Below is an approximation of this video’s audio content. To see any graphs, charts, graphics, images, and quotes to which Dr. Greger may be referring, watch the above video.

“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 U.S.—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 US and the UK.” 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] overdiagnosis [bias].”

I’ve talked about overdiagnosis, 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 five years later; so, without screening, five-year survival: only 40%. But, let’s say, with screening, an additional 2,000 cancers are overdiagnosed—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 five-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 three years; so, the five-year survival for this group: 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. In this scenario, each patient survives 10 years; so, the five-year survival rate 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 three times longer—10 years instead of three. 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 seven 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 “[i]f 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] overdiagnosis [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 the lead-time bias was.” And then, when they asked the remaining 11, “Okay, what is it?”, only two 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 custom[s], [or] on [industry representatives] and their [information].”

Please consider volunteering to help out on the site.