Methods and Tools: Cancer Survival Statistics

Diagram of Cancer Survival Methods and Software Health Disparities Calculator (HD*Calc)
SEER*Stat Observed Survival CanSurv Survival Models

Cancer survival statistics are typically expressed as the proportion of patients alive at some point subsequent to the diagnosis of their cancer. Several statistical methods and software tools have been developed for the analysis and reporting on cancer survival statistics. Overview of Population-based Cancer Survival Statistics provides detailed information about survival measures, approaches to cohort definition, and available statistics.

SEER*Stat Statistical Methods

There are three measures of cancer survival that can be calculated in SEER*Stat software, including:

  • Observed all cause survival - survival using as end point all causes of death
  • Net cancer-specific survival - cancer survival in the absence of other causes of death (the confounding effects of death from other causes are removed)
  • Crude probability of death - probability of death from cancer and the probability of death from other causes, each estimated in the presence of the other

Read more about applications of these measures on Overview of Population-based Cancer Survival Statistics.

Two of the survival measures, net cancer-specific survival and crude probability of death, each have two methods in which they can be estimated: using cause of death information or expected survival tables. These two approaches to estimation are described in Measures of Cancer Survival: Approaches to Estimation.

Cohort definition using diagnosis year - There are different approaches of grouping survival experience (or patients) with respect to year of diagnosis and follow-up to obtain more up-to-date estimates of patients recently diagnosed with cancer or to obtain survival trends. The current appraoches include cohort, complete analysis, and period.

Cancer Survival Analysis Software (Cansurv)

Cansurv is statistical software to analyze population-based survival data. For grouped survival data, it can fit both standard survival models and mixture cure models and provide graphs and tests for inference and diagnosis. It can also fit parametric (cure) survival models to individually-listed data. Cansurv uses population-based survival data extracted from SEER*Stat survival session. See the Cansurv website for more information.