In prior blogs in this series, we reviewed NCI data on changes in U.S. cancer death rate since 1950. There has been very little improvement in the U.S. cancer death rate in more than a half century. In my opinion, death rate is a good measure of progress in the war against cancer because it reflects cumulative changes from cancer causes, cancer preventions, and cancer treatments.
Though the cancer death rate has not decreased much in the past half century, there are many recent claims that cancer survival has greatly improved.
Cancer survival is determined by finding the proportion of people who are alive at various intervals after receiving a diagnosis of cancer. The claims are that today, a person with a diagnosis of cancer would be expected to survive the cancer much longer than someone receiving the same diagnosis at some earlier time.
Here is an excerpt from the MedicineNet site:
http://www.medicinenet.com/script/main/art.asp?articlekey=157
The title of the report is, "Better and Longer Survival for Cancer Patients"
The lead-off text is, "Statistics (released in 1997) show that cancer patients are living longer and even "beating" the disease. Information released at an AMA sponsored conference for science writers, showed that the death rate from the dreaded disease has decreased by 3 percent in the last few years. In the 1940's only one patient in four survived on the average. By the 1960's, that figure was up to one in three, and now has reached 50% survival."
When conducting a clinical trial for a new cancer drug, it is essential to show that the drug offers some kind of improvement over the standard treatment. Determining differences in cancer survival between the standard treatment group and the experimental treatment group is very important. In well-designed trials, these comparisons answer very specific questions about specific subsets of people with specific clinical stages of disease.
It is difficult, or impossible, to generalize much about the survivability of all stages of cancer, in all cancer patients, by looking at survival data in clinical trials.
Here is a partial listing of the biases in survival data:
1. Stage assignment bias (diagnostic or screening tools shifting the proportion of people at different disease stages)
2. Lead-time bias (extending time-after diagnosis without changing date of death)
3. Population bias (exclusion of important subpopulations)
4. Statistical method bias (different methods in different studies of same treatment)
5. Demographic bias (different demographics in the time interval of the study)
6. Measurement bias (inability to accurately measure clinical or biological study parameters)
7. Record bias (medical records can be incomplete flawed, including death status)
8. Re-abstraction bias (gaps in records often require re-abstraction from other sources, and there may be biases in the way that the additional information is collected for certain subpopulations of patients).
9. Comorbidity bias (survial may be determined by processes other than the cancer under study).
10. Overdiagnosis bias (when the patient has a benign condition that is erroneously diagnosed as cancer)
11. Second trial bias (also called patient selection bias. Second trials of the same drug or procedure commonly produce better survival results than the first trial, because the clinicians become more adept at selecting patients who will benefit from the treatment).
12. Money bias (if there's money at stake, even minimal benefit can be spun into major advances; even 4 month extensions in life can be promoted as miracles)
13. Stage treatment bias (finding an improvement that is effective for a small subset of people with a cancer can be falsely interpreted as a measure that enhances survival for everyone).
14. Apples-Oranges bias (sometimes you can't determine improvements in survival because the objectives of different studies may not be comparable)
15. Confounders (sometimes factors may improve health and survivability without being part of a treatment; for example, statins may increase survival by reducing the risk of heart disease in cancer patients, without having any direct effect on the patient's cancer).
For all these reasons, you need to be very careful when discussing survival trends in cancer patients. I'll discuss some of these specific points (about survival studies) in future blogs.
My opinion is that if there were major gains in cancer survival, they would show up in the U.S. cancer death rate. As discussed in prior blogs in this series, there has been only incremental (at best) improvement in the U.S. cancer death rate in the past half-century.
-Copyright (C) 2008 Jules J. Berman
key words: cancer, tumor, tumour, carcinogen, neoplasia, neoplastic development, classification, biomedical informatics, tumor development, precancer, benign tumor, ontology, classification, developmental lineage classification and taxonomy of neoplasms
In June, 2014, my book, entitled Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases was published by Elsevier. The book builds the argument that our best chance of curing the common diseases will come from studying and curing the rare diseases.
I urge you to read more about my book. There's a generous preview of the book at the Google Books site. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.
