"Breast Cancer and the 5-Year Survival Rate Myth"
While running for president of the United States,
former New York Mayor Rudy Giuliani
ran a campaign ad contrasting his chance
of surviving prostate cancer in the US—82%—
with the same chance of surviving prostate cancer in England,
only 44% under socialized medicine,
where they don’t do routine PSA testing for prostate cancer.
To Giuliani, this meant that he was lucky
to be living in New York rather than old York,
because his chances of surviving prostate cancer
seemed to be twice as high here in the U.S.
Yet, despite this impressive difference
in this five-year survival rate, the mortality rate—
the rate at which men were dying of prostate cancer—
was about the same in the U.S. and the U.K.
Wait, what?
PSA testing increased survival from 44 to 82% –
how is that not evidence
that screening saves lives?
For two reasons:
lead time bias and over-diagnosis bias.
I’ve talked about over-diagnosis,
where a cancer is picked up that would have otherwise
never caused a problem.
Without screening, let’s say out of a thousand people
with progressive cancer,
only 400 are alive 5 years later;
so, without screening 5-year survival – only 40%.
But, let’s say with screening,
an additional 2000 cancers are over-diagnosed,
meaning you picked up cancers that would have never caused
a problem or even would have disappeared on their own.
Since the cancer was harmless,
five years later, of course, they’re all still alive,
assuming their unnecessary cancer treatment
didn’t kill them,
and, all of a sudden, you just doubled
the 5-year survival rate
even though in either case, the same number of people
died from cancer.
That’s one way how changes in survival rates
with screening may not correlate
with changes in actual cancer death rates.
The other is lead time bias.
This is how it works.
Imagine a group of patients in whom cancer was diagnosed
because of symptoms at age 67 years,
all of whom die at age 70.
Each patient survives only 3 years;
so, 5-year survival for this group is 0%.
Now, imagine that same group
undergoes screening.
Screening tests by definition lead to earlier diagnosis.
Suppose that with screening,
cancer is diagnosed in all patients at age 60 years,
but imagine in this case they,
nevertheless, all still die at age 70 years.
In this scenario, each patient survives 10 years;
so, the 5-year survival for this group is 100%.
Survival just went from zero to 100%.
Call the newspapers!
With this new screening test,
now cancer patients are living 3 times longer --
10 years instead of 3— it's a miracle!
Whereas all that really happened in this case
was that the person was treated as a cancer patient
for an additional 7 years,
which, if anything, probably
just diminished their quality of life.
So, that’s the second way how changes in survival rates
with screening may not correlate
with changes in actual cancer death rates.
And, in fact, the correlation is zero.
There is no correlation at all
between increases in survival rates
and decreases in mortality rates.
That’s why “if there were an Oscar
for misleading statistics, using survival statistics
to judge the benefit of screening
would win a lifetime achievement award hands down."
There is no way to disentangle the lead time bias
and the over-diagnosis bias from screening survival data.
That’s why these statistics are meaningless
when it comes to screening.
Yet, that’s what you see in the ads
and the leaflets from most of the cancer charities.
That’s what you hear coming from the government.
Even prestigious cancer centers,
like M.D. Anderson, have tried
to hoodwink the public like that.
If you’ve never heard of lead time bias,
don’t worry – you’re not alone.
Your doctor may not have either.
Fifty-four of 65 physicians surveyed said they did not know
what lead-time bias was.
And then, when they asked the remaining 11,
“OK, what is it?”, only 2 were actually correct.
So, at this point in the video,
already, you may know more
about this than 97% of doctors.
To be fair, though, maybe they don’t recognize the term,
but understand the concept?
Nope...
“The majority of primary care physicians did not know
which screening statistics provide reliable evidence
on whether screening works.”
They were 3 times more likely to say
they would “definitely recommend” a cancer screening test
based on irrelevant evidence,
compared to a test that actually decreased
cancer mortality by 20%.
If physicians don’t even understand key cancer statistics,
how are they going to effectively counsel their patients?
“Statistically illiterate physicians
are doomed to rely on their statistically illiterate conclusions",
or on local customs, or on industry representatives and their information.