Up-to-date cancer survival: period analysis and beyond.
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.
Brenner
Hermann
H
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany. h.brenner@dkfz-heidelberg.de
Hakulinen
Timo
T
D015331:Cohort Studies; D005260:Female; D005387:Finland / Q000453:epidemiology; D005833:Genital Neoplasms, Female / Q000453:epidemiology / Q000401:mortality; D005834:Genital Neoplasms, Male / Q000453:epidemiology / Q000401:mortality; D006801:Humans; D008297:Male; D009369:Neoplasms / Q000453:epidemiology* / Q000401:mortality; D012042:Registries; D016019:Survival Analysis; D017741:Survivors; D013997:Time Factors