Learn More Than 97% of Doctors About Lead-Time Bias

December 2, 2021 Auto Bot breast cancer, breast cancer survival, cancer, Europe, informed consent, lifespan, longevity, mammograms, Medical Ethics, men's health, mortality, News, Nutrition, prostate cancer, PSA, surgery, womens health 0

After reading this, you’ll know more than an estimated 97 percent of doctors about a critical concept called lead-time bias.

While running for president of the United States, former New York mayor Rudy Giuliani ran a campaign ad contrasting his 82 percent chance of surviving prostate cancer in the United States with the 44 percent chance of surviving it in England “under socialized medicine” where routine PSA testing for prostate cancer is not done. “To Giuliani this meant that he was lucky to be living in New York and not in York, because his chances of surviving prostate cancer seemed to be twice as high in New York. Yet despite this impressive difference in the 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.” How could that be? PSA testing increased survival from 44 to 82 percent, so how is that “not evidence that screening saves lives? For two reasons: The first is lead time bias…The second is overdiagnosis.”

As I illustrate at 1:05 in my video Breast Cancer and the Five Year Survival Rate Myth, overdiagnosis is when a cancer that otherwise would have never caused a problem is detected. Consider this: Let’s say that, without screening, only 400 people out of a thousand with progressive cancer are alive five years later. That means that without screening, the five-year survival rate is only 40 percent. But, let’s say that with screening, an additional two thousand cancers are overdiagnosed, meaning cancers that would have never caused a problem or may have disappeared on their own are picked up. So, because those cancers are harmless, those overdiagnosed patients all still alive five years later, assuming their unnecessary cancer treatment didn’t kill them. In this way, the five-year survival rate has just doubled, even though in either case, the same number of people died from cancer. If that’s confusing, watch the video. That’s one way the changes in survival rates with screening may not correlate with changes in actual cancer death rates.

The other way is lead time bias. Imagine a group of patients who were diagnosed with cancer because of symptoms at age 67 and all died at age 70. Each patient survived only three years. So, the five-year survival rate for the group is 0 percent. Now, imagine that the same group underwent screening. By definition, screening tests lead to earlier diagnosis. Suppose that with screening, the cancers were diagnosed in all patients at age 60 instead of 67, but, nevertheless, they all still died at age 70. In this screening scenario, each patient survived ten years, which makes the five-year survival rate for this group 100 percent. Survival just went from 0 to 100 percent! You can imagine the headlines: “ Cancer patients live three times longer with new screening test, ten years instead of three.” All that really happened in this screening scenario, though, is that the people were treated as cancer patients for an additional seven years. If anything, that would likely just diminish their quality of life.

So, that’s the second way that changes in survival rates with screening may not correlate with changes in actual cancer death rates. In fact, the correlation is zero, as you can see at 3:14 in my video. 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 lead time and overdiagnosis biases from screening survival data.” That’s why, “in the context of screening, these statistics are meaningless: there is no correlation between changes in survival and what really matters, changes in how many people die.” Yet, that’s what you see in the ads and leaflets from most of the cancer charities and what you hear from the government. Even prestigious cancer centers, like M.D. Anderson, have tried to hoodwink the public this way, as you can see at 3:57 in my video.

If you’ve never heard of lead time bias, don’t worry, you’re not alone. Your doctor may not have heard of it either. “Fifty-four of the 65 physicians [surveyed] did not know what the lead-time bias was. Of the remaining 11 physicians who indicated they did know, only 2 explained the bias correctly.” So, just by having read to this point in this blog post, you may already know more about this than 97 percent of doctors.

To be fair, though, is it possible the doctors don’t recognize the term but understand the concept? No. “The majority of primary care physicians did not know which screening statistics provide reliable evidence on whether screening works.” In fact, they “were also 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 percent.

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, on local custom, and on the (mostly) inaccurate promises of pharmaceutical sales representatives and their leaflets.”

KEY TAKEAWAYS

Overdiagnosis, the detection of cancer that otherwise would never have caused a problem, can result in unnecessary cancer treatments and affect survival rates of breast cancer patients.
For example, without screening, the five-year survival rate is 40 percent. With screening, however, overdiagnosis results in more cancer patients, despite the likelihood that their cancers are harmless or may disappear on their own. And, those overdiagnosed patients should be alive after five years, which doubles the five-year survival rate, even though the same number of patients died from cancer.
Lead time bias is also an issue. Symptomatic patients may be diagnosed at a later age than had they been with screening, which, by definition, leads to earlier diagnosis. In this case, imagine patients were diagnosed without screening at age 67 and died three years later, so the five-year survival rate is 0 percent. Now imagine the group underwent screening and the cancers were diagnosed at age 60, so they were alive for ten years before dying at 70. In the screening scenario, the five-year survival rate for the group is 100 percent.

In fact, there is no correlation between increases in survival rates and decreases in mortality rates.
It is not possible to disentangle the biases of lead time and overdiagnosis from screening survival data.
The overwhelming majority of doctors—54 out of 65 physicians surveyed—are unfamiliar with lead time bias, and of the 11 who indicated they did know, only 2 explained the bias accurately.

How can doctors who don’t even understand key cancer statistics effectively counsel their patients?

There is just so much confusion when it comes to mammography, combined with the corrupting commercial interests of a billion-dollar industry. As with any important health decision, everyone should be fully informed of the risks and benefits, and make up their own mind about their own bodies. This is one installment in my 14-part series on mammograms, which includes: