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International comparison of population-based cancer survival is a key component of monitoring progress against cancer. Its validity depends to an unknown degree on completeness of ascertainment of deaths in the cancer registries involved which may vary according to legal and administrative circumstances. The aim of this study was to assess the impact of incomplete registration of deaths through various mechanisms on the validity of long-term absolute and relative survival estimates. For that purpose, we simulated underascertainment of deaths through linkage failure of registry data with death certificates with probabilities between 0.1 and 5%, and underascertainment of deaths by unregistered annual emigration with probabilities between 0.05 and 2%, using data from the Finnish Cancer Registry. The expected impact on estimates of 5-, 10- and 15-year absolute and relative survival was assessed. We demonstrate that even modest levels of under-registration of deaths may lead to severe overestimation of long-term survival estimates, ranging from 0 to 31 percent units in the scenarios assessed. In general, relative survival is much more affected than absolute survival, and potential problems are much larger for relative survival estimates in older compared with younger patients. Potential overestimation strongly increases with length of follow-up, and this increase is particularly pronounced for under-registration of deaths because of unrecorded emigration. Every effort should be made in cancer registry based survival analyses to ascertain deaths with close to 100% completeness. When such completeness cannot be achieved, long-term relative survival estimates and their comparison across populations must be interpreted with much caution., (Copyright 2009 UICC.)

Since its introduction in 1996, period analysis has been shown to be useful for deriving more up-to-date cancer survival estimates, and the method is now increasingly used for that purpose in national and international cancer survival studies. However, period analysis, like other commonly employed methods, is just a special case from a broad class of design options in the analysis of cancer survival data. Here, we explore a broader range of design options, including 2 model-based approaches, for deriving up-to-date estimates of 5- and 10-year relative survival for patients diagnosed in the most recent 5-year interval for which data are available. The performance of the various designs is evaluated empirically for 20 common forms of cancer using more than 50-year long time series of data from the Finnish Cancer Registry. Period analysis as well as the 2 model-based approaches, one using a "cohort-type model" and another using a "period-type model", all performed better than traditional cohort or complete analysis. Compared with "standard period analysis", the cohort-type model further increased up-to-dateness of survival estimates, whereas the period-type model increased their precision. While our analysis confirms advantages of period analysis over traditional methods in terms of up-to-dateness of cancer survival data, further improvements are possible by flexible use of model-based approaches.