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The article seems to suggest the false positive rate is only 38%: The trial followed 25,000 adults from the US and Canada over a year, with nearly one in 100 g

The article seems to suggest the false positive rate is only 38%: The trial followed 25,000 adults from the US and Canada over a year, with nearly one in 100 getting a positive result. For 62% of these cases, cancer was later confirmed. (It also had a false negative rate of 1%:) The test correctly ruled out cancer in over 99% of those who tested negative.

hn_throwaway_99 1y ago
If the stats were as good as the hyperbole in the article, it would clearly state the only 2 metrics that really matter: predictive value positive (what's the actual probability that you really have cancer if you test positive) and predictive value negative (what's the actual probability that you're cancer free if you test negative). As tptacek points out, these metrics don't just depend on the sensitivity and specificity of the test, but they are highly dependent on the underlying prevalence of the disease, and why broad-based testing for relatively rare diseases often results in horrible PVP and PVN metrics. Based on your quoted sections, we can infer: 1. About 250 people got a positive result ("nearly one in 100") 2. Of those 250 people, 155 (62%) actually had cancer, 95 did not. 3. About 24,750 people got a negative test result. 4. Assuming a false negative rate of 1% (the quote says "over 99%") it means of those 24,750 people, about 248 actually did have cancer, while about 24,502 did not. When you write it out like that (and I know I'm making some rounding assumptions on the numbers), it means the test missed the majority of people who had cancer while subjecting over 1/3 of those who tested positive to fear and further expense.
- dv_dt 1y ago
  so possibly saving lives and late stage cancer care level medical expenses 2/3 of positive results vs fear and lighter medical care 1/3 of the time. is this not a win?
- thaumasiotes 1y ago
  > If the stats were as good as the hyperbole in the article, it would clearly state the only 2 metrics that really matter: predictive value positive (what's the actual probability that you really have cancer if you test positive) and predictive value negative (what's the actual probability that you're cancer free if you test negative). As tptacek points out, these metrics don't just depend on the sensitivity and specificity of the test This is a bizarre thing to say in response to... a clear statement of the positive and negative predictive value. PPV is 62% and NPV is "over 99%". Your calculations don't appear to have any connection to your criticism. You're trying to back into sensitivity ("the test missed the majority of people who had cancer") from reported PPV and NPV, while complaining that sensitivity is misleading and honest reporting would have stated the PPV and NPV.
- inglor_cz 1y ago
  "only 2 metrics that really matter" Nope, there is another important thing that matters: some of the cancers tested are really hard to detect early by other means, and very lethal when discovered late. I would not be surprised if out of the 155 people who got detected early, about 50 lives were saved that would otherwise be lost. That is quite a difference in the real world. Even if the statistics stays the same, the health consequences are very different when you test for something banal vs. for pancreatic cancer.
  - holowoodman 1y ago
    Maybe. Let's do a thought experiment. Let's say you do have a positive test for pancreatic cancer. Overall 5 year survival rate 12%, but other than with other cancers, people continue to die after that. Basically, it is almost a death sentence if it is a true positive. Early detection will increase your odds a bit, and prolong your remaining expected lifetime, but even stage 1 pancreatic cancer, only 17% survive to 10 years. Let's say you are one of the 99% of false positives, because everyone gets tested in this hypothetical scenario. Let's say imaging and biopsy looks clean. No symptoms (which you typically don't have until stage 3 with pancreatic cancer, where it is far too late anyways). With the aforementioned odds, what would you do? Panic? Certainly, given that if it is a real positive, you might as well order your headstone. Panic more? Maybe people with those news will change their behaviour and engage in risky activities, get depressed, or attempt suicide (https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2800688 https://jamanetwork.com/journals/jamanetworkopen/fullarticle... ). All of which will kill some of those people. Get surgery to remove your pancreas? Well, just the anesthesia as a 0.1% chance of killing you, the surgery might kill 0.3% in total. No pancreas means you will instantly have diabetes, which cuts your life expectancy by 20 years. Start chemotherapy? Chemo is very dangerous, and there is no chemo mixture known to be effective against pancreatic cancer, usually you just go with the aggressive stuff. It is hard to come by numbers as to how many healthy people a round of chemo would kill, but in cancer patients, it seems that at least 2% and up to a quarter die in the 4 weeks following chemotherapy (https://www.nature.com/articles/s41408-023-00956-x https://www.nature.com/articles/s41408-023-00956-x ). And chemotherapy itself has a risk of causing cancers later on. Start radiation therapy? Well, you don't have a solid tumor to irradiate, so that is not an option anyways. But if done, it would increase your cancer risk as well as damage the irradiated organ (in that case probably your pancreas). So in all, from 100 positive tests you have 99 false positives in this scenario. If just one of those 99 false positives dies of any of the aforementioned causes, the test has already killed more people than the cancer ever would have. Even if no doctor would do surgery, chemotherapy or radiation treatment on those hypothetical false positives, the psychological effects are still there and maybe already too deadly. So it is a very complex calculation to decide whether a test is harmful or good. Especially in extreme types of cancer.

hn_throwaway_99 1y ago

If the stats were as good as the hyperbole in the article, it would clearly state the only 2 metrics that really matter: predictive value positive (what's the actual probability that you really have cancer if you test positive) and predictive value negative (what's the actual probability that you're cancer free if you test negative). As tptacek points out, these metrics don't just depend on the sensitivity and specificity of the test, but they are highly dependent on the underlying prevalence of the disease, and why broad-based testing for relatively rare diseases often results in horrible PVP and PVN metrics. Based on your quoted sections, we can infer: 1. About 250 people got a positive result ("nearly one in 100") 2. Of those 250 people, 155 (62%) actually had cancer, 95 did not. 3. About 24,750 people got a negative test result. 4. Assuming a false negative rate of 1% (the quote says "over 99%") it means of those 24,750 people, about 248 actually did have cancer, while about 24,502 did not. When you write it out like that (and I know I'm making some rounding assumptions on the numbers), it means the test missed the majority of people who had cancer while subjecting over 1/3 of those who tested positive to fear and further expense.

dv_dt 1y ago
so possibly saving lives and late stage cancer care level medical expenses 2/3 of positive results vs fear and lighter medical care 1/3 of the time. is this not a win?
thaumasiotes 1y ago
> If the stats were as good as the hyperbole in the article, it would clearly state the only 2 metrics that really matter: predictive value positive (what's the actual probability that you really have cancer if you test positive) and predictive value negative (what's the actual probability that you're cancer free if you test negative). As tptacek points out, these metrics don't just depend on the sensitivity and specificity of the test This is a bizarre thing to say in response to... a clear statement of the positive and negative predictive value. PPV is 62% and NPV is "over 99%". Your calculations don't appear to have any connection to your criticism. You're trying to back into sensitivity ("the test missed the majority of people who had cancer") from reported PPV and NPV, while complaining that sensitivity is misleading and honest reporting would have stated the PPV and NPV.
inglor_cz 1y ago
"only 2 metrics that really matter" Nope, there is another important thing that matters: some of the cancers tested are really hard to detect early by other means, and very lethal when discovered late. I would not be surprised if out of the 155 people who got detected early, about 50 lives were saved that would otherwise be lost. That is quite a difference in the real world. Even if the statistics stays the same, the health consequences are very different when you test for something banal vs. for pancreatic cancer.
- holowoodman 1y ago
  Maybe. Let's do a thought experiment. Let's say you do have a positive test for pancreatic cancer. Overall 5 year survival rate 12%, but other than with other cancers, people continue to die after that. Basically, it is almost a death sentence if it is a true positive. Early detection will increase your odds a bit, and prolong your remaining expected lifetime, but even stage 1 pancreatic cancer, only 17% survive to 10 years. Let's say you are one of the 99% of false positives, because everyone gets tested in this hypothetical scenario. Let's say imaging and biopsy looks clean. No symptoms (which you typically don't have until stage 3 with pancreatic cancer, where it is far too late anyways). With the aforementioned odds, what would you do? Panic? Certainly, given that if it is a real positive, you might as well order your headstone. Panic more? Maybe people with those news will change their behaviour and engage in risky activities, get depressed, or attempt suicide (https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2800688 https://jamanetwork.com/journals/jamanetworkopen/fullarticle... ). All of which will kill some of those people. Get surgery to remove your pancreas? Well, just the anesthesia as a 0.1% chance of killing you, the surgery might kill 0.3% in total. No pancreas means you will instantly have diabetes, which cuts your life expectancy by 20 years. Start chemotherapy? Chemo is very dangerous, and there is no chemo mixture known to be effective against pancreatic cancer, usually you just go with the aggressive stuff. It is hard to come by numbers as to how many healthy people a round of chemo would kill, but in cancer patients, it seems that at least 2% and up to a quarter die in the 4 weeks following chemotherapy (https://www.nature.com/articles/s41408-023-00956-x https://www.nature.com/articles/s41408-023-00956-x ). And chemotherapy itself has a risk of causing cancers later on. Start radiation therapy? Well, you don't have a solid tumor to irradiate, so that is not an option anyways. But if done, it would increase your cancer risk as well as damage the irradiated organ (in that case probably your pancreas). So in all, from 100 positive tests you have 99 false positives in this scenario. If just one of those 99 false positives dies of any of the aforementioned causes, the test has already killed more people than the cancer ever would have. Even if no doctor would do surgery, chemotherapy or radiation treatment on those hypothetical false positives, the psychological effects are still there and maybe already too deadly. So it is a very complex calculation to decide whether a test is harmful or good. Especially in extreme types of cancer.