Estimates of long-term survival for newly diagnosed cancer patients: a projection approach.
BACKGROUND
Patients with newly diagnosed cancer may request an estimate of their prospects for long-term survival. Unfortunately, standard estimates of survival may be outdated, because they do not reflect recent advances. The authors present a projection method that incorporates trends in survival and provides more up-to-date estimates of long-term survival for newly diagnosed patients.


METHODS
The projection method fits a regression model to interval relative survival and includes a parameter associated with a trend on diagnosis year. The cumulative relative survival rate (CRS) in a target year is calculated by multiplying the projected interval survival rates for that year. To investigate the predictive ability of the projection approach and to develop model-selection rules, data from the Surveillance, Epidemiology, and End Results Program and the Connecticut tumor registry were used to recreate data that were available at a particular time in the past, and those data were used to project survival for specified target years.


RESULTS
The projection method was better at predicting the survival of recently diagnosed patients than current methods, especially long-term survival for patients who had disease sites with an increasing and stable trend in survival. The authors predicted that the 15-year CRS for patients who were diagnosed in 2003 will be 61% for all cancer sites combined, 57% for colorectal cancer, 82% for female breast cancer, 53% for ovarian cancer, and 97% for prostate cancer.


CONCLUSIONS
Although the projection method was more speculative than other methods that are aligned more closely with current observed data, it offered the possibility of providing improved estimates of long-term survival for recently diagnosed patients. Caution should be used when applying these methods for cancer sites where there has been a dramatic uptake of screening, e.g., prostate cancer, for which the projected results may be overly optimistic.
Mariotto
Angela B
AB
Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. mariotta@mail.nih.gov
Wesley
Margaret N
MN
Cronin
Kathleen A
KA
Johnson
Karen A
KA
Feuer
Eric J
EJ
D005260:Female; D006801:Humans; D008297:Male; D009369:Neoplasms / Q000401:mortality*; D018426:SEER Program; D015996:Survival Rate