CANSURV: A Windows program for population-based cancer survival analysis

Comput Methods Programs Biomed. 2005 Dec;80(3):195-203. doi: 10.1016/j.cmpb.2005.08.002. Epub 2005 Oct 27.


Patient survival is one of the most important measures of cancer patient care (the diagnosis and treatment of cancer). The optimal method for monitoring the progress of patient care across the full spectrum of provider settings is through the population-based study of cancer patient survival, which is only possible using data collected by population-based cancer registries. The probability of cure, "statistical cure", is defined for a cohort of cancer patients as the percent of patients whose annual death rate equals the death rate of general cancer-free population. Mixture cure models have been widely used to model failure time data. The models provide simultaneous estimates of the proportion of the patients cured from cancer and the distribution of the failure times for the uncured patients (latency distribution). CANSURV (CAN-cer SURVival) is a Windows software fitting both the standard survival models and the cure models to population-based cancer survival data. CANSURV can analyze both cause-specific survival data and, especially, relative survival data, which is the standard measure of net survival in population-based cancer studies. It can also fit parametric (cure) survival models to the individual data. The program is available at . The colorectal cancer survival data from the Surveillance, Epidemiology and End Results (SEER) program [Surveillance, Epidemiology and End Results Program, The Portable Survival System/Mainframe Survival System, National Cancer Institute, Bethesda, 1999.] of the National Cancer Institute, NIH is used to demonstrate the use of CANSURV program.

MeSH terms

  • Computer Simulation
  • Data Interpretation, Statistical
  • Humans
  • Models, Biological*
  • Models, Statistical
  • Neoplasms / mortality*
  • Population Dynamics*
  • Proportional Hazards Models*
  • Risk Assessment / methods*
  • Risk Factors
  • Software*
  • Survival Analysis*
  • Survival Rate