In an era of ongoing improvement in cancer patient survival, available long-term survival figures from cancer registries are often outdated and too pessimistic for two reasons: first, delay in availability of cancer registry data, typically in the order of a few years, and, second, application of cohort-based methods of survival analysis, which provide survival estimates for patients diagnosed many years ago. We developed a model-based period analysis approach aimed to overcome both problems. We provide extensive empirical evaluation of our approach by comparing its performance with that of previously available methods for monitoring of 5- and 10-year relative survival, with the use of data from the nationwide Finnish Cancer Registry of 490,279 patients ages >/=15 years and diagnosed with one of 20 common forms of cancer between 1953 and 1997. We show that, in most cases, the model-based approach predicts 5- and 10-year relative survival expectations of newly diagnosed patients quite closely and much better than any of the previously available methods, including standard period analysis. We conclude that the model-based approach may enable deriving up-to-date cancer survival rates even with the common latency in availability of cancer registry data.