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 Unfortunately, standard estimates of survival may be outdated, because they do not reflect recent advances.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.
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 Unfortunately, standard estimates of survival may be outdated, because they do not reflect recent advances.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.
Authors: Angela B Mariotto; Anne-Michelle Noone; Nadia Howlader; Hyunsoon Cho; Gretchen E Keel; Jessica Garshell; Steven Woloshin; Lisa M Schwartz Journal: J Natl Cancer Inst Monogr Date: 2014-11
Authors: Jeanne S Mandelblatt; Ling Cai; George Luta; Gretchen Kimmick; Jonathan Clapp; Claudine Isaacs; Brandeyln Pitcher; William Barry; Eric Winer; Stephen Sugarman; Clifford Hudis; Hyman Muss; Harvey J Cohen; Arti Hurria Journal: Breast Cancer Res Treat Date: 2017-03-31 Impact factor: 4.872
Authors: Jonathan D Nilles; Dooyoung Lim; Michael P Boyer; Brittany D Wilson; Rebekah A Betar; Holly A Showalter; Darren Liu; Elitsa A Ananieva Journal: Environ Geochem Health Date: 2022-04-05 Impact factor: 4.609
Authors: Gretchen G Kimmick; Brittny Major; Jonathan Clapp; Jeff Sloan; Brandelyn Pitcher; Karla Ballman; Myra Barginear; Rachel A Freedman; Andrew Artz; Heidi D Klepin; Jacqueline M Lafky; Judith Hopkins; Eric Winer; Clifford Hudis; Hyman Muss; Harvey Cohen; Aminah Jatoi; Arti Hurria; Jeanne Mandelblatt Journal: Breast Cancer Res Treat Date: 2017-03-10 Impact factor: 4.872