survival has increased significantly in recent years.
We're so used to argument that criticizing logic is taken as criticizing the conclusion.
Evidence: https://www.nejm.org/doi/full/10.1056/NEJMoa1301969
Large prospective cohorts (Nurses’ Health Study + Health Professionals Follow-Up Study) with long follow-up - screening colonoscopy was associated with a 68% lower risk of death from colorectal cancer overall (multivariable HR ≈ 0.32, 95% CI 0.24–0.45) and showed significant reduction for proximal colon mortality as well (HR ≈ 0.47, 95% CI 0.29–0.76).
This was my key takeaway. In a society organized around statistics, we're struggling through an era where those statistics expire faster everyday, and faster than new data can be generated. I can almost relate to the mindset that devalues "facts" because they're increasingly complicated, rapidly changing and come with too many caveats.
I don't think there is any person who is aware of the idea of cancer mortality who would equate 'Stage IV' to lead to 'average' survival.
So maybe the article's only point (which is very obvious, and does not require Markov modeling) is that if you increase the number of people who live a long time in a sample, then the average of that sample will go up.
This feels like someone saw a fact on the internet and didn't try to read about it before writing a blog post.
like the colon cancer thing. he talks about how it would only be more effective to catch colon cancer early if you assume we have treatments for it that would work early. but we don't need to just assume blindly. we already know we do have those treatments!