Its complicated...

https://sciencebasedmedicine.org/the-early-detection-of-canc...

>Unless one can follow a cohort over time, there is no way of accurately estimating the probability that a subclinically detected abnormality will naturally progress to an adverse outcome. The probability of such an outcome is mathematically constrained, however, by the prevalence of the detected abnormality. The upper limit of this probability can be derived from reasoning that dates to the 17th century, when vital statistics were first collected. If the number of persons dying from a specific disease is fixed, then the probability that a person with the disease will eventually die from it is inversely related to the prevalence of the disease. Therefore, given fixed mortality rates, an increase in the detection of a potentially fatal disease decreases the likelihood that the disease detected in any one person will be fatal..... Lead-time and length biases pertain not only to changes that lower the threshold for detecting disease, but also to new treatments that are applied at the same time. Whether or not new therapy is more effective than old therapy, patients given diagnoses with the use of lower detection thresholds will appear to have better outcomes than their historical controls because of these biases. Consequently, new therapies often appear promising and could even replace older therapies that are more effective or have fewer side effects. Because the decision to treat or to investigate the need for treatment further is increasingly influenced by the results of diagnostic imaging, lead-time and length biases increasingly pervade medical practice.

>There is another complication that these more powerful imaging modalities can lead to that wasn’t discussed in the paper, stage migration. This is a phenomenon that occurs when more sophisticated imaging studies or more aggressive surgery leads to the detection of tumor spread that wouldn’t have been noted in an identical patient using previously used tests. This phenomenon is colloquially known in the cancer biz as the Will Rogers effect. The name is based on Will Rogers’ famous joke: “When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states.” This little joke describes very well what can happen in cancer. What in essence happens is that technology results in a migration of patients from one stage to another that does the same thing for cancer prognosis that Will Rogers’ famous quip did for intelligence. Consider this example. Patients who would formerly have been classified as, for example, stage II cancer (any cancer), thanks to better imaging or more aggressive surgery, have additional disease or metastases detected that wouldn’t have been detected in the past. They are now, under the new conditions and using the new test, classified as stage III, even though in the past they would have been classified as stage II. This leads to the paradoxical statistical effect of making the survival of both groups (stage II and III) appear better, without any actual change in the overall survival of the group as a whole. This paradox comes about because the patients who “migrate” to stage III tend to have a lower volume of disease or less aggressive disease compared to the average stage III patient and thus a better prognosis. Adding them to the stage III patients from before thus improves the apparent survival of stage III patients as a group. The converse is that patients with more disease that was previously undetected, tended to be the stage II patients who would have recurred and done more poorly compared to the average patient with stage II disease; i.e., the worst prognosis stage II patients. But now, they have “migrated” to stage III, leaving behind stage II patients who truly do not have as advanced disease and thus in general have a better prognosis. Thus, the prognosis of the stage II group also ends up appearing to be better with no real change in the overall survival from this cancer.