Although age is recognized as the strongest predictor of mortality in chronic disease epidemiology, a calendar-based approach is often employed when evaluating time-related variables. An age-based analysis file, created by determining the value of each time-dependent variable for each age that a cohort member is followed, provides a clear definition of age at exposure and allows development of diverse analytic models. To demonstrate methods, the relationship between cancer mortality and external radiation was analyzed with Poisson regression for 14,095 Oak Ridge National Laboratory workers. Based on previous analysis of this cohort, a model with ten-year lagged cumulative radiation doses partitioned by receipt before (dose-young) or after (dose-old) age 45 was examined. Dose-response estimates were similar to calendar-year-based results with elevated risk for dose-old, but not when film badge readings were weekly before 1957. Complementary results showed increasing risk with older hire ages and earlier birth cohorts, since workers hired after age 45 were born before 1915, and dose-young and dose-old were distributed differently by birth cohorts. Risks were generally higher for smokingrelated than non-smoking-related cancers. It was difficult to single out specific variables associated with elevated cancer mortality because of: (1) birth cohort differences in hire age and mortality experience completeness, and (2) time-period differences in working conditions, dose potential, and exposure assessment. This research demonstrated the utility and versatility of the age-based approach
