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Cancer Survival Statistics: Cohort Definition Using Diagnosis Year

Survival estimates from cancer registry data are usually dated measures of current-year survival, because of the time needed to observe survival and lag between available data and the current year. There are different approaches of grouping survival experience with respect to year of diagnosis and follow-up to obtain more up-to-date estimates of patients recently diagnosed.

Observed Survival by Year of Follow-up and Year of Diagnosis Regional Female Breast Cancer, 1997-2001: Cohort=74.7%, Multiple-year=75.2%, Period=76.6%

Cohort - uses the observed survival for a cohort of patients diagnosed in a single calendar year. Cohort is a well defined measure and is useful in communicating survival trends.

Multiple-year cohort - includes all patients diagnosed in the most recent years spanning the maximum duration to be estimated. Because the multiple-year cohort includes patients diagnosed more recently, it gives a more up-to-date estimate of recent survival.

Period - uses only the most recent interval survival estimate of cases diagnosed in different calendar years (cross-sectional estimate of survival). The estimate of period 5-year survival from cases diagnosed between 1997 and 2001 uses the first year interval survival from patients diagnosed in 2001, the two-year interval survival from patients diagnosed in 2000, and so on. Because period uses only the most recent survival experience, when there is an increasing trend in survival it provides a more up-to-date measure of recent survival (Brenner et al. 2002). The method implemented in SEER*StatExternal Web Site Policy differs slightly from Brenner et al., (See Cronin et al., 2003 (PDF) for more information).

The three SEER*StatExternal Web Site Policy matrices used to obtain the percentages for the Observed Survival by Year of Follow-up and Year of Diagnosis figure shown above are available for download.You must have the SEER*Stat software in order to open these files.

Projection Method - models and extrapolates survival using all available information. Newly diagnosed patients may desire an estimate of their prospects for long term survival. Standard estimates of survival may be outdated since they do not reflect recent advances. 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 in a target year is calculated by multiplying the projected interval survival for that year. Because recent trends in survival are estimated and projected, the projection method may provide more up-to-date estimates of survival for newly diagnosed patients. This type of modeling can be done through the CanSurv software.