British Journal of Cancer

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Calculating age-adjusted cancer survival estimates when age-specific data are sparse: an empirical evaluation of various methods

A Gondos, D M Parkin, E Chokunonga and H Brenner

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Table 1. Examples of age adjustment of population-based cancer survival estimates in the recent literature

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Authors (year) Study/registry Cancer sites Age groups (years) Standard population Note
Aareleid et al (1998) EUROCARE-2Testicular cancer15–44, 45–54, 55–64, 65–74, 75+All cases with malignant testicular cancer included in the study, in addition time-series comparison using EUROCARE-1Age adjustment often failed
Sant et al (1998) EUROCARE-2Malignant brain tumours15–44, 45–54, 55–64, 65–99All cases with malignant brain tumours included in the studyAge adjustment often failed
Sant et al (2001) EUROCARE-210+ different sites15–44, 45–54, 55–64, 65–99All cases included in the study, by site 
Capocaccia et al (2003) EUROCARE-310+ different sites15–44, 45–54, 55–64, 65–74, 75+All cases included in the study, by siteMissing age-specific values modelled
Dickman et al (1999) Finland10+ different sites0–14, 15–29, 30–44, 45–59, 60–74, 75+Cancer patient populations of the most recent period in the study (1985–1994) 
Sankaranarayanan et al (1998) IARC: Cancer survival in developing countries10+ different sites0–34, 35–44, 45–54, 55–64, 65–74, 75+World Standard Cancer Patient PopulationsAge groups were collapsed when age-specific survival could not be calculated, additional analyses for truncated age range 0–74 years
Wang et al (2003) SingaporeCervical cancer0–39, 40–49, 50–59, 60–69, 70+World Standard Cancer Patient Populations 
Chokunonga et al (2004) ZNCRCervical Cancer0–34, 35–44, 45–54, 55–64, 65–74, 75+World Standard Cancer Patient Populations 

 IARC=International Agency for Research on Cancer, Lyon, France; ZNCR=Zimbabwe National Cancer Registry, Harare.

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